{ "componentChunkName": "component---src-templates-keyword-page-js", "path": "/services/team-augmentation/", "result": {"data":{"posts":{"edges":[{"node":{"excerpt":"","fields":{"slug":"/blog/claude-code-copilot-or-cursor-how-to-choose-ai-tooling-for-your-team/"},"frontmatter":{"title":"Claude Code, Copilot, or Cursor? How to choose AI tooling for your team","order":null,"content":[{"body":"## Claude Code vs Copilot vs Cursor: what each tool is actually built for\n\nUnderstanding the design intent behind each tool matters more than comparing their feature lists.\n\n**GitHub Copilot** is an AI assistant that lives inside the IDE and suggests code as you type. It is fast, familiar, and governed – which is exactly what makes it attractive at enterprise scale. It fits naturally into existing GitHub workflows, gives security and legal teams the audit trails and access controls they need, and lets developers move faster through well-understood work without changing their habits. Its context is local by design: it sees the file, the function, the diff. That is a reasonable trade-off for the kind of work it is built for.\n\n**Cursor** is a fork of VS Code with AI built directly into the editing experience. It offers multi-line autocomplete, inline chat, and agent modes, all within an interface most developers already know. Its strength is ergonomics – it reduces the friction of day-to-day coding by keeping suggestions close to where the work happens. It supports multiple models including Claude Sonnet and Gemini, which gives teams some flexibility in how they manage costs and preferences. Where it starts to show limits is in depth of reasoning and in automated, terminal-driven workflows. It is built for interactive editing, not for wiring into CI/CD pipelines.\n\n**Claude** **Code** is designed differently from both. It is terminal-first and agentic – it does not just suggest, it plans, edits across multiple files, runs commands, and integrates with GitHub, CI pipelines, and external tooling. Its context window is reliably large, which matters when the system you are reasoning about spans dozens of services and years of commits. The most important distinction for engineering leaders is this: Claude Code is less about making individual developers type faster and more about giving teams the ability to understand and safely change complex systems. That is a different category of value.\n\n## Codebase complexity, governance, and workflow: the three variables that drive the decision\n\nFeature comparisons are less useful than asking three questions about your specific situation:\n\n### How complex is your codebase? \n\nCopilot and Cursor both handle local, well-scoped tasks efficiently. They start to struggle when a change touches many services, when the codebase carries significant historical debt, or when understanding the system matters more than producing output quickly. If your engineers regularly need to trace business logic across services or reason about the risk surface of a refactor, you need a tool with deeper context. Claude Code is built for that level of complexity.\n\n### What do your governance requirements look like? \n\nFor teams in regulated industries – financial services, healthcare, government-adjacent software – the security review is often the gate that determines whether a tool gets deployed at scale. Copilot has the clearest governance story: role-based access, identity provider integration, content and IP policies, and audit logs built into GitHub's existing infrastructure. Claude Code's enterprise tier includes a Compliance API, an Analytics API, SCIM, SSO, RBAC, and audit logs – which creates the oversight trail that regulated environments typically require. Cursor's enterprise controls are competent for an IDE-centric tool but reflect different assumptions about who controls what.\n\n### What kind of workflow does your team actually run? \n\nIf most of your AI use happens during interactive editing – writing new features, generating tests, making incremental changes to clean code – then Copilot or Cursor will cover most of the need with less setup. If you are running automated analysis in CI pipelines, managing code review at scale, or asking the AI to reason about architectural decisions rather than just produce output, Claude Code's terminal-first, agentic design fits the workflow better.\n\n## Decision matrix: Claude Code, Cursor or Copilot\n\n
GoalToolWhy
Write code faster day-to-dayCopilot or CursorSuggestion-driven, low friction, familiar IDE environment
Understand a complex or legacy systemClaude CodeDeep context, git history, architecture-level reasoning
Onboard junior developersCopilotStandard patterns, safe defaults, governed environment
Support senior engineers on hard problemsClaude CodeReasoning partner for design decisions and refactoring risk
Compliance and regulated environmentsCopilot or Claude CodeCopilot for policy-first control; Claude Code for deep audit capability
Large undocumented codebasesClaude CodeRepo-wide analysis, pattern extraction, commit history context
Daily interactive editingCursorIDE ergonomics, multi-model flexibility, plugin ecosystem
CI/CD and automated workflowsClaude CodeTerminal-first, GitHub Actions integration, agentic execution
\n\n## Why enterprise engineering teams use all three tools together\n\nIn practice, the most effective enterprise setups do not standardize on one tool. They assign tools to layers of the development process, and the separation is intentional.\n\n**Copilot** or **Cursor** handles the high-frequency, lower-risk work: writing new features in well-understood areas, generating tests, making incremental improvements to clean code. These tools stay in the editor, close to the developer, keeping feedback loops short. The choice between them often comes down to governance requirements and team preference – Copilot for organizations that need policy-first controls, Cursor for teams that want more flexibility in their editing experience.\n\n**Claude Code** operates at a different level. It is the tool you reach for when you need to understand something before changing it – when a refactor spans multiple services, when someone asks where a business rule is actually enforced, or when a schema migration needs to be validated against a system no one has fully mapped. It also belongs in CI pipelines and secured terminals for automated analysis and code review, away from the day-to-day editing flow.\n\nHigh-frequency work benefits from low friction. High-stakes work benefits from deeper reasoning. Conflating the two (expecting one tool to do both well) usually means getting a mediocre version of each.\n\n## What this looks like in practice\n\nConsider a backend team maintaining a large Java system built over several years. The codebase has custom abstractions, event-driven flows, and domain logic that is only partially documented. Delivery pressure is constant.\n\nIn this environment, Copilot or Cursor handles the daily work: writing controllers and repositories, generating test scaffolding, moving quickly through pull requests. They are fast and familiar and they do not require the team to change how they work.\n\nClaude Code steps in for the harder problems. When a senior engineer needs to understand how a pricing rule propagates across services before refactoring it, Claude Code can trace it. When the team is planning a schema migration and needs to map what will break, Claude Code can reason about it with the full context of the repository and its history. When the organization needs those activities logged and auditable, the Compliance API covers that requirement.\n\nThe tools are not competing for the same moment in the workflow. They are solving different problems, and recognizing that is what makes the stack work.\n\n## The tooling decision is the easy part\n\nMost engineering leaders we talk to can see the productivity case for AI tooling. The harder question is how to deploy it without accumulating invisible risk – where does the AI's output get reviewed, who owns the decision when the tool suggests something that technically works but architecturally does not fit, and how do you preserve system knowledge when the tool is doing more of the synthesis.\n\nPicking the right tool for the right layer of the process is the starting point. Designing the process around it is the work that actually matters.\n\nIf that is where your team is, the 30-minute conversation is the right starting point.\n\nBook your strategy session [here](https://www.boldare.com/contact/)"}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1776327733/Frame_2087325381_swjj10.png","lead":"If you manage an engineering team, you are probably being asked to have an opinion on AI coding tools more often than you would like. The conversation has shifted from \"should we use AI?\" to \"which one, and for what?\" – and the answer is no longer obvious now that three genuinely capable tools are competing for the same budget line.\n\n[Claude Code](https://claude.com/product/claude-code), [GitHub Copilot](https://github.com/features/copilot), and [Cursor](https://cursor.com) are frequently compared as if they were interchangeable. They are not. They were built on different assumptions about where AI belongs in the development process, and deploying them without understanding that distinction is how teams end up with tools that technically work but do not actually help.\n\nThis article is the ultimate guide for making that decision without getting lost in feature checklists.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-04-16T08:01:35.023Z","slug":"claude-code-copilot-cursor-how-to-choose-ai-coding-tool-for-enterprise","type":"blog","slugType":null,"category":null,"additionalCategories":["GenAI","How to"],"url":null},"author":"Aleksander Dąbrowski","authorAdditional":"","box":{"content":{"title":"Claude Code, Copilot, or Cursor? How to choose AI tooling for your team","tileDescription":"Compare Claude Code, Copilot, and Cursor to find the right AI coding tool for your enterprise engineering team.","coverImage":""},"coverImage":null}},"id":"eac30e5c-b259-5052-ad38-acaf15fe594a"}},{"node":{"excerpt":"","fields":{"slug":"/blog/how-vibe-coding-can-break-your-product-without-a-design-system-and-how-to-prevent-it/"},"frontmatter":{"title":"How vibe coding can break your product without a design system (and how to prevent it)","order":null,"content":[{"body":"## What vibe coding can do to an interface\n\n\n\nVibe coding – building with AI as the primary driver – has a fundamental characteristic: AI has no memory of context between sessions and no knowledge of your broader product unless you explicitly provide it. Every prompt starts from zero. Every generated component is locally coherent and globally disconnected.\n\nTeams that adopted vibe coding without design infrastructure in place tend to describe the same pattern. The first two weeks feel spectacular – the speed is unlike anything they've worked with before. Then sprint three or four arrives, and someone has to walk through the entire product and manually reconcile what AI generated without coordination.\n\nTime saved on generation comes back as visual debt that needs to be paid down.\n\nAccording to 2026 data, 67% of design teams use generative AI tools in their daily workflow. Most of them don't have a design system ready for this scenario. In practice, that means most AI-assisted products are quietly accumulating debt today that will become visible in three months.\n\n\n\n## Why a design system is the answer\n\n\n\nA design system isn't a component library. It's the source of truth for how a product looks and behaves across every possible context. Design tokens – colors, typography, spacing, border-radius, shadows – define the visual language of a product in a form that can be handed to a developer or passed to a prompt.\n\nThat's the shift in perspective worth holding onto: a design system has stopped being a tool for designers. It's become infrastructure for anyone generating UI – human or model.\n\nWhen AI generates a component inside a product with a working design system, it doesn't invent colors from scratch. It uses `--color-action-primary`. It doesn't choose typography by feel. It applies `text-heading-2`. The result is consistent not because AI becomes smarter – but because it has boundaries it can't cross.\n\nIn the vibe coding era, a design system works the way a quality management system works in manufacturing. It doesn't slow the process down. It eliminates a category of errors that would otherwise be inevitable.\n\n\n\n## Where the problems tend to appear\n\n\n\nVisual debt from vibe coding accumulates in three places.\n\nThe first is UI states nobody designed. AI generates the default state well – it's the most represented in prompts and training examples. Error states, loading states, empty states, edge-case data states – these arrive in the product as improvised variants, each handled differently. Without a design system, there's no pattern for AI to reference.\n\nThe second is components generated independently by different team members. Two primary buttons, three input variants, four different toast implementations – each locally correct, none fitting the rest. In single-team products this is a manageable problem. In products with three squads it becomes the norm, unless there's a shared system.\n\nThe third – and hardest to catch – is token drift. AI regularly \"optimizes\" numerical values: spacing that should be 16px arrives as 15px or 17px because the model judged it to look better. Over a month, the product starts to resemble a bad photocopy – each iteration slightly worse than the last.\n\n\n\n## How to prevent it before it starts\n\n\n\nThe best time to introduce a design system is before the first prompt that generates UI. The second best time is now.\n\nMinimum requirements for a design system to function as an effective guardrail for AI:\n\n**Tokens as a contract.** Every visual value needs a name and needs to be available in a format that can be passed into a prompt or context. Instead of `#2D6BE4` – `color.action.primary`. Instead of `16px` – `spacing.md`. Tokens become the vocabulary AI uses instead of its own.\n\n**Component documentation as context.** Every component in the design system should have a plain-text description of its purpose, variants, and usage context. That documentation goes into the prompt as system context – not a Figma screenshot.\n\n**Figma–code parity.** A design system that lives only in Figma won't prevent code drift. Tokens need to be implemented in code – as CSS custom properties, design tokens in JSON, or variables in the component system. AI generating code should draw from the same values a designer uses in Figma.\n\n**Validation rules.** A linter or review gate that flags components inconsistent with the system. It doesn't need to be complex – the question \"is this in the design system?\" simply needs to be part of code review before a PR reaches main.\n\n\n\n## When the debt is already there\n\n\n\nIf a product has three months of vibe coding behind it and no design system, adding tokens to Figma won't be enough on its own. Visual debt already exists and needs to be inventoried before it can be fixed.\n\nA design system audit in this context looks different from a standard one. The first step is a map of inconsistencies – a product review that identifies variants which should be the same component but aren't. The second step is a deliberate decision: which variants to preserve as intentional, which to unify. Only then is a system built – not from scratch, but as a structured version of what already exists.\n\nThere's a counterintuitive advantage here: products built with vibe coding often have more modular component structures than traditionally built ones. AI naturally generates things as isolated modules. That's a reasonable foundation for a design system – it needs organizing, not rewriting.\n\n\n\n## What this means for teams building with AI\n\n\n\nVibe coding doesn't reduce the time spent on a product – it shifts where that time goes. Without a design system, the speed of generation translates into visual debt and consistency regressions. With one, the same speed produces a product that scales without visual chaos.\n\nThis isn't an argument against vibe coding. It's an argument for treating a design system the same way teams treat CI/CD, monitoring, or test coverage – not as a design project that will happen \"eventually,\" but as a precondition for AI-assisted development that creates value rather than debt.\n\nTeams that make this shift gain something specific: AI that generates consistently because it has constraints – rather than AI that generates freely because no constraints were defined."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/q_auto/f_auto/v1776246381/Vibe_coding_is_fast._Without_a_design_system_it_s_also_chaos._vsgadw.png","lead":"Vibe coding has changed what's possible for product teams. What used to take a week of development now takes hours – AI generates components, layouts, entire interface sections. A prototype before lunch, an iteration before end of day.\n\n**The problem tends to surface when those components meet a live product.**\n\nA button in one place has border-radius 4px, in another – 8px. The action color in navigation is `#2D6BE4`, on the landing page – `#2B6CE3`. **Heading typography varies between sections because each prompt generated styles from scratch**. None of these issues is visible in isolation. Together, they produce a product that looks like it was assembled by four different companies.\n\n**This isn't a problem with AI. It's a problem that emerges when AI operates without a design system to constrain it.**","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-04-15T09:00:39.266Z","slug":"vibe-coding-design-system","type":"blog","slugType":null,"category":null,"additionalCategories":["GenAI"],"url":null},"author":"Magdalena Chmiel","authorAdditional":"","box":{"content":{"title":"Design system for vibe coding teams — how to prevent visual debt","tileDescription":"Vibe coding can break product consistency without a design system in place. Learn how AI-generated UI accumulates visual debt — and what design infrastructure prevents it.","coverImage":""},"coverImage":null}},"id":"b95a9c2c-3368-549f-bdbb-a49d5d049012"}},{"node":{"excerpt":"","fields":{"slug":"/blog/figma-to-code-how-to-keep-design-and-development-in-sync-at-scale/"},"frontmatter":{"title":"Figma to code: how to keep design and development in sync at scale","order":null,"content":[{"body":"## Why design and code drift apart – the four failure points\n\n\n\nMost teams experience design-to-code drift as a single frustrating problem. In practice, it's four separate failure modes that compound each other.\n\n\n\n### **Token drift**\n\n\n\nColours, spacing, and typography values are hardcoded in CSS rather than referenced from a shared variable system. When a designer updates a token in their design tool, nothing in the codebase changes automatically. The update has to be manually propagated – and it rarely is, consistently.\n\n\n\n### **Component naming divergence**\n\n\n\nA designer names a component \"Card/Featured\" in the design file. A developer builds `FeaturedCard.tsx` in the codebase. These are the same component, but without an explicit mapping between them, neither side knows that. Every component change requires a human to translate between two naming systems.\n\n\n\n### **State gaps**\n\n\n\nA designer specifies the default and hover states of a component. The developer implements those two states and ships. Three months later, a disabled state is designed and added to the design file. It never makes it into the codebase because there's no process that flags the gap.\n\n\n\n### **Governance failure**\n\n\n\nNobody is explicitly responsible for keeping both environments in sync simultaneously. Designers update the design tool. Developers update the codebase. The two environments drift in parallel, and nobody notices until a design review reveals that production and design have been telling different stories for weeks.\n\n> **The principle:** Design-to-code sync isn't a handoff problem. It's a maintenance problem. Handoff is a moment. Sync is a practice.\n\n\n\n## The token layer – why it's the foundation of everything else\n\n\n\nDesign tokens are the only layer of a design system that can be technically synchronised automatically. Every other layer – components, documentation, governance – requires human judgment at some point. Tokens, once structured correctly, can flow from design tool to codebase without manual intervention.\n\nThe mechanism is straightforward: design tokens are defined as named variables in your design tool, exported as a JSON file, transformed by a tool like [Style Dictionary](https://amzn.github.io/style-dictionary/) into CSS custom properties, Sass variables, or platform-specific formats, and then referenced in component code. When a token changes in the design tool, the change propagates through the pipeline automatically – every component that references the token updates without anyone touching component code.\n\nWhat happens without this pipeline is instructive. Figma's own design systems team described how, after migrating their colour system from a spreadsheet to Figma Variables, they found over 280 differences – designers had been working on stale information. [Figma](https://www.figma.com/best-practices/how-figma-uses-dev-mode/) The spreadsheet had been the source of truth in name only. The actual source of truth was fragmented across wherever the last manual update had landed.\n\nThe token pipeline solves this by making your design tool variables the genuine source of truth – not aspirationally, but technically. When the pipeline is working, a designer who updates a colour token triggers a process that ends with a pull request in the codebase. Developers review and merge. The change is traceable, reversible, and doesn't require anyone to remember to do it.\n\n\n\n> **Pro tip:** The W3C Design Tokens specification reached version 1.0 in October 2025, establishing the first official standardised format for design tokens. [Figma](https://www.figma.com/), [Sketch](https://www.sketch.com/), and [Penpot](https://penpot.app/) are all implementing the same spec, which means token files can now move between tools without custom transformation scripts.\n\n\n\n## The component layer – naming conventions that both sides can use\n\n\n\nThe token layer handles values. The component layer handles structure – and structure is where most design-to-code sync breaks down in practice, because it requires agreement between two disciplines that use different languages.\n\nA designer thinks in visual hierarchies and states. A developer thinks in component APIs and props. Without an explicit convention that maps between the two, every component handoff is a translation exercise – and translations introduce errors.\n\nThree principles make component naming work across both environments:\n\n\n\n### **Use a consistent hierarchy**\n\n\n\nA naming structure like `Category/Component/Variant` in your design tool maps cleanly to a file structure like `components/category/Component.tsx` in code. When the hierarchy matches, designers and developers can find the same component without asking each other. [GitHub's Primer design system](https://primer.style/guides/figma/) separates libraries by function – Primer Primitives for colour, type, and spacing; Primer Web for UI components – with a clear architectural principle: the source of truth is the code, documented on Primer.style. The design tool is secondary to the code, not the other way around.\n\n\n\n### **Make states explicit, not implicit**\n\n\n\nEvery component state that exists in the design tool – default, hover, focus, disabled, loading, error – should have a corresponding variant that is named identically to the prop or class that controls it in code. A state that exists in one environment but not the other is a gap waiting to cause a bug.\n\n\n\n### **Treat naming changes as breaking changes**\n\n\n\nWhen a designer renames a component in the design tool, or a developer renames a component in code, the mapping between the two environments breaks silently. Both sides need to treat renaming as a cross-environment operation that requires coordination – the same way a database schema change requires a migration.\n\n\n\n## The tooling layer – what actually keeps design and code connected\n\n\n\nNo tool solves the sync problem on its own. But the right combination of tools reduces the manual work to the point where governance becomes feasible. Here is what the current tooling landscape looks like and what each tool actually does.\n\n**Token management in your design tool.** [Token Studio](https://tokens.studio/) manages design tokens inside [Figma](https://www.figma.com/). [Sketch](https://www.sketch.com/) handles tokens through its native variables system. [Penpot](https://penpot.app/) supports design tokens natively as part of its open-source infrastructure. All three can export tokens as JSON for use in a token pipeline. The goal is the same regardless of tool: a structured, exportable definition of every visual decision in your product.\n\n**[Style Dictionary](https://amzn.github.io/style-dictionary/)** transforms JSON token files into whatever format the codebase uses – CSS custom properties, Sass variables, JavaScript constants, or platform-specific formats for iOS and Android. It sits in the middle of the token pipeline, between the design tool and the component code.\n\n**Dev inspection tooling.** [Figma's Dev Mode](https://www.figma.com/dev-mode/) is a developer-focused interface for inspecting designs. It surfaces component properties, token values, spacing measurements, and links to external resources like Storybook and GitHub. It also shows which frames are marked as ready for development. Teams using Sketch or Penpot have equivalent inspection workflows through their own developer handoff features.\n\n**[Figma Code Connect](https://help.figma.com/hc/en-us/articles/23920389749655-Code-Connect)** maps Figma components to actual code components in a repository. When implemented, developers inspecting a component in Dev Mode see real code snippets from the production codebase – not auto-generated CSS. Figma described the problem Code Connect was built to solve directly: \"We built a design system, but it's not being used to its full potential\" – developers use parts of the design system but often misuse components in ways that don't align with the system's intended guidelines. [Figma](https://www.figma.com/blog/introducing-code-connect/)\n\n**[Storybook](https://storybook.js.org/)** documents and previews coded components in isolation. It's the code-side equivalent of a design tool component library – a place where developers can see every component, every variant, and every state without running the full application. Linking design tool components to their Storybook stories closes the loop between design and code documentation.\n\n> **Watch out:** These tools handle the mechanics of synchronisation. None of them handle the question of who is responsible for keeping both environments updated when something changes. That's governance – and it's the layer most teams skip.\n\n\n\n## The governance layer – the one most teams skip\n\n\n\nGovernance is the hardest layer to build because there's no tool that implements it for you. It's a set of decisions about ownership, process, and accountability – and the absence of those decisions is what causes the other three layers to drift even when the tooling is in place.\n\nThree governance decisions determine whether design-to-code sync holds at scale:\n\n**Who owns the token pipeline.** When a designer updates a colour in the design tool, who is responsible for ensuring that change reaches the codebase? In most teams, the answer is \"whoever notices\" – which means it happens inconsistently. A working governance model assigns explicit ownership: either a designated design systems engineer runs the sync on a defined cadence, or the process is automated to the point where human intervention is only needed at the review stage.\n\nFigma's own design systems team built a GitHub Action that automatically syncs Figma Variables to CSS tokens. Designers update variables in Figma, the Action creates a pull request, and developers review and merge. [Figma](https://www.figma.com/best-practices/how-figma-uses-dev-mode/) This model keeps both sides in their native environment – designers work in the design tool, developers work in GitHub – while eliminating the manual step that most sync processes break down at.\n\n**Who approves new components.** Every team eventually faces the question of whether a new pattern should be added to the design system or handled as a one-off. Without a defined process for making that decision, engineers build one-off components that never make it into the system, and designers add components to the design tool that never get coded. The governance model needs to answer: who reviews new component requests, what criteria determine whether something belongs in the system, and how quickly can that decision be made.\n\n**How changes are versioned.** Design systems change. Components get deprecated. Token values get updated. Without versioning, teams can't communicate change to the people who depend on the system, can't roll back when a change breaks something, and can't audit where a visual decision came from. Versioning design system changes – treating them like software releases, with changelogs and semantic versioning – is what separates a maintained system from one that drifts.\n\n> **The principle:** Governance isn't a process for large teams. It's the answer to one question: who is the last person who knows what's in both environments simultaneously? If the answer is \"nobody\", the system will drift.\n\n\n\n## How to audit your current sync – a practical checklist\n\n\n\nBefore investing in new tooling or process, it's worth understanding where your current sync is actually breaking. These five questions surface the most common failure points.\n\n**1. Can a developer find the CSS variable for any colour in your product in under a minute?** If the answer is no – because colours are hardcoded, because the variable names don't match between design tool and code, or because nobody has documented the mapping – the token layer is broken.\n\n**2. Do component names in the design tool match component names in the codebase?** Pick five components at random and compare what they're called in the design file versus in the repository. If the names diverge, there's no reliable mapping between design and code – and every handoff requires translation.\n\n**3. Are all component states in the design tool implemented in code?** For each component, check whether every variant and state has a corresponding implementation. Unimplemented states are gaps in the sync that will surface as bugs when those states are needed.\n\n**4. When was the last time design tokens were updated in both the design tool and the codebase on the same day?** If the answer is \"never\" or \"I don't know\", the token pipeline doesn't exist or isn't being used. The two environments are drifting independently.\n\n**5. Who would you ask if you needed to know whether the design tool and production are currently in sync?** If there's no clear answer – or if the answer is \"I'd have to check both\" – governance is absent. No single person or process owns the sync.\n\n\n\n## Sync is a practice, not a project\n\n\n\nTeams that solve design-to-code sync don't do it in a single sprint. They build a practice – a set of habits, tools, and responsibilities that make staying in sync the path of least resistance rather than an extra effort on top of regular delivery.\n\nThe token layer makes automatic sync possible. The component layer makes the system navigable across disciplines. The tooling layer removes the manual steps that break down under deadline pressure. The governance layer keeps it all honest when no one is looking.\n\nNone of these layers is optional. A token pipeline without governance drifts. A component library without naming conventions creates translation overhead. Tooling without tokens to sync has nothing to connect. The four layers work together – or they don't work at all."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/q_auto/f_auto/v1776246377/Design_ships_perfect._Code_ships_close_enough._lxsuzs.png","lead":"The design is done. The file is handed off. The developer opens it, inspects the components, and starts building. Two weeks later, the product looks close to the design – but not quite. The spacing is slightly off. The button colour doesn't match. A component state that was designed never made it into production. Nobody made a deliberate decision to diverge. The gap just appeared, the way it always does: gradually, then everywhere.\n\n**This is the design-to-code problem. And it's not a tooling problem, even though most teams try to solve it with tools.** It's a synchronisation problem – across four distinct layers that can each drift independently: tokens, components, tooling, and governance. **Fix one without the others and the gap reopens within a sprint.**\n\n**This article breaks down each layer, what breaks when it's missing, and what teams who've solved it actually do.**","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-04-14T10:14:50.396Z","slug":"figma-to-code-design-development-sync","type":"blog","slugType":null,"category":null,"additionalCategories":["GenAI"],"url":null},"author":"Magdalena Chmiel","authorAdditional":"","box":{"content":{"title":"Figma to code: how to keep design and development in sync at scale","tileDescription":"Most design systems look perfect in the design tool and fall apart in code. This guide covers the four layers of design-to-code sync – tokens, components, tooling, and governance – and how teams like GitHub and Figma itself keep them aligned.","coverImage":""},"coverImage":null}},"id":"9f52976a-2500-5137-b3e5-ae2e3714caa7"}},{"node":{"excerpt":"","fields":{"slug":"/blog/10-signs-your-product-needs-a-design-system-and-what-its-costing-you-now/"},"frontmatter":{"title":"10 signs your product needs a design system (and what it's costing you now)","order":null,"content":[{"body":"## 10 signs your product needs a design system\n\n\n\n### 1. Your team uses AI tools, but every output looks slightly different\n\n\n\n[Cursor](https://www.cursor.com/), [Claude Code](https://www.anthropic.com/claude-code), and [Figma Make](https://www.figma.com/make/) generate UI from whatever visual logic is available in the codebase. Without a shared token architecture, each tool infers spacing, colour, and typography independently – from whatever it finds in the code. The inferences are close, but not identical, and they compound across every session and every team member using them. The result isn't obviously broken. It's subtly inconsistent – and subtle inconsistency is harder to fix than obvious breakage, because it never creates a single moment of urgency.\n\nA design system gives AI tools a shared reference. Instead of inferring, they read from your token architecture. The output stays within the visual boundaries your product was built on.\n\n\n\n### 2. You have more than one version of the same component in production\n\n\n\nThis is the most diagnostic sign of all. Three button variants, two modal styles, four card layouts – none of them wrong enough to block a release, all of them different enough to signal that decisions are being made locally rather than systemically.\n\n[Airbnb's engineers described exactly this pattern in their 2016 codebase](https://www.infoq.com/news/2020/02/airbnb-design-system-react-conf/): when pressed with incoming deadlines, engineers would write their own components rather than use the design system's components, defeating the point of having a system in the first place. The component count grows faster than the product does, and the only way to stop it is to make the shared component the path of least resistance.\n\n\n\n### 3. A new designer or developer spends their first two weeks asking which component to use\n\n\n\nOnboarding time is a direct measure of documentation quality. If the answer to \"which component do I use here\" exists only in the memory of the person who built the system, every new hire starts from zero. The knowledge doesn't compound – it resets.\n\n[Tuomas Artman, co-founder and CTO of Linear](https://linear.app/now/quality-wednesdays), describes the inverse of this problem: **engineers who have trained their eye for quality through consistent practice start noticing problems while building new features, not just when reviewing existing ones**. That kind of compounding attention only develops when there's a shared standard to measure against.\n\n\n\n### 4. Changing a colour or spacing value requires touching dozens of files\n\n\n\nThis is the absence of design tokens made visible. One decision – update the primary button colour – becomes a multi-sprint engineering task instead of a single token change that propagates automatically across your design tool and your codebase.\n\n[Shopify's Polaris design system](https://polaris.shopify.com/), launched in 2017 to solve fragmented user experiences across Shopify's growing app marketplace, was built specifically around this problem: before Polaris, developers building apps for Shopify struggled to match Shopify's interface standards, resulting in jarring transitions between native features and third-party apps. The system made consistency the path of least resistance.\n\n\n\n## 5. Design and production look different, and nobody is sure why\n\n\n\nYour design tool says one thing. The codebase does another. The gap widens with every sprint because there's no defined process for keeping them in sync – and no single source of truth either side is working from.\n\nDesign-to-code parity isn't a tooling problem. It's a governance problem. The tools exist to keep design and code aligned – [Token Studio](https://tokens.studio/) in Figma, [Style Dictionary](https://amzn.github.io/style-dictionary/) in the codebase – but without a defined process for who updates what and when, the gap reopens faster than it gets closed.\n\n\n\n### 6. Your team is preparing for AI-assisted development but has no token architecture\n\n\n\nIf design tokens aren't named, documented, and shared before your team starts using AI tools at scale, those tools will infer their own visual logic from the existing codebase. That logic will be inconsistent, because the codebase is inconsistent. The design system needs to exist before the AI tools are deployed at scale – not after, when the fragmentation has already accumulated.\n\n\n\n### 7. Three or more teams are building the same product simultaneously\n\n\n\nParallel development without shared infrastructure is the fastest path to visual fragmentation. Each team makes locally reasonable decisions – a slightly different card component, a slightly different spacing scale – that are globally incompatible. The product begins to feel assembled rather than designed.\n\n[Google's Material Design](https://design.google/library/material-design-eras), announced in 2014, was built in part to solve this problem at a scale most companies will never face: unifying Gmail, Chrome, Android, and every other Google product so they felt like they existed in the same family. The internal description at Google was that they needed to \"clean out the cruft – the unnecessary bubbles and bumps that had been part of Google's UI story for a long time.\"\n\n### 8. A full redesign feels inevitable, but you can't justify the cost\n\n\n\nDesign debt compounds silently. The moment a team starts discussing whether it's easier to rebuild than to fix, the absence of a design system has already become the most expensive line item in the product budget – it just hasn't been named yet.\n\n[Airbnb's own design system had to be rebuilt in 2018](https://www.infoq.com/news/2020/02/airbnb-design-system-react-conf/) – not because the original was badly built, but because the product had expanded so significantly that the system couldn't accommodate the new scope without a structural rethink. Building the system earlier doesn't prevent evolution. It makes evolution cheaper.\n\n\n\n### 9. Your product feels like it was built by several different companies\n\n\n\nUsers rarely articulate inconsistency directly. They don't say \"your spacing scale is off\" or \"this component doesn't match the one two screens back.\" They say the product feels off, or they stop trusting it, or they churn. The feeling precedes the diagnosis.\n\n[Tuomas Artman described this dynamic at Linear](https://linear.app/now/quality-wednesdays): **quality doesn't affect revenue, until it does.** And by then it's a slow drain, not a sudden drop – hard to attribute, hard to reverse. The 150-millisecond fade-out on a hover state that Linear spent time getting right isn't a detail for its own sake. It's the accumulated effect of hundreds of such decisions on how the product feels.\n\n\n\n### 10. You're scaling the team but not the system\n\n\n\nHiring more designers and developers into a product without shared infrastructure doesn't accelerate delivery – it accelerates divergence. Every new team member is another vector for visual inconsistency without a system to constrain it.\n\n[Shopify's Polaris](https://polaris.shopify.com/) was built not just for Shopify's internal teams, but for the thousands of third-party developers building apps in the Shopify ecosystem. **The design system became the mechanism through which Shopify could maintain consistent quality across a product surface no single team could directly control.** Scale made the system necessary. The system made scale possible.\n\n\n\n## What it's actually costing you\n\n\n\nThe cost of not having a design system rarely appears as a single line item. It accumulates across three categories:\n\n**Time.** Rebuilding components that already exist. Debugging visual inconsistencies introduced by parallel teams. Onboarding new hires into a system that lives in people's heads rather than documentation. These hours are real, but they're distributed across sprints and teams in ways that make them invisible in any single planning cycle.\n\n**Speed.** The gap between a feature request and a shipped feature widens as the component inventory grows without governance. Engineers spend time deciding which component to use, creating new ones when they can't find the right one, and resolving conflicts when two teams built the same thing differently.\n\n**Quality.** The product gradually stops feeling like a single thing. Users don't need to be able to name design inconsistency to feel it. The feeling compounds into trust – or its absence.\n\n## The threshold question\n\n\n\nEvery team that has built a design system describes the same pattern: they waited longer than they should have, the cost of starting was lower than they feared, and the compounding returns came faster than they expected.\n\n**The ten signs above are not a checklist to complete before you act. They're a map of where you already are.** If three or more of them describe your product today, the threshold has been crossed."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/q_auto/f_auto/v1776246381/Your_product_is_drifting._Here_are_10_signs_you_ve_already_crossed_the_line._yjuuor.png","lead":"In 2016, [Airbnb](https://www.airbnb.com/) engineers described their codebase as suffering from three compounding problems: **fragmentation, complexity, and performance issues**. The fragmentation alone – engineers using different frameworks, CSS hardcoded and overridden across multiple files – had grown to the point where the team printed out their screens and laid them side by side on a board just to see where the experience was breaking. [That exercise became the starting point for Airbnb's Design Language System](https://medium.com/airbnb-design/building-a-visual-language-behind-the-scenes-of-our-airbnb-design-system-224748775e4e).\n\nThe decision to build a design system is rarely proactive. It usually happens when the cost of not having one becomes impossible to ignore. **The question isn't whether your product needs one. It's whether you've already crossed the threshold where the absence of a system is slowing you down more than building it would.**\n\nThese are the ten signs that threshold has been crossed.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-04-14T09:33:47.108Z","slug":"signs-your-product-needs-a-design-system","type":"blog","slugType":null,"category":null,"additionalCategories":["GenAI"],"url":null},"author":"Magdalena Chmiel","authorAdditional":"","box":{"content":{"title":"10 signs your product needs a design system (and what it's costing you now","tileDescription":"Inconsistent UI, slow onboarding, AI tools generating mismatched components – these are the signs a design system isn't a nice-to-have anymore. Here's how to recognise them and what they're actually costing your team.","coverImage":""},"coverImage":null}},"id":"ef508f7a-d05f-592d-85a2-f9ee2ccfbdb3"}},{"node":{"excerpt":"","fields":{"slug":"/blog/tytul-design-tokens-explained-a-practical-guide-for-product-teams/"},"frontmatter":{"title":"Design tokens explained: a practical guide for product teams","order":null,"content":[{"body":"## What are design tokens\n\n\n\nA design token is not a colour, a font size, or a spacing value. A design token is a named variable that stores a visual decision. The difference matters: `#1A73E8` is a value. `color-button-primary` is a decision. One describes what something looks like. The other describes what something means.\n\nThis distinction is the entire point of design tokens. When you store visual decisions as named variables rather than hardcoded values, two things become possible that weren't before. First, a single change to a token propagates automatically to every component that references it – in your design tool and in your codebase simultaneously. Second, every tool working with your product – including AI tools like [Cursor](https://www.cursor.com/), [Claude Code](https://www.anthropic.com/claude-code), and [Figma Make](https://www.figma.com/make/) – has a shared reference for what your visual decisions actually are. Without a token architecture, those tools infer from whatever they can find in the codebase – and infer inconsistently, because the codebase is inconsistent.\n\n> **Pro tip:** A quick way to check whether your product uses design tokens or hardcoded values: ask a developer how long it would take to change the primary button colour across the entire product. If the answer is anything other than \"a few minutes\", you have hardcoded values – not tokens.\n\n\n\n## Why design tokens matter in 2026 – the AI angle\n\n\n\nAI tools generate UI faster than any team has been able to before. [Cursor](https://www.cursor.com/) builds a new settings screen, [Claude Code](https://www.anthropic.com/claude-code) ships an onboarding flow, [Figma Make](https://www.figma.com/make/) prototypes a new feature in an afternoon. The productivity gains are real, and using AI in product development is one of the smartest decisions an engineering team can make right now.\n\nBut every AI tool works from available context. When it generates a button, it infers the colour, spacing, and typography from whatever it can find in the codebase or the prompt. If your visual decisions are hardcoded and scattered, the tool infers inconsistently – because the codebase is inconsistent. Each session produces output that is close to your visual language, but not quite the same.\n\nDesign tokens change this. A well-structured token architecture gives AI tools a single, unambiguous reference for every visual decision in your product. Instead of inferring, the tool reads. Instead of approximating, it applies. The output is consistent not because the AI got lucky, but because the infrastructure made inconsistency impossible.\n\nThis is why design tokens are the most critical component of an AI-ready design system – not components, not documentation, but tokens, because they're the layer AI tools actually work from.\n\n## The three types of design tokens you need to know\n\n\n\nMost teams that struggle with token implementation are working with a flat structure – one layer of tokens that tries to do everything at once. The result is a system that's hard to maintain, hard to extend, and impossible for AI tools to use reliably. A well-structured token architecture has three layers.\n\n\n\n### Global tokens\n\n\n\nGlobal tokens are raw values – they define every possible value in your visual palette, every colour, every spacing step, every type size, without any reference to how those values are used. Example: `color-blue-500: #1A73E8`. They are the foundation of the system, never referenced directly in components, existing solely as a source of truth for alias tokens to draw from.\n\n\n\n### Alias tokens\n\n\n\nAlias tokens are semantic – they describe the purpose of a visual decision rather than its value. An alias token doesn't store a colour; it references a global token and gives that colour a meaning. Example: `color-button-primary: {color-blue-500}`. When you change `color-button-primary` from `{color-blue-500}` to `{color-green-500}`, every component that references it updates automatically – no search and replace, no missed instances.\n\n\n\n### Component tokens\n\n\n\nComponent tokens are the most specific layer, mapping alias tokens to individual components and their states. Example: `button-background-default: {color-button-primary}`. They exist because different components may use the same alias token differently, giving you precise control at the component level without breaking the chain of reference that makes the whole system work.\n\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
LayerExamplePurpose
Globalcolor-blue-500: #1A73E8Stores raw values. Never used directly in components.
Aliascolor-button-primary: {color-blue-500}Gives values meaning. The layer that makes changes propagate.
Componentbutton-background-default: {color-button-primary}Maps alias tokens to specific components and states.
\n\n\n\n**Watch out:** Most token system failures happen when teams skip the alias layer and map global tokens directly to components. It works until you need to change something – then you discover that changing one global token breaks ten things you didn't intend to touch.\n\n\n\n## How to name design tokens – the decision that determines everything\n\n\n\nToken naming is the most consequential decision in a token system. A token named after its value – `color-blue` – is accurate today and misleading the moment your primary button colour changes to green. A token named after its purpose – `color-button-primary` – remains accurate regardless of what value it holds.\n\nThree principles for naming design tokens:\n\n**Name for purpose, not appearance.** `color-text-error` tells you what the token does. `color-red` tells you what it looks like. When your brand updates its error colour from red to orange, `color-text-error` stays true – while `color-red`becomes a lie that propagates through every component that references it.\n\n**Use a consistent naming structure.** A reliable pattern: `category-property-variant-state`. Applied: `color-button-primary-hover`. Every token follows the same logic, which makes the system navigable by people who didn't build it – and by AI tools that need to understand it programmatically.\n\n**Separate what something is from what it does.** Global tokens describe what something is (`color-blue-500`). Alias tokens describe what it does (`color-button-primary`). Keeping these layers distinct is what makes the system maintainable. Blending them – naming a token `color-primary-blue` – collapses that distinction and makes every future change more fragile than it needs to be.\n\n> **Pro tip:** Token naming directly affects how well [Cursor](https://www.cursor.com/) and [Claude Code](https://www.anthropic.com/claude-code) can use your design system. Semantically named tokens give AI tools the context they need to apply the right token in the right place. Tokens named after values require the AI to guess intent – and guesses compound into inconsistency.\n\n\n\n## Design tokens in practice – from design tool to code\n\n\n\nA token system only works when it exists in both your design tool and your codebase, and when both environments stay in sync. Here is how that process works in practice.\n\n**Step 1: Define tokens in your design tool.** Most design tools support token management either natively or through plugins. In [Figma](https://www.figma.com/), [Token Studio](https://tokens.studio/) is the most widely used plugin for defining and managing all three token layers. [Sketch](https://www.sketch.com/)handles tokens through its native variables system or third-party plugins. [Penpot](https://penpot.app/) supports design tokens as part of its open-source infrastructure. Regardless of the tool, the goal is the same: a structured, exportable definition of every visual decision in your product.\n\n**Step 2: Export to JSON.** Most token management tools export your token architecture as a JSON file – a format both design and engineering environments can read, and that version control can track alongside your codebase.\n\n**Step 3: Transform tokens for the codebase.** Tools like [Style Dictionary](https://amzn.github.io/style-dictionary/) take the JSON token file and transform it into whatever format your codebase uses – CSS variables, Sass variables, JavaScript constants, or platform-specific formats for iOS and Android.\n\n**Step 4: Reference tokens in components.** Developers reference tokens in component code rather than hardcoded values. A button's background isn't `#1A73E8` – it's `var(--color-button-primary)`. When the token changes, the component updates without touching the component code.\n\n**Step 5: Maintain sync.** This is where most token implementations break down. Design and code start aligned and drift apart as sprints ship without updating both sides. A governance process – who is responsible for keeping tokens in sync, how updates are reviewed and merged – is what prevents drift from becoming the default state.\n\n> **Watch out:** Tools handle the mechanics of token sync, but governance handles the discipline. A team with the best token tooling and no governance will have a beautifully structured token file that slowly diverges from production. A team with governance and no tooling will have manually maintained variables that are always slightly out of date. You need both.\n\n\n\n## The most common design token mistakes\n\n\n\n**Naming tokens after values, not purpose.** This is the most frequent mistake and the most expensive to fix once a codebase grows. Renaming `color-blue` to something semantic after it's been referenced across hundreds of components requires touching every one of those references – semantic naming built in from the start costs nothing extra, while fixing the absence of it later costs weeks.\n\n**Skipping the alias layer.** Teams often map global tokens directly to components to save time during setup, which works until the first time any value needs to change. Without the alias layer, a single change to a global token can break components in ways that weren't intended, because the semantic layer that would have scoped the change doesn't exist. The alias layer isn't a refinement – it's the mechanism the whole system depends on.\n\n**Tokens in your design tool only, not in code.** A token system that lives only in a design tool is a design artefact, not a design system. Design and production diverge from the first sprint, and the token system creates a false sense of consistency that makes the divergence harder to notice until it becomes too expensive to ignore.\n\n**No governance for token changes.** Without a defined process for requesting new tokens, reviewing changes, and deprecating old ones, individual teams add tokens ad-hoc to solve immediate problems. The system accumulates values with no shared logic, and within months it resembles a token library more than a token architecture – a growing collection of decisions that no longer add up to a coherent system.\n\n## When to build design tokens – and when to start with an audit\n\n\n\nIf you're building a product from scratch, start with token architecture before you build a single component. Tokens are the foundation everything else depends on – components, documentation, and AI-readiness all follow from a well-structured token system, and retrofitting tokens into a codebase that was built without them is significantly harder than building them in from the start.\n\nIf you have an existing product, the question is different. Before building tokens, you need to understand what you're working with: how many hardcoded values exist in the codebase, where your design tool and production diverge, and which parts of the system are consistent enough to tokenise versus which need to be rebuilt first. \n\nA design system audit answers these questions in one week – it maps every visual decision currently in your product, identifies where hardcoded values are costing your team time, and gives you a prioritised plan for building a token architecture on top of what already works."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/q_auto/f_auto/v1776246377/One_color_change._Seven_places_to_fix_it_manually._There_s_a_better_way._vjrl6p.png","lead":"Your team decides to update the primary button colour. A designer changes it in Figma. A developer updates it in the stylesheet. Then someone finds three more places where it was hardcoded. Then two more. **A week later, the button is still three different shades of blue depending on which screen you're on.**\n\nThis is not a design problem. **It's an infrastructure problem.** And it has a name: the absence of design tokens.\n\nDesign tokens are not a designer's tool or a developer's concern – they're the shared language that keeps your product visually coherent across every team, every tool, and every AI-generated screen. **Without them, every visual decision your product has ever made exists somewhere – in a design file, in a stylesheet, in someone's memory – but nowhere all at once.** With them, a single change propagates everywhere, automatically.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-04-14T08:44:32.281Z","slug":"/design-tokens-explained/","type":"blog","slugType":null,"category":null,"additionalCategories":["GenAI"],"url":null},"author":"Magdalena Chmiel","authorAdditional":"","box":{"content":{"title":"Design tokens explained: a practical guide for product teamsDesign tokens explained: a practical guide for product teamsDesign tokens explained: a practical guide for product teamsDesign tokens explained: a practical guide for product teams","tileDescription":"Design tokens are named variables that replace hardcoded values across your design tool and codebase. This guide explains what they are, how to structure them, and why they're the foundation of every AI-ready design system","coverImage":""},"coverImage":null}},"id":"2b72e2ea-7f48-5159-b2c8-48d114adc9e7"}},{"node":{"excerpt":"","fields":{"slug":"/blog/design-system-for-ai-assisted-development-why-fast-teams-ship-consistent-products/"},"frontmatter":{"title":"Design system for AI-assisted development: why fast teams ship consistent products","order":null,"content":[{"body":"## What is a design system – and what it's not\n\nA design system is not a design tool file. It's not a component library. It's not a style guide.\n\nA design system is four things working together:\n\n**1. Design tokens** – the single source of truth for every visual decision in your product. Colour, typography, spacing, elevation, border radius. Defined once, referenced everywhere. When a token changes, every component that uses it updates automatically – in your design tool and in production.\n\n**2. Component library** – a production-ready set of UI components built in your design tool of choice – Figma, [Sketch](https://www.sketch.com/), [Penpot](https://penpot.app/), or others. Buttons, forms, modals, navigation, typography, built once and reused across every team and every sprint. Not a starting point that every designer rebuilds from scratch each sprint.\n\n**3. Coded components** – the same components built in code, maintaining design-to-code parity. Developers implement components, not interpretations. What exists in your design tool matches what ships in production.\n\n**4. Documentation and governance** – usage guidelines, naming conventions, extension rules, and a defined process for requesting new components. Design tokens without governance drift over time, and coded components without documentation become tribal knowledge that leaves with the person who built them. The system only holds when all four parts are maintained together.\n\n> **Pro tip:** When evaluating whether you have a design system or just a design file, ask one question: **if a new designer joins your team tomorrow, can they find, understand, and use every UI component without asking anyone?** If the answer is no, you have a starting point – not a system.\n\n## Why 2026 is different – the AI-assisted development problem\n\nFor the past decade, design system adoption was driven by scale. Once you had three or more product teams building in parallel, visual inconsistency became expensive enough to justify the investment. That calculus has changed.\n\nAI tools have introduced a new variable: **speed without memory.** **Cursor doesn't know what your buttons looked like last sprint. Claude Code doesn't know your spacing scale. Figma Make doesn't know which modal variant your team standardised on six months ago.** Each tool generates UI based on what it can infer from the prompt and the immediate context – not from the accumulated decisions your product was built on.\n\nThis is not a criticism of AI tools – **using AI in product development is one of the smartest decisions an engineering team can make in 2026.** Boldare builds this way, and so do the teams we work with. The productivity gains are real, the speed is real, and the quality ceiling is higher than it's ever been.\n\nBut AI tools amplify whatever foundation they're working from. A strong foundation – clear tokens, consistent components, documented patterns – gets amplified into faster, more consistent output. Without that foundation, the same speed produces fragmentation instead.\n\n### Three scenarios that play out without a design system:\n\n**Scenario 1: The token drift problem.** Your team uses Cursor to generate a new settings screen. Cursor infers spacing and colour from the surrounding code – but that code was written across eighteen months by four different developers. The new screen is technically correct and visually close. Close enough that it passes review. Six months later, you have a product where every screen is \"almost\" consistent, and fixing it requires touching everything at once.\n\n**Scenario 2: The component multiplication problem.** Claude Code builds a new onboarding flow. It creates a card component – slightly different from the card component your design team built, because there's no single source of truth either side is working from. Now you have two card components in production. Then three. Then a redesign becomes unavoidable not because the product grew, but because the components did.\n\n**Scenario 3: The handoff collapse problem.** Figma Make ships a flow directly to production. It looks right. But the spacing tokens it used don't match the coded components your developers maintain. Design and code are now out of sync – and every subsequent AI-generated screen widens that gap.\n\nNone of these scenarios are caused by AI – they're caused by the absence of the infrastructure AI needs to work consistently.\n\n> **The principle:** AI tools don't create design debt. They accelerate whatever system – or absence of system – already exists.\n\n## 10 signs your product needs a design system\n\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
#SignWhat it means
1Your team uses AI tools, but every output looks slightly differentCursor, Claude Code, and Figma Make are generating UI across your product – and each session produces components that are close to your visual language, but not quite the same. The inconsistency is subtle enough to pass review and expensive enough to fix at scale.
2You have more than one version of the same component in productionThree button variants. Two modal styles. Four card layouts. None of them wrong enough to flag, all of them different enough to erode the sense that your product was built by one team with a shared vision.
3A new designer or developer spends their first two weeks asking which component to useOnboarding time is a direct measure of documentation quality. If institutional knowledge lives in people rather than in a system, every new hire resets the clock.
4Changing a colour or spacing value requires touching dozens of filesThis is the absence of design tokens made visible. One decision – update the primary button colour – becomes a multi-day engineering task instead of a single token change that propagates everywhere automatically.
5Design and production look different, and nobody is sure whyYour design tool says one thing. The codebase does another. The gap widens with every sprint because there's no process for keeping them in sync – and no single source of truth either side is working from.
6Your team is preparing for AI-assisted development but hasn't defined token architectureIf design tokens aren't named, documented, and shared before your team starts using Cursor or Claude Code at scale, the AI tools will infer their own visual logic from the existing codebase. That logic will be inconsistent, because the codebase is inconsistent. The design system needs to come first.
7Three or more teams are building on the same product simultaneouslyParallel development without shared infrastructure is the fastest path to visual fragmentation. Each team makes locally reasonable decisions that are globally incompatible.
8A full redesign feels inevitable, but you can't justify the costDesign debt compounds silently. The moment a team starts discussing whether it's easier to rebuild than to fix, the absence of a design system has already become the most expensive line item in the product budget – it just hasn't been named yet.
9Your product feels like it was built by several different companiesUsers rarely articulate this. They don't say \"your spacing is inconsistent\" or \"this component doesn't match the one two screens back.\" They say the product feels off, or they stop trusting it, or they churn. Inconsistency is invisible until it isn't.
10You're scaling the team but not the systemHiring more designers and developers into a product without shared infrastructure doesn't accelerate delivery – it accelerates divergence. Every new team member is another vector for visual inconsistency without a system to constrain it.
\n\n\n\n\n\n## What a design system actually consists of – a practical breakdown\n\nUnderstanding the four components of a design system matters because most failures are partial. Teams build the component library but skip governance. They define tokens but don't achieve design-to-code parity. They deliver components but produce no documentation. The result looks like a design system and functions like a starting point that every team gradually diverges from.\n\nHere's what each component is, what it does, and why it can't be skipped.\n\n**Design tokens**\n\nDesign tokens are the single source of truth for every visual decision in your product – colour, typography, spacing, elevation, border radius. They're named variables that replace hardcoded values across both your design tool and your codebase. When a token changes, every component that references it updates automatically, in both environments simultaneously.\n\nFor AI-assisted development, tokens are the most critical component. They're the only mechanism through which Cursor, Claude Code, or Figma Make can generate UI that stays within your visual language rather than inventing its own. A tool with access to a well-structured token architecture generates consistent output. A tool working from a codebase with hardcoded values generates its best approximation – which is close, but not the same, and different every time.\n\n> **Pro tip:** Token naming matters as much as token values. Tokens named `color-primary-500` describe what something is. Tokens named `color-button-default` describe what something does. Semantic naming is what makes tokens usable by AI tools and human developers alike.\n\n**Component library**\n\nA production-ready component library – built in Figma, Sketch, Penpot, or your team's design tool of choice – is the design half of your system. Buttons, forms, modals, navigation, typography, built once and reused across every team and every sprint. The key word is production-ready: components structured with variants, states, and auto-layout so they behave predictably when used, not just when they're being built.\n\nThe component library eliminates the most common source of design waste: rebuilding what already exists. When components are shared, decisions made once stay made.\n\n**Coded components and design-to-code parity**\n\nCoded components are the engineering half – the same components built in code, maintained in parallel with the design library. Design-to-code parity means component names, token references, and states match between design and production. Developers implement components, not interpretations of components.\n\nThis is where most design systems fail in practice. The design library exists and the codebase exists, but they diverge over time because there's no defined process for keeping them in sync. Every sprint that ships without updating both sides widens the gap. A governance model prevents this.\n\n**Documentation and governance**\n\nDocumentation answers the question every new team member asks: which component do I use, and when? Usage guidelines, variant explanations, extension rules – written for designers and developers who didn't build the system and shouldn't need to ask the person who did.\n\nGovernance answers the harder question: what happens when the system needs to change? Who approves new components? How are deprecations handled? How does the system version as the product grows? Without a defined process, design systems accumulate ad-hoc additions until they're no longer a shared standard – they're a starting point everyone diverges from at their own pace.\n\nA design system without governance has a half-life. With it, the system scales as the product scales.\n\n## Vibe coding without a design system: what happens after three months\n\nVibe coding – building with AI as the primary driver, using tools like Claude Code, Cursor, and Figma Make to go from idea to working interface in hours rather than days – is one of the most productive shifts in product development in years. Teams that have adopted it are shipping prototypes faster, validating ideas earlier, and compressing the distance between concept and working software in ways that weren't practical eighteen months ago.\n\nThe design system is what determines whether that speed produces a coherent product or a fragmented one.\n\nConsider two teams, both using the same AI tools at the same pace.\n\n**The first team has a design system in place** – defined tokens, a shared component library, design-to-code parity, documented governance. When a developer uses Cursor to build a new screen, the tool works within the token architecture. The components it generates reference the same spacing scale, the same colour variables, the same typography decisions as every other screen in the product. The output is fast and consistent. A designer reviews it, makes minor adjustments, and it ships.\n\n**The second team moves at the same speed without that foundation.** Cursor generates a screen that looks right – close enough to pass a quick review. But the spacing is slightly off from the rest of the product. The button variant it used is similar to, but not the same as, the standard button. The card component it created is the third card variant now in the codebase. Three months later, the product works but feels assembled rather than designed, and the team is facing a consistency problem that requires dedicated time to resolve.\n\nThe difference between these two teams isn't the AI tools they use. It's the infrastructure those tools are working from.\n\n> **The principle:** Vibe coding amplifies your foundation. A design system turns AI speed into consistent output. Without one, the same speed produces technical and visual debt at the same rate it produces features\n\n## How to get started\n\nYou don't need a fully resourced design system project to make progress. Most teams can take the first meaningful step in a week.\n\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
StepActionWhat to do
1Audit what you haveMap component variants, identify where your design tool diverges from production, check whether spacing and colour values are hardcoded or tokenised.
2Define your token architecture firstStart with tokens before components – colour, typography, spacing, elevation – named for what they do, not what they are. This is the layer AI tools will work from.
3Establish design-to-code parity on your most-used componentsStart with the five to ten components used on every screen – buttons, inputs, cards, navigation, typography – and build them to parity. The system builds from there.
4Write governance before you write documentationDecide who approves new components and how deprecated variants are handled. A one-page governance document written before launch prevents six months of ad-hoc additions.
5Test AI-readiness before you shipUse Cursor or Claude Code to generate a new screen using only the system's tokens and components. If the output is consistent with the rest of the product, the system is working.
\n\n\n\n## What to look for in a design system development agency\n\nWhen a team decides to bring in external expertise for design system development, the evaluation criteria matter as much as the shortlist. These are the four things that determine whether an engagement produces a system your team can own – or one they'll depend on someone else to maintain.\n\n**They build design and code in parallel, not in sequence.** The most common design system failure mode is a component library that gets handed off to engineering six weeks later. By that point, implementation decisions have already been made, and the gap between design and code is built in from day one. The right agency runs design and engineering in the same team, building design components and coded components simultaneously so parity is the starting condition, not the goal.\n\n**They define governance before they define components.** A component library without a governance model is a time-limited asset. Within months, teams add components ad-hoc, tokens drift, documentation falls behind. An agency that builds governance into the engagement – who approves changes, how the system versions, how new components get added – delivers a system with a longer useful life.\n\n**They build for AI-assisted development by default.** In 2026, a design system that isn't structured for AI tools is already behind. Token naming conventions, component documentation format, and design-to-code parity all affect how well Cursor, Claude Code, and Figma Make can work within the system. This shouldn't be an add-on – it should be the baseline assumption.\n\n**They build for independence, not dependency.** The measure of a successful design system engagement is whether your team can maintain and extend the system without the agency that built it. Documentation written for the people who will use the system – not the people who built it – and a governance model your team can actually run are the two indicators that independence was the goal from the start.\n\n## Ready to see what's inconsistent in your product?\n\nA design system audit takes one week. [We'll map every component currently in your product, identify where design and code diverge, assess AI-readiness, and give you a prioritised plan for what to fix and in what order.](https://www.boldare.com/contact/)"}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/q_auto/f_auto/v1776153340/Marylin_design_system_fhjf0m.png","lead":"Most teams treat a design system as a design team project. A Figma library, a component inventory — something designers build and developers eventually adopt. That framing made sense when design and engineering worked in separate lanes.\n\n**It no longer applies.**\n\nIn 2026, AI tools – [Cursor](https://www.cursor.com/), [Claude Code](https://www.anthropic.com/claude-code), [Figma Make](https://www.figma.com/make/) – are active participants in your product development process. They generate components, build screens, and ship flows. They work fast – and they work from whatever visual logic is available to them. **Without a shared design system, that logic is different for every tool, every session, and every team member using them.**\n\nA design system is the layer that makes AI-assisted development coherent. Without it, speed becomes fragmentation – every AI output pulling the product in a slightly different direction. **With a shared system, that stops.**\n\nThe teams getting the most out of AI tools didn't slow down to build a design system. They sped up because of it.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-04-13T12:05:32.591Z","slug":"/design-system-ai-assisted-development","type":"blog","slugType":null,"category":null,"additionalCategories":["GenAI"],"url":null},"author":"Magdalena Chmiel","authorAdditional":"","box":{"content":{"title":"Design system for AI-assisted development: why fast teams ship consistent products","tileDescription":"AI tools generate UI faster than ever. Without a design system, every output pulls your product in a different direction. Here's why a design system is now engineering infrastructure – and how to build one that makes Cursor, Claude Code, and Figma Make work consistently across every team.","coverImage":""},"coverImage":null}},"id":"ef19db7c-d1ef-56a0-93c4-4dd8ca23d926"}},{"node":{"excerpt":"","fields":{"slug":"/blog/guide-how-to-reduce-your-llm-api-costs-by-60-without-losing-quality/"},"frontmatter":{"title":"Guide: How to reduce your LLM API costs by 60% without losing quality","order":null,"content":[{"body":"## You can't optimize what you can't see\n\nMost teams see one global billing number. They don't know cost per feature, per request type, or per model call. Without that, optimization is just guesswork.\n\nAdd per-request telemetry: log model name, token counts, latency, and estimated cost for every call – tagged by feature or workflow. Tools like [Helicone](https://www.helicone.ai/) and [LangSmith](https://www.smith.langchain.com/) make this straightforward to layer in as middleware. Set cost alerts per feature, not just at the account level. Track long-context requests separately as they carry hidden per-token premiums that distort aggregate numbers.\n\nInstrumentation is the precondition for everything else.\n\n## Why bills spiral\n\nOnce you have visibility, the causes are usually obvious:\n\n* **Context window inflation** – sending full conversation history every turn, compounding token counts as sessions grow\n* **No caching** – identical or semantically similar requests hitting the model fresh every time\n* **Model over-provisioning** – using a frontier model across the board when most requests don't need it\n* **Batch-eligible workloads running in real-time** – document processing, bulk classification, and similar jobs priced at synchronous rates\n\n## Five things worth changing\n\n### 1. Prompt compression & context pruning\n\nReplace full conversation history with rolling summaries. Audit system prompts for accumulated redundancy – many production prompts are 3–4× longer than they need to be. For RAG-heavy workloads, LLMLingua and LongLLMLingua are purpose-built for reducing long-context cost while preserving task performance.\n\n> **Estimated saving:** 20–35%\n>\n> **Guardrail**: regression test on a representative input sample before shipping.\n\n### 2. Model routing (budget-aware tiering)\n\nBuild a lightweight policy layer that classifies requests by complexity and routes them to the appropriate model. Simple tasks like reformatting, extraction, classification, don't need a frontier model. In most production systems, a large share of traffic turns out to be simple once you actually look. A/B test routed vs. unrouted traffic before full rollout.\n\n> **Estimated saving**: 25–40% \n\n### 3. Caching\n\nProvider-native caching (OpenAI cached input pricing, Anthropic prompt caching) gives material discounts on repeated prompt prefixes with no application-side infrastructure. **Check your provider's current docs** – **this may be the lowest-effort saving available to you.**\n\nSemantic caching goes further: cache by intent similarity, not just exact match. Tools like GPTCache or [Redis](https://redis.io/) with embedding-based similarity search make this implementable. Best for support bots, internal knowledge assistants, FAQ-style workflows. Track hit rate – if it stays below 20%, the workload may not be a fit.\n\nApplication-level memoization – exact-match caching for deterministic inputs. Simple to implement, limited scope.\n\n> **Estimated saving:** 15–30% (semantic); higher for provider-native on prompt-heavy workloads.\n\n### 4. Output length and structured generation\n\nUse `max_tokens` as a forcing function and structured outputs (JSON mode, schema-constrained generation) wherever downstream systems consume the response programmatically. Structured responses are shorter by nature, more reliable, and eliminate fragile output parsing. Add explicit prompt instructions for concise responses.\n\n### 5. Async batching\n\nBoth OpenAI and Anthropic offer batch endpoints at materially lower prices than synchronous calls. The trade-off is latency. This lever only applies to non-interactive workloads – document processing, overnight analysis, bulk classification. Not a candidate for real-time, user-facing features.\n\n> **Estimated saving:** up to 50% for eligible workloads.\n\n![Infographic titled “LLM API Cost Optimization” showing five strategies to reduce large language model API costs: prompt compression, model routing, caching with three types, output length control, and async batching, with estimated savings ranging from situational to up to 50 percent.](https://res.cloudinary.com/de4rvmslk/image/upload/v1775818810/Infographic_sbbnqo.png \"LLM API Cost Optimization Infographic: 5 Ways to Reduce Large Language Model Costs\")\n\n## The 60% reduction formula\n\n
LeverImpact
Prompt compression−25% tokens per request
Semantic caching−20% total requests
Model routing−30% cost on routed segment
Async batching−50% cost on batch segment
\n\n> **Overall:** 55–65% reduction, depending on workload mix. Teams with high async volume or repetitive-intent products see the higher end. Real-time-only systems with diverse requests see less.\n\n## Where to start\n\nInstrument first. Then identify your highest-spend workloads and model the impact of each lever against real traffic. Provider-native caching and prompt compression have the lowest implementation cost **–** start there. Model routing and semantic caching take more engineering but move the needle more.\n\nIf your team doesn't have bandwidth for an LLM cost audit **–** mapping spend to features, setting up routing logic, implementing caching, and building quality guardrails **–** that's exactly the work we do at Boldare.\n\n[Talk to us about your LLM architecture](https://www.boldare.com/contact/)"}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1775819366/reeducing_llm_costs_cijksx.png","lead":"Your team ships an LLM-powered feature. A month later, the API invoice is three times the forecast. The instinct is to switch to a cheaper model – and that's usually the wrong first move.\n\nCutting costs by 60% is realistic, but it comes from stacking five optimization levers in the right order, not from a single trick. And most importantly –  it starts with measurement, not code changes.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-04-10T11:06:27.121Z","slug":"how-to-reduce-llm-api-costs","type":"blog","slugType":null,"category":null,"additionalCategories":["How to","Strategy"],"url":null},"author":"Aleksander Dąbrowski","authorAdditional":"","box":{"content":{"title":"How to reduce your LLM API costs by 60% without losing quality","tileDescription":"Cut LLM API costs with model routing, prompt caching, semantic caching, and batching. Practical 2026 strategies with quality guardrails.","coverImage":""},"coverImage":null}},"id":"7f1296b8-61cc-5887-82c2-5b30390bb1c5"}},{"node":{"excerpt":"","fields":{"slug":"/blog/case-study-how-we-extracted-structured-data-from-arabic-english-pdfs-with-claude-vision/"},"frontmatter":{"title":"Case Study: How we extracted structured data from Arabic-English PDFs with Claude Vision","order":null,"content":[{"body":"## The Challenge\n\nOur client receives multiple Purchase Orders every month from vendors across the Gulf region. Each document presents a unique challenge: bilingual content (Arabic and English), complex tables with roles and rates, and critical dates scattered across pages. Manual data entry took approximately 15 minutes per document and produced around 5% error rate in amounts and expiration dates – mistakes that proved costly to fix downstream.\n\n## Our Approach\n\nWe built an end-to-end intelligent document processing pipeline that transforms unstructured PDFs into validated, queryable data:\n\n**Google Drive** → **Claude Sonnet 4 Vision API** → **Databricks Unity Catalog** → **Streamlit Review App**\n\nThe flow works as follows: PDFs land in a monitored Google Drive folder. Claude Vision processes each document, extracting structured JSON with roles, rates, dates, and line items. Data flows into Databricks using a medallion architecture (Bronze for raw extractions, Silver for validated records). A Streamlit app hosted on Databricks Apps gives finance teams a side-by-side view of the original PDF and extracted data for final approval.\n\n![Workflow diagram of automated PO processing system using Claude Vision API for PDF data extraction, integrating Google Drive, Databricks Unity Catalog, and Streamlit app for structured data validation.](https://res.cloudinary.com/de4rvmslk/image/upload/v1776246017/PO_SCANNER_ukblvd.png \"PO Data Extraction Pipeline with Claude Vision API\")\n\n## Why Claude Vision\n\nWe evaluated several document AI solutions before settling on Claude Sonnet 4. Four capabilities made the difference:\n\n1. **Native PDF processing**. No need to convert pages to images first. Claude handles the PDF directly, preserving layout context that image-based approaches often lose.\n2. **Structured output**. We define a JSON schema upfront. Claude returns data in exactly that format, eliminating post-processing gymnastics.\n3. **Multilingual understanding**. Arabic and English coexist in these documents – sometimes in the same table cell. Claude handles both without separate OCR passes or language detection logic.\n4. **Table comprehension.** Purchase Orders live and die by their line-item tables. Claude accurately extracts rows with roles, quantities, unit rates, and totals even when formatting varies between vendors.\n\n## Results\n\n
MetricBeforeAfter
PO processing time~15 min2–3 min
Contract report generation1+ hour~15 min
Data entry errors~5%<0.5%
Expiring PO monitoringManualAutomatic, real-time
\n\nBeyond the numbers, our client's finance team now catches expiring purchase orders before they become urgent. Automated alerts replaced calendar reminders and spreadsheet checks.\n\n## Key Takeaways\n\n### Human-in-the-loop by design\n\nAI extracts but humans approve. The Streamlit app displays extracted data alongside the source PDF. Final submit stays with the user – we automated the tedious part, not the accountability.\n\n### Audit trail matters\n\nEvery extraction logs the model version, timestamp, and full JSON payload. When questions arise months later, we can trace exactly what the system saw and produced.\n\n### Smart deduplication prevents chaos \n\nThe pipeline tracks processed files by hash. Re-running the job won't create duplicates, and reprocessing a corrected PDF cleanly updates existing records.\n\n- - -\n\nLooking to implement intelligent document processing for complex, multilingual documents? We've built production pipelines for bilingual PDFs, invoices, and contracts. \n\n[Let's talk about your use case.](https://www.boldare.com/contact/)"}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1775735524/case_study_gmoyii.png","lead":"Bilingual documents, complex tables, tight deadlines. Our client's finance team spent 15 minutes manually processing each Purchase Order – and still faced a 5% error rate. We built a Claude Vision pipeline that cut processing time to under 3 minutes and dropped errors below 0.5%.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-04-09T11:13:33.501Z","slug":"automated-purchase-order-processing-claude-vision-databricks","type":"blog","slugType":null,"category":null,"additionalCategories":["How to","GenAI"],"url":null},"author":"Aleksander Dąbrowski","authorAdditional":"","box":{"content":{"title":"Case Study: How we extracted structured data from Arabic-English PDFs with Claude Vision","tileDescription":"Automate purchase order processing with Claude Vision API. Extract data from Arabic-English PDFs, reduce errors, and speed up finance workflows.","coverImage":""},"coverImage":null}},"id":"d0497fbe-7c03-5735-b87c-60d9eeed1b1b"}},{"node":{"excerpt":"","fields":{"slug":"/blog/6-llm-integration-patterns-for-existing-codebases-without-a-full-rewrite/"},"frontmatter":{"title":"6 LLM integration patterns for existing codebases (without a full rewrite)","order":null,"content":[{"body":"## The mindset shift: LLM as a layer, not a replacement\n\nBefore diving into patterns, let's establish a key principle: **Generative AI integration** is supposed to be functional augmentation, not architectural revolution.\n\nThink about how [GitHub Copilot](https://github.com/features/copilot) works. It doesn't replace your editor – it sits alongside it, offering suggestions within the existing developer workflow. Products like Notion integrated AI into existing workflows and interfaces instead of turning it into a separate product experience. Salesforce Einstein GPT augments CRM workflows by adding generative capabilities to existing customer data, rather than requiring users to adopt a separate AI system.\n\nThe pattern is consistent: **LLM as an overlay, not an overhaul.**\n\nThis matters because it changes the conversation with stakeholders. You're not asking for budget to rebuild. You're proposing to add a capability layer that enhances what's already working.\n\n\n\n## What 2026 demands from production-grade integration\n\nLet's be clear about what \"production-ready\" generative AI integration means nowadays. Every LLM integration in a serious codebase needs to address:\n\n**Structured outputs and schema enforcement**\n\nLLMs cannot return \"almost correct\" data structures. When output feeds into deterministic business logic, you need guaranteed schema adherence. [OpenAI's Structured Outputs ](https://openai.com/index/introducing-structured-outputs-in-the-api/)(not just JSON mode) and similar features from other providers enforce this at the API level. If you're parsing LLM responses into typed objects, this is non-negotiable.\n\n**Observability** \n\nNo LLM integration without observability. This means tracing prompts and responses, tracking token usage and latency per endpoint, monitoring cost, and debugging retrieval/inference flows. Tools like [Langfuse](https://langfuse.com/), [Helicone](https://www.helicone.ai/), and [Arize](https://arize.com/) are standard infrastructure now.\n\n**Prompt versioning and management** \n\nTreat prompts like code. Version them, review them, test them. Prompt drift is real, and rollback capability is essential when a prompt change breaks downstream logic.\n\n**Evaluation loops** \n\nHow do you know the LLM is performing well? Define metrics upfront (e.g. accuracy against labeled data, latency, user satisfaction signals) and measure continuously.\n\n**Privacy controls**\n\nBefore sending user data to external LLM APIs, implement PII masking. GDPR and compliance teams will thank you.\n\nThese aren't \"nice to haves\" anymore. They're table stakes for any team that wants to ship LLM features without creating operational nightmares.\n\n## Pattern 1: Sidecar / Wrapper\n\n### How It works\n\nThe LLM runs as an aux service alongside your existing microservice. Your main application logic remains untouched while the sidecar handles all AI-related processing and exposes a simple API for your service to call when needed.\n\n```\n┌─────────────────┐ ┌─────────────────┐\n│ Your Service │────▶│ LLM Sidecar │\n│ (unchanged) │◀────│ (new service) │\n└─────────────────┘ └─────────────────┘\n```\n\n### When to use\n\n* Adding AI-generated responses to existing support ticket systems\n* Augmenting search results with semantic understanding\n* Generating summaries or translations for content already in your system\n\n### Implementation example\n\n```\n# llm_sidecar/main.py\n# A separate microservice that handles all LLM calls\n\nfrom fastapi import FastAPI\nfrom openai import OpenAI\nfrom pydantic import BaseModel\n\napp = FastAPI()\nclient = OpenAI()\n\n# Response structure — enforces consistent output format\nclass SupportResponse(BaseModel):\n response_text: str\n confidence: float\n suggested_tags: list[str]\n\n# Endpoint called by your main application\n@app.post(\"/generate-response\")\nasync def generate_support_response(ticket: dict) -> SupportResponse:\n completion = client.beta.chat.completions.parse(\n model=\"gpt-4o\",\n messages=[\n {\"role\": \"system\", \"content\": \"Generate a helpful support response.\"},\n {\"role\": \"user\", \"content\": ticket[\"description\"]}\n ],\n response_format=SupportResponse,\n temperature=0.3\n )\n return completion.choices[0].message.parsed\n```\n\n### Production constraints\n\n
AspectGuidance
Failure modeSidecar timeout or model error leaves main service waiting. Always set aggressive timeouts and define fallback behavior.
Latency fitAcceptable for async or semi-sync flows (e.g., ticket response generation). Not ideal for sub-100ms user-facing paths.
Control pointsTimeout (2-5s max), structured output schema, fallback to template response, request/response logging, rate limiting.
\n\n### Tools\n\nOpenAI API with Structured Outputs, Anthropic Claude API, Ollama for local models, BentoML for model serving.\n\n> **Pro Tip:** For latency-sensitive use cases, consider running a local model (Mistral, Llama) through Ollama. You control the infrastructure and eliminate external API dependencies.\n\n## Pattern 2: Middleware / Interceptor\n\n### How it works\n\nThe LLM is inserted into your request pipeline as middleware. It processes requests before they hit your business logic (pre-processing) or enriches responses before they're sent to clients (post-processing).\n\n```\nRequest → [LLM Middleware] → Business Logic → [LLM Middleware] → Response\n```\n\n### When to use\n\n* Semantic validation of user input before processing\n* Automatic query rewriting (natural language → SQL, GraphQL)\n* Response enrichment (adding context, translations, summaries)\n* PII detection and masking before data reaches your backend\n\n### Implementation example\n\n```\n# middleware/llm_interceptor.py\nfrom fastapi import Request\nfrom starlette.middleware.base import BaseHTTPMiddleware\nfrom openai import AsyncOpenAI\nfrom pydantic import BaseModel\n\nclient = AsyncOpenAI()\n\nclass SearchIntent(BaseModel):\n category: str | None\n color: str | None\n max_price: float | None\n keywords: list[str]\n\nclass LLMEnrichmentMiddleware(BaseHTTPMiddleware):\n async def dispatch(self, request: Request, call_next):\n # Pre-processing: enrich search requests with structured intent\n if request.url.path == \"/search\":\n body = await request.json()\n try:\n structured_query = await self.extract_search_intent(body[\"query\"])\n request.state.structured_query = structured_query\n except Exception as e:\n # Fallback: pass raw query through if LLM fails\n request.state.structured_query = None\n \n # Continue to your business logic\n response = await call_next(request)\n return response\n \n async def extract_search_intent(self, natural_query: str) -> SearchIntent:\n # LLM converts \"red shoes under $100\" → structured SearchIntent\n completion = await client.beta.chat.completions.parse(\n model=\"gpt-4o\",\n messages=[\n {\"role\": \"system\", \"content\": \"Extract search intent from natural language query.\"},\n {\"role\": \"user\", \"content\": natural_query}\n ],\n response_format=SearchIntent,\n )\n return completion.choices[0].message.parsed\n```\n\n### Production constraints\n\n
AspectGuidance
Failure modeMiddleware timeout blocks entire request. Schema validation failure on LLM output corrupts downstream logic.
Latency fitOnly for paths where 200-500ms added latency is acceptable. Never on checkout or payment flows.
Control pointsPath matching (don't run on every request), strict timeout (1-2s), schema validation with Pydantic/Zod, graceful fallback to passthrough, per-path observability.
\n\n> **Watch out:** Never put synchronous LLM calls in middleware that runs on every request. Use path matching to limit scope. Always define what happens when the LLM fails or times out.\n\n## Pattern 3: Feature Flag + Shadow Mode\n\n### How It works\n\nYou deploy the LLM integration behind a feature flag. In shadow mode, the LLM processes requests in parallel with your existing logic, but its output is logged – not served to users. This lets you compare accuracy, latency, and cost before going live.\n\n```\nRequest → Existing Logic → Response (served)\n └→ LLM Logic → Logged (not served)\n```\n\n### When to use\n\n* Validating LLM accuracy against your current system\n* A/B testing AI-generated content vs. human-written\n* Gradual rollout to percentage of users\n* Building confidence with stakeholders before full deployment\n\n### Implementation example\n\n```\n# handlers/support_ticket.py\nfrom feature_flags import is_enabled, get_variant\n\nasync def handle_ticket(ticket: dict):\n # Always run existing logic first\n existing_response = await legacy_response_generator(ticket)\n \n # Check if LLM integration is enabled via feature flag\n if is_enabled(\"llm_support_responses\"):\n try:\n llm_response = await llm_sidecar.generate_response(ticket)\n \n # Shadow mode: compare outputs without affecting users\n if get_variant(\"llm_support_responses\") == \"shadow\":\n await log_comparison(\n ticket_id=ticket[\"id\"],\n existing=existing_response,\n llm=llm_response,\n latency_delta_ms=llm_response.latency - existing_response.latency\n )\n return existing_response # Users still get the old response\n \n # Live mode: serve LLM response to users\n return llm_response\n \n except Exception as e:\n log_llm_failure(e)\n return existing_response # Fallback on any LLM failure\n \n return existing_response\n```\n\n### Production constraints\n\n
AspectGuidance
Failure modeShadow mode doubles compute cost. Comparison metrics poorly defined → false confidence in rollout readiness.
Latency fitShadow path is async/fire-and-forget. No latency impact on served response.
Control pointsFeature flag granularity (user %, geo, account tier), structured comparison logging, cost tracking per variant, automatic rollback triggers.
\n\n### Tools\n\nLaunchDarkly, Optimizely, Unleash, Flipper, or a simple Redis-backed flag store.\n\n> **Pro tip:** Define your comparison metrics upfront. Track response time, token cost, user satisfaction (if measurable), and accuracy (if you have labeled data). Don't roll out based on vibes.\n\n## Pattern 4: API Gateway with LLM\n\n### How It works\n\nA centralized gateway handles all LLM traffic. Your services don't call OpenAI or Claude directly – they call your AI Gateway, which manages routing, rate limiting, key rotation, prompt templates, and cost tracking.\n\n```\n┌──────────────┐ ┌──────────────┐ ┌──────────────┐\n│ Service A │────▶│ │────▶│ OpenAI │\n├──────────────┤ │ AI Gateway │ ├──────────────┤\n│ Service B │────▶│ │────▶│ Claude │\n├──────────────┤ │ │ ├──────────────┤\n│ Service C │────▶│ │────▶│ Local LLM │\n└──────────────┘ └──────────────┘ └──────────────┘\n```\n\n### When to use\n\n* Multiple services need LLM access\n* You need centralized cost control and observability\n* Compliance requires audit logs of all prompts and responses\n* You want to swap models without changing service code\n\n### Implementation example\n\n```\n# ai_gateway/main.py\nfrom fastapi import FastAPI, Header, HTTPException\nfrom litellm import completion\nimport hashlib\n\napp = FastAPI()\n\n# Centralized prompt management\nPROMPT_TEMPLATES = {\n \"support_response\": \"You are a helpful support agent...\",\n \"summarize\": \"Summarize the following text concisely...\",\n}\n\nresponse_cache = {}\n\n@app.post(\"/v1/complete\")\nasync def unified_completion(\n request: dict,\n x_service_name: str = Header(...), # Identifies calling service\n x_prompt_template: str = Header(None), # Optional template key\n x_cache_ttl: int = Header(0) # Cache duration in seconds\n):\n # Rate limiting per service\n if not await rate_limiter.check(x_service_name):\n raise HTTPException(429, \"Rate limit exceeded\")\n \n # Check cache for repeated requests\n cache_key = hashlib.sha256(str(request).encode()).hexdigest()\n if x_cache_ttl > 0 and cache_key in response_cache:\n return response_cache[cache_key]\n \n # Apply centralized template if specified\n system_prompt = PROMPT_TEMPLATES.get(x_prompt_template, request.get(\"system\"))\n messages = [\n {\"role\": \"system\", \"content\": system_prompt},\n {\"role\": \"user\", \"content\": request[\"prompt\"]}\n ]\n \n # Route to model with automatic fallback\n try:\n response = await completion(\n model=request.get(\"model\", \"gpt-4o\"),\n messages=messages\n )\n except Exception:\n # Fallback to secondary provider\n response = await completion(\n model=\"claude-3-haiku-20240307\",\n messages=messages\n )\n \n # Log for cost tracking and compliance\n await log_request(x_service_name, request, response)\n \n # Cache if requested\n if x_cache_ttl > 0:\n response_cache[cache_key] = response\n \n return response\n```\n\n### Production constraints\n\n
AspectGuidance
Failure modeSingle point of failure. Gateway outage = all AI features down. Requires HA deployment.
Latency fitAdds 10-50ms overhead. Acceptable for most use cases.
Control pointsPer-service rate limits, prompt template versioning, model fallback chain, response caching, full audit logging, cost dashboards, PII filtering before external calls.
\n\n### Tools\n\nKong, Tyk, custom FastAPI gateway, LiteLLM Router, Portkey, Helicone.\n\n> **2026 Trend:** AI Gateways are becoming standard infrastructure. They handle prompt versioning, A/B testing between models, automatic fallback (GPT-4 → Claude → local), and real-time cost dashboards. If you're integrating LLM across multiple services, build this early.\n\n## Pattern 5: Event-Driven / Async Processing\n\n### How It works\n\nThe LLM operates asynchronously, triggered by events in a message queue. It processes work in the background without blocking user-facing requests.\n\n```\nUser Action → Queue (Kafka/SQS) → LLM Worker → Result Store → Notification\n```\n\n### When to use\n\n* Batch processing (summarizing daily logs, generating reports)\n* Non-blocking enrichment (recommendations sent after purchase)\n* Heavy processing that would timeout in synchronous flow\n* Cost optimization through batching\n\n### Implementation example\n\n```\n# workers/llm_processor.py\nfrom kafka import KafkaConsumer\nimport json\n\nconsumer = KafkaConsumer(\n 'content-to-summarize',\n bootstrap_servers=['localhost:9092'],\n value_deserializer=lambda m: json.loads(m.decode('utf-8'))\n)\n\ndef process_batch(messages: list):\n # Batch multiple items into single LLM call for efficiency\n combined_prompt = \"\\n---\\n\".join([m[\"content\"] for m in messages])\n \n response = llm_client.complete(\n prompt=f\"Summarize each section separated by ---:\\n{combined_prompt}\",\n response_format=BatchSummaryResponse\n )\n \n # Store results with full tracing\n for msg, summary in zip(messages, response.summaries):\n result_store.save(\n id=msg[\"id\"],\n summary=summary,\n trace_id=response.trace_id,\n tokens_used=response.usage.total_tokens\n )\n\n# Batch processing: collect 10 messages, then process\nbatch = []\nfor message in consumer:\n batch.append(message.value)\n if len(batch) >= 10:\n process_batch(batch)\n batch = []\n```\n\n### Production constraints\n\n
AspectGuidance
Failure modeDead letter queue fills up. Results never arrive. User sees stale data indefinitely.
Latency fitNot for user-facing sync flows. Results available minutes to hours later.
Control pointsDLQ monitoring, batch size limits, processing timeout per message, idempotency keys, result TTL, cost tracking per batch.
\n\n### Tools\n\nKafka, RabbitMQ, AWS SQS, Redis Streams, Temporal.io for orchestration.\n\n> **Pro tip:** Batching can materially reduce inference cost and request overhead in asynchronous workflows, especially for repeatable summarization and enrichment jobs. Combine related items into batched requests where the use case allows.\n\n## Pattern 6: Model-Agnostic Abstraction Layer\n\n### How It works\n\nYou build an internal \"AI SDK\" that abstracts away the specific model provider. Your application code calls your SDK; the SDK handles routing to Claude, GPT, Mistral, or a local model.\n\n```\n# Your code calls this:\nresponse = await ai_sdk.complete(task=\"summarize\", content=text)\n\n# SDK handles:\n# - Model selection based on task\n# - Fallback if primary model fails\n# - Response schema validation\n# - Cost tracking\n# - Observability\n```\n\n### When to use\n\n* You want flexibility to switch providers without code changes\n* Different tasks need different models (fast/cheap vs. slow/accurate)\n* You're preparing for a future where model pricing and capabilities shift rapidly\n* Enterprise policy requires multi-vendor strategy\n\n### Implementation example\n\n```\n# ai_sdk/client.py\nfrom litellm import completion\nfrom pydantic import BaseModel\n\nclass AIClient:\n # Route tasks to optimal models with fallbacks\n MODEL_ROUTING = {\n \"summarize\": [\"claude-3-haiku-20240307\", \"gpt-4o-mini\"], # Fast, cheap\n \"analyze\": [\"gpt-4o\", \"claude-sonnet-4-20250514\"], # Accurate\n \"generate\": [\"claude-sonnet-4-20250514\", \"gpt-4o\"], # Balanced\n }\n \n async def complete(\n self, \n task: str, \n content: str, \n response_schema: BaseModel = None,\n **kwargs\n ):\n models = self.MODEL_ROUTING.get(task, [\"gpt-4o-mini\"])\n \n # Try each model in order until one succeeds\n for model in models:\n try:\n response = await completion(\n model=model,\n messages=[{\"role\": \"user\", \"content\": content}],\n response_format=response_schema\n )\n await self.log_success(task, model, response)\n return response\n except Exception as e:\n await self.log_failure(task, model, e)\n continue\n \n raise AllModelsFailedError(task, models)\n\n# Usage in your application — no direct provider dependencies\nai = AIClient()\nsummary = await ai.complete(\"summarize\", long_text, response_schema=SummarySchema)\n```\n\n### Production constraints\n\n
AspectGuidance
Failure modeAbstraction hides model-specific behaviors. Debugging becomes harder. Fallback chain masks repeated failures.
Latency fitDepends on underlying models. Abstraction adds minimal overhead (<10ms).
Control pointsPer-task model routing config, fallback chain definition, unified observability across providers, cost allocation per task type, capability feature flags (e.g., vision, function calling).
\n\n### Tools\n\nLiteLLM, LangChain, Portkey, custom abstraction.\n\n> **Why this matters in 2026:** Enterprise increasingly uses multiple model families rather than a single provider. This pattern is no longer optional for teams that want operational flexibility and cost optimization.\n\n## Decision framework: Which pattern should you use?\n\nInstead of a simple \"situation → pattern\" mapping, consider these four criteria:\n\n
CriterionQuestions to AskPattern Implications
Latency sensitivityIs this in a user-facing sync path? Sub-500ms requirement?High sensitivity → Sidecar with aggressive timeout, or Async. Never Middleware on hot paths.
Blast radiusIf this fails, what breaks? Core checkout? Internal tooling?High blast radius → Shadow mode first, Gateway for centralized control, aggressive fallbacks.
Compliance / PII exposureDoes data leave your infrastructure? GDPR/HIPAA constraints?High exposure → Gateway with PII masking, audit logging, possibly local models only.
Model portabilityDo you need to switch providers? Multi-model strategy?High portability need → Abstraction Layer, Gateway with routing.
\n\n### Quick reference\n\n
Your SituationStart WithWhy
Monolith, low risk toleranceSidecar + Feature FlagIsolated, easy rollback
Microservices, multiple teamsAPI GatewayCentralized control, cost visibility
High-volume, latency-tolerantEvent-drivenCost-efficient, non-blocking
Request enrichment/validationMiddlewareClean pipeline integration (with strict timeouts)
Uncertain about model choiceAbstraction LayerFlexibility to pivot
Regulated industryGateway + Shadow ModeAudit trail, gradual validation
\n\n**Start small.** Pick one use case, one pattern, and prove value before expanding.\n\n## Antipatterns to avoid\n\n**Synchronous LLM in hot path**\n\nA 2-second LLM call in your checkout flow will kill conversion. If it must be synchronous, cache aggressively, set strict timeouts, and always have a non-LLM fallback.\n\n**No caching strategy**\n\nIdentical prompts should return cached responses. Without this, costs spiral and latency becomes unpredictable.\n\n**Hardcoded prompts**\n\nTreat prompts like code – version them, review them, test them. Prompt drift is real, and you need rollback capability.\n\n**LLM as black box**\n\nLog prompts, responses, latency, and token usage. You can't optimize what you can't measure. Observability tools like Langfuse, Helicone, or custom logging are essential infrastructure.\n\n**JSON mode instead of Structured Outputs**\n\nIf you're parsing LLM output into business logic, use proper schema enforcement (OpenAI Structured Outputs, Anthropic tool use with schemas). \"Almost valid JSON\" will corrupt your data.\n\n**Skipping PII considerations**\n\nBefore sending user data to external LLM APIs, implement masking. GDPR and compliance teams will thank you.\n\n**No evaluation loop**\n\nHow do you know quality is maintained over time? Define metrics, measure continuously, alert on drift.\n\n\n\n## Getting started\n\nYou don't need permission to experiment. Most of these patterns can be prototyped in a day:\n\n1. **Pick a low-risk use case** – internal tooling, batch reports, non-critical features\n2. **Deploy a sidecar** with a simple REST endpoint and structured outputs\n3. **Add observability from day one** – even basic logging beats nothing\n4. **Run in shadow mode** for a week, collect comparison data\n5. **Review results** with your team – latency, accuracy, cost\n6. **Expand** **or** **pivot** based on evidence\n\nThe goal isn't to \"add AI\" alone but to solve a real problem faster or better than you could before. The patterns just help you do it without breaking what's already working.\n\n## Ready to integrate LLM without the risk?\n\nBoldare helps engineering teams design and deploy **generative AI integration** patterns matched to their stack **–** Python, Node.js, Java, Kotlin, Go. We've done this for energy providers, SaaS platforms, and enterprise systems that couldn't afford downtime.\n\n[Talk to our AI integration team](https://www.boldare.com/contact/) now."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1775720466/INTEGRATION_PATTERNS_hstnaw.png","lead":"According to the [2026 State of AI Infrastructure Report by DDN](https://www.ddn.com/2026-state-of-ai-infrastructure-report), 54% of enterprises have delayed or cancelled AI projects in the past two years – often because they approached AI as a full-stack transformation rather than a targeted integration. The organizations succeeding with LLM adoption share a common trait: they're not rewriting their systems. They're augmenting them.\n\nThis article walks through six proven patterns for adding LLM capabilities to your existing systems. Whether you're running a decade-old monolith or a sprawling microservices landscape, there's a path forward that doesn't involve rewriting your core.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-04-08T13:58:31.300Z","slug":"llm-integration-patterns","type":"blog","slugType":null,"category":null,"additionalCategories":["GenAI","How to"],"url":null},"author":"Aleksander Dąbrowski","authorAdditional":"","box":{"content":{"title":"6 LLM integration patterns for existing codebases (without a full rewrite)","tileDescription":"Learn six proven ways to integrate LLMs into existing codebases safely, incrementally, and without a full rewrite of your system.","coverImage":""},"coverImage":null}},"id":"b8b0e420-8a35-56d5-bd9d-700f408151f6"}},{"node":{"excerpt":"","fields":{"slug":"/blog/how-to-build-a-production-rag-system-that-doesnt-hallucinate/"},"frontmatter":{"title":"How to build a production RAG system that doesn't hallucinate","order":null,"content":[{"body":"## Not every bad answer is a hallucination\n\nBefore you can fix the problem, you need to diagnose it correctly. The “hallucinations” have become a universal complaint, but in reality, it consists of several distinct failure modes, each requiring a different solution:\n\n**Retrieval miss** \n\nThe system didn't find the right documents. Your user asked about Q3 revenue, but the retriever pulled Q2 data instead. The LLM answered accurately based on what it received, just not what was needed.\n\n**Missing context**\n\nThe retrieved chunk exists but lacks surrounding information. A sentence saying \"The agreement was terminated\" tells you nothing without knowing which agreement, when, or why. The chunk was found; its meaning was lost.\n\n**Grounding failure** \n\nThe model had the right context but ignored it. Instead of synthesizing from retrieved documents, it fell back on parametric knowledge or generated plausible-sounding fiction.\n\n**Unsupported answer** \n\nThe response goes beyond what the sources actually say. The documents mention \"strong growth\"; the model outputs \"47% year-over-year increase.\" Close, but fabricated.\n\n**Prompt injection** \n\nA malicious or accidental input manipulated the system's behavior. Someone embedded instructions in a document, or a user query contained a payload that altered the generation.\n\nImproving your embedding model alone won't fix grounding failures. Writing better prompts won't solve retrieval misses. Security hardening won't help with missing context. That’s why each of these failure modes requires different countermeasures. \n\n**Diagnosis before treatment. Always.**\n\n## Retrieval quality: the foundation of trustworthy answers\n\nThe highest-impact improvement in most RAG systems is **retrieval** **quality**. If the model receives wrong, incomplete, or irrelevant context, no amount of prompt engineering will save you.\n\n### Move beyond naive vector search\n\nThe 2023-era pattern of \"embed query, find top-k similar chunks, stuff into prompt\" doesn't scale to production. [Modern retrieval requires multiple strategies working together.](https://aclanthology.org/2025.coling-main.449/) These include:\n\n**Hybrid search** combines dense retrieval (embeddings) with sparse retrieval (keyword/BM25). Dense search captures semantic similarity –\"revenue\" matches \"earnings.\" Sparse search captures exact terms –\"Q3-2025\" matches \"Q3-2025.\" Neither alone is sufficient. Together, they cover more ground.\n\n**Reciprocal Rank Fusion (RRF)** merges results from multiple retrievers into a single ranked list. Instead of picking one retrieval method, you run several in parallel and let RRF combine their rankings. This consistently outperforms any single retriever and is straightforward to implement.\n\n**[Query rewriting](https://arxiv.org/abs/2411.13154)** addresses the gap between how users ask questions and how information is stored. A query like \"What did we decide about the X deal?\" might need expansion: \"X account,\" \"X contract,\" \"X partnership,\" \"X negotiation.\" Multi-query retrieval generates variations and unions the results.\n\n**[Reranking ](https://sbert.net/examples/sentence_transformer/applications/retrieve_rerank/README.html)**adds a second-stage filter. After initial retrieval returns 50-100 candidates, a cross-encoder model re-scores each chunk against the original query. This catches semantic matches that vector similarity missed and pushes irrelevant results down. The latency cost is usually worth the precision gain.\n\n### Contextual retrieval: chunks need context\n\nHere's a failure mode that's easy to miss: a chunk that's technically correct but meaningless in isolation.\n\nConsider a document about three different software products. A chunk reading \"The system supports up to 10,000 concurrent users\" is useless without knowing which system. Traditional chunking strips this context away.\n\n[Contextual retrieval](https://www.anthropic.com/engineering/contextual-retrieval)solves this by attaching a brief description to each chunk before embedding. Instead of indexing the raw text, you index: \"This section describes the scalability limits of Product X, our enterprise middleware platform. The system supports up to 10,000 concurrent users.\"\n\nThe description is generated once at indexing time (typically by an LLM summarizing the chunk's place in the larger document). The cost is minimal, but the improvement in retrieval relevance is siginificant.\n\n### Chunking still matters\n\nNo amount of sophisticated retrieval compensates for poor chunking. The fundamentals:\n\n* **Size:** 200-500 tokens is usually the sweet spot. Too small loses context; too large reduces relevance.\n* **Overlap:** 10-20% overlap between chunks prevents information from falling into gaps.\n* **Semantic boundaries:** Split on paragraph or section breaks, not arbitrary token counts. A chunk that ends mid-sentence is a chunk that confuses your model.\n* **Metadata preservation:** Keep source, date, author, section headers. You'll need them for attribution and filtering.\n\n## The generation layer\n\nWhile retrieval gets the right information into the context window, generation determines whether the model actually uses it.\n\n### Citation at the claim level\n\nThe minimum bar for production RAG is source attribution. You need a citation at the claim level.\n\nEvery factual statement in the output should trace to a specific passage in the retrieved context. Not \"according to company documents\" but \"according to the Q3 Financial Review, page 12.\"\n\nThis isn't just about user trust (though it helps). Claim-level citation forces the model to ground each statement, making hallucinations structurally harder. It also makes verification possible – your QA team can spot-check whether citations actually support their claims.\n\n### Confidence scoring and refusal behavior\n\nProduction RAG systems need to know when they don't know.\n\nConfidence scoring evaluates whether the retrieved context actually supports a complete answer. This can be implemented through:\n\n* **Coverage analysis:** Does the context contain information relevant to each part of the query?\n* **Contradiction detection:** Do retrieved chunks conflict with each other?\n* **Source quality signals:** Are the sources authoritative and current?\n\nWhen confidence is low, the system should **fail closed** – refuse to answer rather than guess.\n\nThis is counterintuitive for teams trained on chatbot metrics where response rate matters. But in enterprise contexts, a confident wrong answer creates legal exposure, operational errors, and broken trust. *\"I don't have enough information to answer that accurately\"* is the correct response when evidence is insufficient.\n\nImplement explicit refusal behavior:\n\n* **Lower confidence threshold** → \"I couldn't find sufficient information to answer this reliably\"\n* **Contradictory sources** → \"I found conflicting information on this topic. Here's what each source says...\"\n* **Partial coverage** → \"I can answer part of your question, but I don't have information about X\"\n\n### Prompt architecture for grounding\n\nYour system prompt should explicitly instruct the model to:\n\n1. Answer only based on provided context\n2. Cite specific sources for each claim\n3. Acknowledge when information is missing\n4. Never extrapolate beyond what sources state\n\nBut don't rely on prompts alone. Prompts are merely suggestions, the architecture is the actual enforcement. Combine prompt-level instructions with output validation that verifies claims against retrieved context.\n\n## Security by design\n\nA conversation about production RAG in 2026 without mentioning security matters is incomplete . Two threat classes demand attention: **prompt injection** and **data leakage**.\n\n### Prompt injection defense\n\nPrompt injection occurs when user input or document content manipulates the system's behavior by actions like overriding instructions, extracting system prompts, or causing unintended actions.\n\n**Defense requires multiple layers:**\n\n* **Input validation** screens queries for injection patterns before they reach the model. This catches obvious attacks but won't stop sophisticated ones.\n* **Instruction-data separation** architecturally distinguishes system instructions from user content. Techniques include hierarchical prompting, XML-tagged sections, and instruction placement strategies that make override attempts harder.\n* **Output validation** checks responses for signs of successful injection – system prompt leakage, out-of-scope content, unexpected format changes.\n* **Retrieval-level filtering** prevents malicious document content from reaching the model. If someone embeds \"Ignore previous instructions\" in a PDF, it shouldn't survive preprocessing.\n\nPlease note that no single defense is sufficient. Traditional content filters catch maybe 60% of attacks. Defense-in-depth (consisting of multiple independent layers) is the only viable approach.\n\n### Data authorization and leakage prevention\n\nRAG systems aggregate information. That's the main point, but also a huge risk.\n\n**Pre-retrieval authorization** checks user permissions before searching. If a user shouldn't see HR documents, those documents shouldn't enter their retrieval results – not filtered out after retrieval, but excluded from the search entirely.\n\n**Metadata filtering i**mplements least-privilege retrieval. Tag documents with access levels, departments, classification status. Filter at query time based on user context.\n\n**Output filtering** catches sensitive information that made it through retrieval – PII, credentials, confidential markers. This is your last line of defense.\n\n**Audit logging** records what was retrieved, what was generated, and who saw it. When (not if) you need to investigate an incident, you need the trail.\n\nData governance isn't optional for enterprise RAG. It's the difference between a useful tool and a compliance violation waiting to happen.\n\n## Continuous evaluation\n\nProduction systems need production-grade testing. For RAG, this means automated evaluation pipelines that run on every deployment.\n\n### Core metrics\n\n* **Faithfulness** measures whether the response is supported by the retrieved context. A faithful answer doesn't add information the sources don't contain.\n* **Answer relevancy** measures whether the response actually addresses the query. High faithfulness with low relevancy means you accurately reported irrelevant information.\n* **Contextual precision** measures whether retrieved chunks are actually relevant. High precision means less noise in the context window.\n* **Contextual recall** measures whether retrieval captured the information needed to answer. Low recall means relevant documents were missed.\n* **Answer correctness** compares responses against known ground truth (when you have it). This catches cases where the system is faithful to bad sources.\n\nFrameworks like RAGAS provide [standardized implementations](https://docs.ragas.io/en/v0.1.21/concepts/metrics/) of these metrics. They're designed for automation, not one-time assessment.\n\n### Building evaluation into CI/CD\n\nEvaluation belongs in your deployment pipeline, not in quarterly reviews.\n\n**Golden sets** are curated question-answer pairs with verified correct responses. Run them on every release candidate. Regressions fail the build.\n\n**Adversarial prompts** test edge cases and attack resistance. Include injection attempts, ambiguous queries, questions requiring information you don't have.\n\n**Regression tracking** monitors metric trends over time. A 2% faithfulness drop might not fail any single test but signals degradation worth investigating.\n\n**Shadow evaluation** runs new model versions against production traffic (without serving responses) to compare behavior before cutover.\n\nThe goal is catching problems before users do, so forget monitoring in production – it’s not a testing strategy.\n\n## Observability: seeing the whole chain\n\nRAG failures are debugging nightmares without proper observability. The answer was wrong – but was it retrieval? Ranking? Generation? The prompt? That’s why you need visibility into every step.\n\n### Tracing end-to-end\n\nInstrument your pipeline to capture:\n\n* **Query:** Original input, normalized form, any rewrites\n* **Retrieval:** Which chunks were retrieved, their scores, which retriever produced them\n* **Reranking:** Score changes, final ordering\n* **Context assembly:** What actually went into the prompt\n* **Generation:** Full response, token usage, latency\n* **Validation:** Confidence scores, any triggered guardrails\n* **Outcome:** User feedback, downstream actions\n\n[OpenTelemetry](https://opentelemetry.io/) has become the standard for LLM telemetry. Dedicated tools like [LangSmith](https://smith.langchain.com/), [LangFuse](https://langfuse.com/), or Phoenix provide RAG-specific visualization and analysis.\n\n### Dashboards and alerts\n\nAggregate metrics need monitoring:\n\n* **Retrieval quality:** Average relevance scores, empty result rates, latency percentiles\n* **Generation quality:** Faithfulness scores, refusal rates, citation density\n* **Error rates:** Timeouts, validation failures, guardrail triggers\n* **Usage patterns:** Query volumes, peak times, token consumption\n\nSet alerts on anomalies. A sudden spike in refusal rates might indicate a retrieval problem. Dropping faithfulness scores suggest grounding issues. Unusual query patterns might signal abuse.\n\n### Human feedback loops\n\nAutomated metrics aren't everything. Build mechanisms for human feedback:\n\n* **Thumbs up/down** on responses\n* **Citation verification** by reviewers\n* **Escalation paths** for uncertain cases\n* **Correction** **workflows** that feed back into golden sets\n\nThe systems that improve fastest are the ones that learn from production.\n\n## Architecture decisions: when to use what\n\nNot every RAG system needs every technique. Here's a practical guide to complexity budgeting.\n\n**Start with 2-step RAG** (retrieve → generate) when:\n\n* Document corpus is small and homogeneous\n* Queries are predictable and well-formed\n* Accuracy requirements are moderate\n* You're proving value before investing in infrastructure\n\n**Add hybrid search and RRF** when:\n\n* Corpus mixes technical terms with natural language\n* Users phrase similar questions differently\n* Single-retriever recall isn't meeting accuracy targets\n\n**Add reranking** when:\n\n* Initial retrieval returns many marginally relevant results\n* Context window is limited (you need to pick the best chunks)\n* Query-document semantic matching is nuanced\n\n**Add query rewriting** when:\n\n* User queries are often ambiguous or incomplete\n* Same information is described different ways across documents\n* Multi-hop reasoning is required (combining information from multiple sources)\n\n**Separate indexes** when:\n\n* Multi-tenant with strict data isolation\n* Dramatically different document types (code vs. legal vs. marketing)\n* Different retrieval strategies needed per domain\n\n**Add workflow orchestration** when:\n\n* Complex queries require decomposition\n* Different query types need different processing paths\n* Multi-step reasoning with intermediate validation\n\nMore complexity means more maintenance, more failure modes, more debugging surface. Add capabilities when you have evidence they're needed, not because they're available.\n\n## Production readiness checklist\n\nBefore you ship:\n\n**Retrieval**\n\n* Chunking strategy tested and tuned for your corpus\n* Contextual retrieval implemented (chunks have surrounding context)\n* Hybrid search (dense + sparse) configured\n* Reranking evaluated and deployed if beneficial\n* Query rewriting tested on ambiguous inputs\n\n**Generation**\n\n* Citation at claim level, not just document level\n* Confidence scoring implemented\n* Refusal behavior defined and tested\n* Grounding verified (model uses context, not parametric knowledge)\n\n**Security**\n\n* Pre-retrieval authorization enforced\n* Input validation for injection patterns\n* Output filtering for sensitive data\n* Audit logging in place\n\n**Evaluation**\n\n* Golden set created and baselined\n* RAGAS or equivalent metrics automated\n* Adversarial test suite included\n* Regression testing in CI/CD\n\n**Observability**\n\n* End-to-end tracing implemented\n* Dashboards for key metrics\n* Alerts on quality degradation\n* Human feedback mechanism deployed\n\n**Operations**\n\n* Fallback behavior defined\n* Incident response documented\n* Model update process established\n* Cost monitoring and limits in place\n\n\n\n## The bottom line\n\nProduction RAG that doesn't hallucinate isn't a matter of finding the right prompt or the best model. It's architecture – retrieval quality, grounded generation, security controls, continuous evaluation, and operational visibility working together.\n\nThe gap between demo and production is real, but it's not mysterious. The techniques exist. The frameworks exist. The patterns are proven.\n\nWhat's required is treating RAG as a system to be engineered, not a feature to be enabled.\n\n- - -\n\nBuilding a production RAG system and looking for RAG implementation best practices matched to your stack? [Let's talk ](https://www.boldare.com/contact/)about your architecture."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1775649029/rag_in_production_pdzszw.png","lead":"Most RAG proof-of-concepts work beautifully in demos. You simply feed a few PDFs into a vector database, wire up an LLM, and watch it answer questions about your documents. The CEO is impressed and the board is excited. But then you try to deploy it.\n\nAnd suddenly, answers that seemed reasonable start contradicting your source material or the system confidently cites documents that don't exist. \n\nThis is the **production RAG gap** – the difference between a working demo and a system you can actually trust with enterprise decisions. The core issue isn't that RAG doesn't work. It's that \"hallucination prevention\" requires architectural thinking, not prompt engineering.\n\nIn 2026, we know enough about production RAG failures to prevent them systematically. This article covers the **RAG implementation best practices** that separate reliable production systems from impressive demos.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-04-08T10:43:30.904Z","slug":"how-to-build-a-production-rag-system","type":"blog","slugType":null,"category":null,"additionalCategories":["How to","GenAI","Tech"],"url":null},"author":"Aleksander Dąbrowski","authorAdditional":"","box":{"content":{"title":"How to build a production RAG system that doesn't hallucinate","tileDescription":"Build a production RAG system that reduces hallucinations with better retrieval, grounded answers, security controls, and continuous evaluation.","coverImage":""},"coverImage":null}},"id":"c023a948-6673-51c4-a31c-c00faadf9059"}},{"node":{"excerpt":"","fields":{"slug":"/blog/rag-vs-fine-tuning-which-approach-is-right-for-your-use-case/"},"frontmatter":{"title":"RAG vs Fine-Tuning: Which approach is right for your use case?","order":null,"content":[{"body":"## What is RAG?\n\n**RAG** (Retrieval-Augmented Generation) doesn't change your model at all. Instead, it changes what the model sees before it answers.\n\n**Here's the core idea:** when a user asks a question, your system first retrieves the most relevant chunks of information from your own knowledge store (e.g documents, databases, wikis, support tickets or whatever you've indexed), and then passes those chunks as context to the LLM alongside the original question. The model generates its response grounded in that retrieved content.\n\nThink of it like the difference between asking a consultant to answer from memory versus handing them the right documents first.\n\n**A typical RAG pipeline in 2026 looks like this:**\n\n1. **Embed** – Your documents are chunked and converted into vector embeddings (using models like OpenAI's text-embedding-3-small, Cohere embeddings, or Jina)\n2. **Store** – Embeddings live in a vector database: Weaviate, Pinecone, Qdrant, or Milvus for on-prem setups\n3. **Retrieve** – On each query, semantically similar chunks are fetched\n4. **Re-rank** – A reranker (Cohere, BGE) filters for the most relevant results\n5. **Generate** – The LLM receives the retrieved context and produces a grounded response\n\nOrchestration layers like LangChain, LlamaIndex, Haystack 2.0, or Dust connect these components into a working pipeline.\n\nThe RAG ecosystem has evolved significantly. Modern variants include Graph RAG (retrieval over a knowledge graph of relationships, not just flat documents), Hybrid RAG (combining semantic + keyword search for better recall), and Memory RAG (caching conversation history as vectors to enable continuity across sessions). These serve as production patterns for enterprise deployments.\n\nThe key insight from an integration standpoint: **RAG is a layer you build around the model, not inside it.** That makes it composable, updatable, and model-agnostic – which matters a lot when you're building a product that needs to evolve.\n\n## What is Fine-Tuning?\n\nFine-tuning takes a different route entirely. Instead of changing what the model sees, it changes the model itself by adjusting the weights through additional training on your own dataset so that the model internalizes new behaviors, styles, or domain knowledge.\n\nA fine-tuned model doesn't need to be told how to sound like your brand – it just does. It doesn't need lengthy examples in the prompt to classify support tickets correctly because it already knows the categories.\n\nIn 2026, fine-tuning is more accessible than it was two years ago, largely due to parameter-efficient methods that make it feasible without massive GPU clusters:\n\n* **LoRA / LoRA 2.0** *(Low-Rank Adaptation)* – freezes most model weights and trains small adapter matrices, dramatically reducing compute\n* **QLoRA** – quantized LoRA, enabling fine-tuning of 7B–13B parameter models on consumer-grade hardware\n* **PEFT adapters** – modular, swappable components available through Hugging Face's PEFT Hub\n\nThe open-weight ecosystem (Llama 3, Mistral Large, Falcon 2, Phi-3) makes this even more attractive. **Fine-tuning a 7B open-weight model costs a few hundred dollars.** Fine-tuning via a closed API (like OpenAI's fine-tuning endpoint) can run into thousands per training run, with ongoing inference costs on top.\n\nOn inference: a fine-tuned open model running on an A100 GPU costs roughly **~$0.001** per query. GPT-4 Turbo via API runs around **~$0.01** per query – a 10x difference that compounds fast at scale.\n\n**The catch:** fine-tuning requires **high-quality training data**. Without several hundred to several thousand well-labeled examples, you won't see meaningful improvement. And every time your domain shifts by new products, policies or terminology you need to retrain. That's **fine-tuning debt**, and it can be a real maintenance burden.\n\n## Key differences: RAG vs Fine-Tuning\n\n
CriterionRAGFine-Tuning
What it changesModel's input contextModel's weights
Customization depthModerate - contextual groundingHigh - behavioral & stylistic
Data freshnessReal-time (update the index)Snapshot from training time
Cost to implementMedium (pipeline + infra)Medium–High (training + data prep)
Inference costDepends on model usedLow if self-hosted open model
MaintenanceKeep knowledge base currentRetrain when domain shifts
Security / PrivacyKnowledge store is external riskData stays local if on-prem
Hallucination riskReduced by grounding in sourcesDepends on training data quality
TransparencyCan cite sources directlyOutput is model-internal
Time to first deploymentDays to weeksWeeks to months
Best forDynamic knowledge, factual accuracyTone, style, narrow classification
\n\n## When to choose RAG\n\nRAG is the right default for most enterprise LLM integrations – especially when you're working with knowledge that exists already, changes frequently, or needs to be auditable.\n\n**Choose RAG when:**\n\n* Your knowledge base changes more than once a month (product docs, pricing, policies, support FAQs)\n* You need the AI to cite sources (important in legal, finance, and healthcare contexts)\n* You're working with unstructured technical documentation where exact retrieval matters more than stylistic output\n* You want to get to production fast without a labeled training dataset\n* Data privacy is a concern – self-hosted retrieval with Qdrant or Milvus keeps your content off third-party infrastructure\n\n**Real-world pattern:** A customer support assistant connected to a Confluence knowledge base via RAG. When the product changes, you update Confluence, not the model. The assistant stays accurate automatically.\n\n**Architectural tip:** Use RAG when your prompt is already long and context-heavy. Retrieval offloads that burden while keeping the model grounded.\n\n**One important disclaimer:** if your knowledge base contains sensitive data you can't send to an external API, architect for o**n-prem embeddings and self-hosted retrieval** from the start. Retrofitting privacy tends to be painful.\n\n## When to choose Fine-Tuning\n\nFine-tuning earns its cost when the problem is about **how** the model behaves, not **what** it knows. It's the right tool when you've hit the ceiling of what prompt engineering can achieve.\n\n**Choose fine-tuning when:**\n\n* You need consistent brand voice or tone that prompt instructions alone can't reliably enforce\n* You're doing **narrow** **classification** in a specialized domain: medical symptom triage, financial document tagging, legal clause extraction\n* You need to **reduce** **token** **usage** – a fine-tuned model can perform a task with a much shorter prompt, cutting per-query cost\n* You're deploying **on-device or edge AI** where the model must be small, fast, and offline-capable\n* Your task is repetitive and well-defined with a clean labeled dataset\n\n**2026 examples:**\n\n* A fintech voice assistant fine-tuned to speak in the product's exact regulatory tone\n* A medical app with a symptom classifier running locally on mobile (QLoRA fine-tuned Phi-3)\n* A SaaS product using a fine-tuned Llama 3 8B model instead of GPT-4 Turbo, cutting inference costs by 8–10x\n\n**Watch out for fine-tuning debt**. Every time your product evolves, your training data is stale. Teams underestimate this – that’s why building a retraining pipeline should be part of the commitment.\n\n**Useful tools:** Hugging Face PEFT Hub, Axolotl, Unsloth (for fast QLoRA), MosaicML.\n\n## Why not both?\n\nIn production, the most capable enterprise AI systems often use RAG and fine-tuning together. And this isn't overengineering. It's just using each tool for what it's good at.\n\n**The pattern:** Fine-tune the model for style and behavior, then add RAG for current knowledge.\n\n**A real-world example**: a SaaS company fine-tunes Llama 3 on their historical customer conversations, so the AI learns their communication style, terminology, and tone. Then they layer in RAG connected to their live product documentation. The result? An AI that sounds like the brand and knows today's pricing.\n\n**The architecture looks like this:**\n\nUser Query\n\n↓\n\n\\[RAG Layer] → Retrieve relevant docs → Inject as context\n\n↓\n\n\\[Fine-tuned Model] → Generate response in brand voice\n\n↓\n\nResponse (grounded + on-brand)\n\nThis **hybrid** **approach** is increasingly the standard for mature enterprise LLM products. The sequencing matters: fine-tune first to establish baseline behavior, then add retrieval for knowledge freshness.\n\n## How to justify the choice to your board\n\nHere's how to translate the architecture choice into business language:\n\n**RAG:**\n\n* Lower upfront investment, faster time-to-value\n* Knowledge stays current without engineering effort per update\n* Reduces AI hallucination risk – auditable, citable answers\n* Vendor flexibility: swap the underlying model without rebuilding\n\n**Fine-tuning:**\n\n* Upfront training cost offset by long-term inference savings (especially at scale)\n* Proprietary model behavior = competitive differentiation\n* Reduced dependency on prompt engineering complexity\n* Open-weight fine-tuned model = no API vendor lock-in\n\n**The honest summary:** RAG is lower risk to start. Fine-tuning is a strategic investment that pays off when you have volume, clear data, and a stable enough domain to make retraining manageable.\n\n## Quick decision checklist\n\nRun through these before your next architecture decision:\n\n**Does your knowledge change frequently?** → RAG\n\n**Is consistent tone / brand voice the core requirement?** → Fine-tuning\n\n**Do you need to cite sources in outputs?** → RAG\n\n**Are your API inference costs already too high at scale?** → Fine-tuned open-weight model\n\n**Do you have 500+ high-quality labeled examples?** → Fine-tuning is viable\n\n**Do you need to ship in under a month?** → RAG first, fine-tune later\n\n**Is the data too sensitive to send to an external API?** → On-prem RAG or self-hosted fine-tuned model\n\n**Is the task narrow and repetitive?** → Fine-tuning; **Is it broad and knowledge-dependent?** → RAG\n\n## Final thoughts\n\nRAG and fine-tuning are both mature, production-ready approaches — but they solve different problems. Most teams that struggle with LLM integration are using one when they need the other, or haven't planned for the maintenance burden of either.\n\nThe best LLM stacks in 2026 aren't built around a single technique. They're built around a clear understanding of what the model needs to know versus how it needs to behave — and they layer accordingly.\n\nPlanning your LLM integration architecture? Boldare's team works across the full stack – from RAG pipelines with on-prem retrieval to fine-tuned open-weight models optimized for your data and cost structure.[](https://www.boldare.com/services/llm-integration-api-development/#contact)\n\n[Let's talk about what fits your use case.](https://www.boldare.com/contact/)"}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1775563752/rag_or_finetuning_ius8nw.png","lead":"You've connected your product to the latest GPT, Claude, or Gemini model. The API works. The model responds. And yet – your users get answers that feel generic, disconnected from your product, your data, your brand. The AI doesn't know what your company actually does.\n\nThis is the moment most teams hit the real question: how do you make an LLM genuinely yours?\n\n**In 2026, two approaches dominate that conversation:** Retrieval-Augmented Generation (**RAG**) and fine-tuning. Both solve the customization problem but in fundamentally different ways, at different costs, with different tradeoffs. Choosing the wrong one can mean months of wasted engineering work, ballooning API bills, or an AI product that still doesn't deliver.\n\nThis article will give you a clear, practical framework for making that call.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-04-07T12:40:03.480Z","slug":"rag-vs-fine-tuning","type":"blog","slugType":null,"category":null,"additionalCategories":["Tech","GenAI","How to"],"url":null},"author":"Aleksander Dąbrowski","authorAdditional":"","box":{"content":{"title":"RAG vs Fine-Tuning: Which Approach Is Right for Your Use Case?","tileDescription":"RAG retrieves knowledge, fine-tuning shapes behavior. Learn how to choose the right LLM customization approach for your use case in 2026.","coverImage":""},"coverImage":null}},"id":"5fb29c67-5f71-516a-8243-6866120d52ec"}},{"node":{"excerpt":"","fields":{"slug":"/blog/is-your-vendor-solving-the-problem-or-becoming-one-the-end-of-body-shopping/"},"frontmatter":{"title":"Is your vendor solving the problem or becoming one? – The end of body shopping","order":null,"content":[{"body":"## What body shopping is – and why it still dominates\n\nBody shopping is simple by design:\n\nA company has a headcount gap ⭢ a vendor fills it with a developer who matches a keyword list ⭢ the client pays per hour, per person. \n\nThis arrangement often means no shared accountability for outcomes, no ownership of the product. Also, many times – no motivation to optimize beyond getting through the backlog.\n\nIt worked for decades because it solved two very real problems simultaneously: talent scarcity and cost pressure. Western companies could reach nearshore and offshore talent pools, reduce hourly rates, and show immediate savings on paper.\n\nBut here's what the spreadsheet doesn't capture: **cheaper per hour is not the same as faster delivery.** \n\nWhen you pay for presence, you get presence – standups attended, tickets picked, hours logged. What you don't get is momentum. And the hidden cost shows up elsewhere: internal engineers spending their time on coordination, constant context transfer, fragmented ownership. The very team you were trying to unburden becomes the integration layer for external capacity. You haven't solved the problem. You've just moved it.\n\n## Why digital-native companies are walking away\n\nInsurtech, fintech, healthtech, SaaS scaleups – seemingly different industries sharing the same constraint. They can't beat the big players on budget or brand. So the only way they win is by moving faster. Shipping sooner, deciding quicker, fixing mistakes before they compound.\n\n**Body shopping breaks all three.**\n\nHere's how it usually goes. Someone new joins from the vendor. They're smart enough, but they don't know your system, your product, or why half the decisions were made the way they were. A few weeks pass. They start getting useful. Then something changes (the contract, the scope, the priorities) and they're gone. Whatever they learned goes with them.\n\nMeanwhile, your internal team has spent those weeks answering questions, reviewing code, and filling in the gaps. Body shopping doesn't reduce your workload but redistributes it in the most expensive way possible.\n\n## What \"elite squad\" actually means (when it's not just marketing)\n\nIn 2026,  everyone claims to have an elite squad. But most of the time it's merely a team augmentation with a rebrand.\n\nWhen it's genuine, a few things are different:\n\n**The team is small and fully senior** – two or three engineers who can each own a problem from architecture to deployment. No hidden juniors, no overhead layer. The size is intentional: small enough to move fast and experienced enough not to need supervision.\n\n**AI is embedded in how they actually work**. The most meaningful part is context engineering: structuring code and documentation so AI outputs something production-ready rather than something that needs fixing. Most teams haven't figured this out yet. The ones that have move noticeably faster.\n\n**Senior engineers in this model help make the right calls** – architecture, trade-offs, what to build and what to leave out. AI handles volume, while humans handle judgment. That combination is what makes the output good, not just fast.\n\nAnd the team acts like it has skin in the game, because structurally it does. When you're measured on outcomes rather than hours, you behave like an owner. You challenge bad decisions, flag problems early, and care what happens after deployment.\n\n\n\n## Why onboarding is faster than you've been told\n\nThe classic argument for body shopping has always been: *\"at least I can have someone next week\"* with the assumption that a better option takes longer to set up. **That's not really true anymore.**\n\nA senior squad with the right tooling and workflow can be fully productive **within two to four weeks**. Senior engineers ramp up faster because they ask better questions. AI helps them navigate an unfamiliar codebase quickly. And small teams get aligned fast because there's just less to coordinate.\n\nIn practice: \n\n**The first week is about understanding** – architecture, product logic, business context, what \"done\" actually means here. \n\n**The second week is paired work** – small contributions, feedback loops, building trust in both directions. \n\n**By weeks three and four**, the squad is carrying real ownership, closing meaningful work, and requiring minimal oversight.\n\n## The uncomfortable question about pricing\n\nIf one engineer working with agentic AI can now produce what previously required three – why is the pricing model still built around hours?\n\nThis is where the industry is catching up slowly and unevenly. The old logic ties rates to time. The new reality is that value created per unit of time has shifted dramatically. Forward-thinking companies are already moving toward **outcome-oriented thinking**: smaller teams, higher leverage, measuring cost per feature rather than cost per hour.\n\nThe vendors who survive this shift will be the ones who can explain how their productivity model translates into real business value – not just faster code generation, but faster learning, faster iteration, and better decisions compounded over time.\n\n## The shift is already happening\n\nBody shopping won't disappear overnight. Too much is built around it – procurement processes, vendor lists, budget structures that count heads rather than measure outcomes. Those things changes slowly.\n\nBut the direction is clear. The engineering leaders who are ahead of this have already made the shift: smaller teams, more accountability, less overhead. They're not asking \"how many developers can we add?\" They're asking \"how much can the right two or three people actually deliver?\"\n\nThose are very different questions. And they lead to very different partnerships.\n\n## This is the model we've been building toward at Boldare\n\nBoldare is not a staffing agency. We don't have a bench of available developers waiting to fill your headcount gap.\n\nWhat we do have is a model built around small, senior squads, AI-native workflows, and genuine accountability for what ships. We've been building this way long enough to have seen the pattern hold across different products, different industries, different team sizes: fewer people with the right setup consistently beat more people without it.\n\nIf you're a CTO ready to try a different approach, we'd like to show you what it looks like.\n\n[Let's talk.](https://www.boldare.com/contact/)"}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1775137177/body_shopping_zmj0sf.png","lead":"The engineering leaders scaling fastest right now are running smaller teams than they did three years ago. Not because they cut headcount under pressure but because they made a deliberate choice. Fewer engineers, higher leverage, tighter integration. And the results are hard to argue with.\n\nRead this article to understand what's driving that shift, why the traditional body-shopping *(or body leasing)* model is structurally incompatible with how modern software gets built, and what a high-performance nearshore squad looks like when it's done right.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-04-02T13:46:07.790Z","slug":"body-shopping-is-over","type":"blog","slugType":null,"category":null,"additionalCategories":["Strategy","Future"],"url":null},"author":"Aleksander Dąbrowski","authorAdditional":"","box":{"content":{"title":"Is your vendor solving the problem or becoming one? – The end of body shopping","tileDescription":"More developers doesn't mean faster delivery. Here's why CTOs are switching to smaller, AI-native squads — and how quickly they can get started.","coverImage":""},"coverImage":null}},"id":"6dc331cc-91ae-53e7-a006-b5fdb091ea94"}},{"node":{"excerpt":"","fields":{"slug":"/blog/this-week-s-ai-bite-multi-agent-workflow-in-claude-code/"},"frontmatter":{"title":"This week’s AI Bite: Multi-agent workflow in Claude Code","order":null,"content":[{"body":"## Background\n\nI had an app that technically worked, but after a few manual tests I decided I wanted a completely different UX and architecture. I prepared a new product vision, a set of required changes, and a technical plan with stages. Then instead of working through it alone — I split the work across agents.\n\nOne important thing: I didn't read any documentation on how to do this. I simply asked the agent itself — \"can you work in parallel on different branches?\" — and it explained the possibilities, proposed a workflow, and organized the entire structure on its own.\n\n## How it worked in practice\n\nThe core idea: instead of one long context — multiple agents, each with a fresh window and its own isolated branch.\n\n```\nmain (API contract updated FIRST)\n │\n ├── Agent 1 (worktree, in parallel)\n │ Backend: new fields, DB migration, integration tests\n │\n ├── Agent 2 (worktree, in parallel)\n │ New AI prompt + new types (independent of Agent 1)\n │\n └── Agent 3 (after Agent 1 completes)\n New endpoint (branched from Agent 1's branch — due to dependency!)\n\n```\n\nEach agent gets isolation via a worktree → Claude Code automatically creates a temporary worktree, the agent works on a separate branch. It then automatically merges into a test branch, runs tests, and I verify through the UI.\n\nIn later stages (independent frontend + backend) I managed to run 3 agents simultaneously — there were no dependencies between them.\n\nModels: Opus for planning and dependency analysis, Sonnet for implementation (faster, cheaper, good enough for coding).\n\n## Synchronous vs. asynchronous agent mode\n\nThere's also an option to launch an agent with run_in_background: true — the agent runs in the background and you get a notification when it's done, instead of waiting in place. In theory you can do something else in the main conversation while agents are working.\n\nIn my case I deliberately didn't use this — agents ran synchronously, because each phase (merge, test verification, decision on next step) required my review before launching the next ones. With this kind of flow, the \"run → wait → evaluate → proceed\" sequence made more sense than \"fire in the background and check when done.\" I will be testing run_in_background in scenarios where agents are truly independent and don't block each other.\n\n## Advantages\n\n* Real parallelism — you wait for the slowest agent, not the sum of all times\n* Context isolation — each agent starts fresh, doesn't \"pollute\" the main conversation\n* Model selection per agent — Opus for thinking, Sonnet for doing\n* Safety — nothing reaches main without your approval, test branch for verification\n* Agents write tests — each agent gets an instruction to verify its own work\n\n## Limitations\n\n* Agents don't know about each other — you have to manually manage dependencies and ordering\n* Dependency ordering is critical — if Agent B needs the output of Agent A, you can't run them in parallel. Dependency analysis before starting is mandatory\n* No real-time visibility — you see results only when the agent finishes (noticeable for 12+ min operations)\n* Prompts must be very precise — the agent doesn't have your conversation context. Vague prompt = wrong implementation\n* Merge conflicts — if two agents touched the same file, you have to resolve manually\n\n## What can be configured better (plan, not all verified)\n\n* CLAUDE.md with a parallel work section — so the agent knows upfront which files not to touch when working alongside others.\n* A dedicated /parallel-analyze skill — a skill that reads the technical plan itself, analyzes dependencies, and proposes how to split work across agents. Currently I do this manually in conversation with the agent.\n* Agent Teams (experimental feature) — agents can communicate with each other and share a common task list, which could eliminate manual dependency management entirely."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1775039921/Group_1000005103_aw1tni.png","lead":"**Weekly AI Bites** is a series that gives you a direct look into our day-to-day AI work. Every post shares insights, experiments, and experiences straight from our team’s meetings and Slack, highlighting what models we’re testing, which challenges we’re tackling, and what’s really working in real products. If you want to know what’s buzzing in AI, check Boldare’s channels every Monday for the latest bite.\n\n**As a Software Engineer, I wanted to share something I've been testing recently — running multiple Claude Code agents in parallel on separate worktrees. This is a practical real-world use case.**","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-04-01T10:34:50.823Z","slug":"this-weeks-ai-bite-multi-agent-workflow-in-claude-code","type":"blog","slugType":null,"category":null,"additionalCategories":["Ideas","Tech","GenAI"],"url":null},"author":"Kamil Tomczyk","authorAdditional":"","box":{"content":{"title":"This week’s AI Bite: Multi-agent workflow in Claude Code","tileDescription":"Explore how multi-agent workflows work in Claude Code. Learn how AI agents collaborate, automate tasks, and boost developer productivity in this week’s AI Bite.","coverImage":""},"coverImage":null}},"id":"43f0246e-f495-5c3e-8435-539a4e49003f"}},{"node":{"excerpt":"","fields":{"slug":"/blog/context-engineering-the-skill-any-ai-tool-becomes-useless-without/"},"frontmatter":{"title":"Context engineering: The skill any AI tool becomes useless without","order":null,"content":[{"body":"## What context engineering actually is\n\n**Prompt engineering = how you talk to the model.** \n\n**Context engineering = what the model knows before you say a word.**\n\nContext engineering is the practice of designing and managing everything a model has access to at the moment it generates code. \n\n**This includes:**\n\n* what memory it holds, \n* which files it sees, \n* what architectural rules are pre-loaded as hard constraints,  \n* what external data gets pulled in on demand – documentation, vector databases, codebase indexes.\n\nThink of it as a meta-layer above prompts. Instead of writing increasingly clever prompt macros, you design the information pipeline – the code index, the filtering rules, the domain tags, the long-term memory, the project profile.\n\n**The simplest way to put it:** you're not teaching the model to write better. You're curating what it knows so it can't write badly.\n\n## Why AI tools can break your architecture without context\n\nThe same model, the same codebase, the same developer can produce brilliant code one day and architectural chaos the next. The differentiator is the state of the context.\n\nHere's what's happening technically:\n\n### The lost-in-the-middle effect. \n\nLarge language models (LLMs) don't read context linearly – they weight it unevenly. With a bloated context window, critical architectural details buried in the middle (e.g. your bounded context definitions, your integration contracts, your naming conventions) get systematically under-weighted. The model technically \"saw\" them but didn't prioritize them.\n\n### Context as noise, not signal. \n\nWithout deliberate curation, what a model receives is a jumble: half the chat history, whatever files the IDE happened to grab, fragments of documentation. This is not a representation of your system's architecture. It's an **information** **landfill**. The model calculates whatever it can pattern-match – and those patterns are often from its training data, not your codebase.\n\n## What tools like Cursor and Claude Code solve on their own – and what they don't\n\nCursor auto-indexes repositories, chunks code, generates embeddings, and supports @-references to files. That's real infrastructure. But the quality of what comes out still depends entirely on how you've organized your repository, your documentation, your module boundaries, and your naming. The tool handles the mechanics. **You have to handle the meaning.**\n\nClaude Code explicitly recommends **aggressive context management** - frequent /clear commands, deliberate file inclusion, vector database integrations – because a polluted, sprawling context degrades output quality measurably.\n\n**The vibe coding trap.** [Thoughtworks](https://www.thoughtworks.com) framed this well in 2025: there's a shift underway from vibe coding (throwing a model at a repository and trusting it to figure things out) to deliberate context engineering, where you design what the AI knows and in what form. The former is exciting and fast. The latter is what makes the code actually shippable.\n\n**The concrete symptoms of bad context management look like this:** \n\n* AI that generates controllers calling repositories from a different bounded context, \n* AI that creates duplicate DTOs because the existing ones are buried in a module it didn't properly index, \n* AI that builds REST endpoints in a system where inter-service communication should flow through events. \n* AI that your senior engineers spend more time correcting than writing themselves.\n\n## How Boldare approaches codebase indexing\n\nWhen we onboard a new project, we don't install a plugin and start chatting. We build a four-layer context architecture before any AI touches production code.\n\n### Layer 1: The architectural contract\n\nBefore the AI sees a single line of code, we define the constraints it must operate within: \n\n* bounded contexts, \n* module boundaries, \n* architectural style (hexagonal, modular monolith, event-driven), \n* integration rules, \n* communication patterns between services.\n\nThese become short, AI-readable rule documents – files that describe what good code looks like on this project, what's explicitly prohibited, with concrete examples of both. Architecture Decision Records (ADRs) are formatted to be retrieval-friendly. These are always pulled as top-priority context – the guardrails that override everything else the model might infer from patterns.\n\n### Layer 2: Codebase indexing\n\nThis is more complex than \"scan the folder.\" A modern indexing pipeline for a large codebase looks like this:\n\n**Semantic chunking.** We use parser-level tools (Tree-sitter and equivalents) to break files into logical units (functions, classes, modules), rather than arbitrary character-count blocks. A chunk containing one complete function with its docstring retrieves far better than a chunk that starts halfway through one function and ends halfway through another.\n\n**Embeddings with enriched metadata.** Each chunk gets embedded and stored in a vector database (Pinecone, Weaviate, or Chroma depending on the project). We enrich chunks with domain tags (billing, onboarding, authentication), module names, and links to related ADRs and test files. This dramatically improves retrieval precision.\n\n**Scope configuration**. We explicitly define what goes into the index. Generated artifacts, node_modules, build outputs, and legacy dead code are excluded. The index represents the living system, not its debris.\n\n**Delta updates**. When a file changes, only the affected chunks are re-embedded. This keeps the index current without the cost of full re-indexing – which matters at scale where a full run is expensive.\n\n**Access** **governance**. In multi-team projects, we increasingly segment indexes by team and service boundary (both for cost control and compliance). An agent working on the payments module doesn't need (and shouldn't have) full-text retrieval over the user identity module.\n\n### Layer 3: Task context assembly\n\nWhen a developer formulates a task: *\"add a subscription payment endpoint\"* – they're not dropping it into a raw chat window. A pipeline assembles the relevant context package:\n\n* The architectural contract rules pertaining to the payments domain\n* The related bounded context files and module interfaces\n* The relevant existing code and its tests\n* Any ADRs touching payment processing decisions\n\nClaude Code or Cursor receives this curated package, not the entire monolith. The model isn't guessing which conventions apply. They're given to it explicitly, prioritized correctly, trimmed to what's relevant. Boundaries get respected because the model is never given the opportunity to violate them without noticing.\n\n### Layer 4: Feedback loop and context evolution\n\nArchitecture changes, so the context system has to evolve with it. When a significant refactor happens, the affected ADRs are updated, domain tags are revised, and if necessary, new guardrail rules are added – for example: *\"this dependency is now deprecated, suggest the new pattern instead\".*\n\nWe also monitor for failure modes. If code review starts catching repeated boundary violations in AI-assisted PRs – say, the application layer repeatedly reaching directly into infrastructure – that's a signal to inspect the context structure, not to blame the model. Usually it means a gap in the architectural contract documentation, or a retrieval issue where the relevant constraint isn't surfacing reliably.\n\n## The deeper shift this represents\n\nContext engineering is not a feature you configure once. It's a discipline closer to information architecture than to prompt writing. It asks engineering teams to think carefully about how their codebase's knowledge is structured, tagged, and made retrievable.\n\nThe teams who've figured this out don't talk about AI as unpredictable or unreliable. They talk about it the way they talk about a well-onboarded junior engineer: one who knows the codebase, knows the rules, and asks the right questions when uncertain.\n\nThe teams who haven't yet built this layer talk about AI as something between \"impressive demo\" and \"expensive liability.\"\n\nIt turns out the question separating those two groups isn't which model you use, or how good your prompts are. It's whether you've built the infrastructure to give the model something worth knowing.\n\n- - -\n\nBoldare builds software products and helps companies navigate AI-assisted development at scale. \n\n**If you want to discuss how context engineering applies to your architecture,** [let’s talk! ](https://www.boldare.com/contact/)"}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1774901341/context_engineering_wnnspz.png","lead":"Every engineering vendor you'll speak to this year is AI-native. Everyone uses Cursor. They've all tried Claude Code. They'll all show you the same demo of code generating in seconds.\n\nThen you ask one specific question – how do you manage context engineering to ensure AI-generated code aligns with your architectural standards? And the room goes quiet.\n\nThat question is a neat filter. And right now, it allows you to separate the vendors who use AI as a party trick from the ones who've actually rebuilt how software gets made.\n\nIf you don't know what context engineering is, your AI tools are working against your architecture. Here's what it actually means – and how to tell whether your team (or your partner) has figured it out.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-03-30T12:54:44.246Z","slug":"context-engineering-ai-development","type":"blog","slugType":null,"category":null,"additionalCategories":["GenAI","How to"],"url":null},"author":"Aleksander Dąbrowski","authorAdditional":"","box":{"content":{"title":"Context engineering: The skill any AI tool becomes useless without","tileDescription":"Without context engineering, AI tools generate architectural chaos. Learn what it is, why it matters, and how to get it right.","coverImage":""},"coverImage":null}},"id":"cd8c7bf3-32a7-5a88-b438-7b8c332eaff6"}},{"node":{"excerpt":"","fields":{"slug":"/blog/claude-code-vs-cursor-which-ai-tool-actually-fits-enterprise-reality/"},"frontmatter":{"title":"Claude Code vs Cursor: Which AI tool actually fits enterprise reality?","order":null,"content":[{"body":"## Cursor explained\n\nCursor is a fork of VS Code with AI built directly into the editing experience. It offers multi-line autocomplete, inline chat, agent modes, and codebase indexing – all within a familiar GUI that most developers already know. Its main strength is speed: Cursor reduces the friction of day-to-day coding by keeping suggestions close to where the work happens.\n\nFor enterprise teams, Cursor's biggest selling point is **low adoption resistance**. Because it looks and feels like VS Code, developers can start using it **without changing their habits**. It supports multiple AI models (including [ChatGPT](https://chatgpt.com/), [Claude Sonnet](http://claude.ai/), and [Gemini](https://gemini.google.com/)), which gives organizations some flexibility in how they manage model costs and preferences. Enterprise pricing is custom and includes advanced access controls and SCIM *(System for Cross-Domain Identity Management)* support.\n\nWhere Cursor starts to show limits is in the depth of its reasoning. Its effective context window, while advertised up to 200k tokens, often compresses only 70–120k in practice under load. For large backend systems with deeply interconnected services, this variability can affect reliability. Cursor is also less suited to automated, terminal-driven workflows – it is built for interactive editing, not for wiring into CI/CD pipelines or operating as a governed agent.\n\n## Claude Code as a reasoning engine for systems\n\nClaude Code is designed differently. It is terminal-first and agentic, meaning it does not just suggest – it plans, edits across multiple files, runs commands, and integrates with GitHub, CI pipelines, and MCP tools. Its context window is reliably **large** *(200k tokens, extendable to 500k+ on enterprise plans)*, which matters when the system you are reasoning about spans dozens of services and years of commits.\n\nFor engineering managers, the most important distinction is this: \n\n> Claude Code is less about making individual developers type faster and more about giving teams the ability to understand and safely change complex systems. \n\nIt can trace a business rule across a codebase, explain why a particular abstraction exists, or map the risk surface of a proposed refactor. That kind of reasoning is not available in suggestion-driven tools.\n\nClaude Code's enterprise tier includes SSO, RBAC, audit logs, SCIM, a Compliance API, and an Analytics API – making it **easier** to satisfy security and legal requirements at scale. Its permission architecture defaults to read-only, requiring explicit approval for file edits and shell commands, which limits blast radius in production-adjacent environments.\n\nThe trade-off is that Claude Code requires more **intentional** rollout. Getting full value from its CI hooks, MCP integrations, and compliance tooling means investing platform engineering time upfront. Teams that treat it as a drop-in replacement for an IDE assistant will underuse it.\n\n## Why codebase size changes everything for AI coding tools\n\nThe gap between the two tools becomes most visible as systems grow. Cursor handles local, well-scoped tasks efficiently. It struggles when a change touches many services, when the codebase carries significant historical debt, or when understanding the system matters more than producing output quickly.\n\nClaude Code is better suited for that level of complexity. It can follow data flows across services, surface undocumented dependencies, and reason about changes that span multiple subsystems. For CTOs managing large backend systems, this kind of system-level understanding often delivers more value than faster autocomplete.\n\n## How Claude Code and Cursor handle compliance differently\n\nFor most enterprise teams, the security review is the gate that determines whether a tool gets deployed at scale or stays limited to individual developers running it locally. Both Claude Code and Cursor have enterprise offerings, but their approaches to governance reflect different assumptions about who controls what.\n\nCursor’s enterprise controls are competent for an IDE-centric tool. It offers SCIM, access controls, and custom pricing that factors in seat count and security requirements. For teams that primarily need centralized licensing and some usage visibility, this is often sufficient.\n\nClaude Code’s governance story goes deeper. Its Compliance API gives security teams programmatic access to usage data for monitoring and audit. Its Analytics API surfaces how the tool is being used across the organization. Combined with SCIM, SSO, RBAC, and audit logs, this creates the kind of oversight trail that **regulated** **industries** – financial services, healthcare, government-adjacent software – typically require before approving a new tool at scale.\n\nThe permission model also matters. Claude Code defaults to read-only and requires explicit approval before writing files or running shell commands. In environments where the AI is operating close to production systems, that architecture limits the blast radius of a misfire. Cursor, as an IDE tool, does not operate in the same way – the developer is always in the loop by design, which is a different but valid approach to risk management.\n\nNeither model is wrong. They reflect different assumptions about where the AI sits in the workflow. The right choice depends on whether your governance requirements need to be built into the tool or built around it.\n\n## When to use Claude Code and when to use Cursor – Decision Matrix\n\nThe question is not which tool is better. It is which tool fits which kind of work. Enterprise backend development is not a single activity – it is a spectrum from fast incremental changes to high-stakes architectural decisions, and the right support looks different at each end.\n\n
GoalToolWhy
Write code faster day-to-dayCursorSuggestion-driven, low friction, familiar IDE environment
Understand a complex or legacy systemClaude CodeDeep context, git history, architecture-level reasoning
Onboard junior developersCursorStandard patterns, safe defaults, fast feedback loop
Support senior engineers on hard problemsClaude CodeReasoning partner for design decisions and refactoring risk
Compliance and regulated environmentsClaude CodeCompliance API, audit logs, SCIM, RBAC, explicit permissions
Large undocumented codebasesClaude CodeRepo-wide analysis, pattern extraction, commit history context
Daily interactive editingCursorIDE ergonomics, plugin ecosystem, multi-model flexibility
CI/CD and automated workflowsClaude CodeTerminal-first, GitHub Actions integration, agentic execution
\n\n## Why the best engineering teams don't pick just one\n\nIn practice, the most effective enterprise setups do not pick one tool and standardize on it everywhere. They assign tools to layers of the development process.\n\nCursor handles the high-frequency, lower-risk work: writing new features in well-understood areas, generating tests, making incremental improvements to clean code. It stays in the editor, close to the developer, keeping feedback loops short.\n\nClaude Code operates at a different level. It is the tool you reach for when you need to understand something before changing it – when a refactor spans multiple services, when someone asks where a business rule is actually enforced, or when a schema migration needs to be validated against a system no one has fully mapped. It is also the tool that belongs in CI pipelines and secured terminals for automated analysis and code review, away from the day-to-day editing flow.\n\nThe separation is intentional. High-frequency work benefits from low friction. High-stakes work benefits from deeper reasoning. Conflating the two, and expecting one tool to do both well, usually means getting a mediocre version of each.\n\n\n\n## What we learned deploying Claude Code in enterprise backend teams\n\nAt Boldare, we work with enterprise backend teams at the point where these decisions get complicated – systems with real history, teams under delivery pressure, and technical debt that accumulated before AI tools existed.\n\nOur experience is that the tooling decision is rarely the hard part. The harder part is designing the process around it: where does the AI’s output get reviewed, who owns the decision when the tool suggests something that technically works but architecturally doesn’t fit, and how do you preserve system knowledge when the tool is doing more of the synthesis.\n\nWe use Claude Code for the work that requires genuine system understanding – [legacy analysis](https://www.boldare.com/blog/application-modernization-2026-ai-legacy-migration-cto-guide/), refactoring support, architectural reasoning, and high-risk changes where shallow context is expensive. We pair it with verification processes that keep engineers in control of decisions, not just execution.\n\nMost teams we talk to have the same concern: they can see the productivity case for AI tooling, but they can't yet see how to deploy it without accumulating invisible risk.\n\nIf that's where you are, the 30-minute conversation is the right starting point.\n\n**→ Book your strategy session** [here](https://www.boldare.com/services/claude-code-consultation/)"}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1774527423/Frame_2087325362_vvq2o7.png","lead":"If you manage engineering teams, you have probably already heard both names more than once. [Claude Code](https://claude.com/product/claude-code) and [Cursor](https://cursor.com/) are two of the most talked-about AI coding tools right now, and for good reason – both are genuinely capable. But the conversation around them often skips the part that matters most for engineering leaders: they are not solving the same problem, and deploying the wrong one in the wrong context creates friction, instead of value.\n\nThis article breaks down what actually separates Claude Code and Cursor, where each belongs in an enterprise backend setup, and how to think about the decision without getting lost in feature checklists.","templateKey":"article-page","specialArticle":true,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-03-26T12:31:16.069Z","slug":"claude-code-vs-cursor-enterprise","type":"blog","slugType":null,"category":null,"additionalCategories":["GenAI","How to"],"url":null},"author":"Aleksander Dąbrowski","authorAdditional":"","box":{"content":{"title":"Claude Code vs Cursor: Which AI tool actually fits enterprise reality?","tileDescription":"Comparing Claude Code vs Cursor for enterprise teams? This guide covers context limits, governance, legacy systems, and when to use both. ","coverImage":""},"coverImage":null}},"id":"de088fce-ce35-5d02-acd6-76aefaebc40a"}},{"node":{"excerpt":"","fields":{"slug":"/blog/spec-first-engineering-for-mission-critical-systems-with-claude-code-insights-from-jakub-walczak/"},"frontmatter":{"title":"Spec-First Engineering: for Mission
-Critical Systems with Claude Code insights from Jakub Walczak","order":null,"content":[{"body":"**Piotr:** Jakub, we keep seeing the same problem across almost every engineering team we work with. AI generates code fast, but a lot of it doesn't hold up in production. You've been experimenting with something called spec-driven development. What is it exactly?\n\n**Jakub:** Right, so like all trending terms, especially in the AI space — it doesn't have one established definition yet. But the way I think about it: before writing any single line of code, you have to prepare specifications. Instead of throwing vague prompts at the AI, you need a structured document covering all the edge cases, all the business requirements, all the expected behaviors. That's what gives you more deterministic, reliable results on the other end.\n\n**Piotr:** So it's basically an expansion of the \"spec first\" idea, the upfront work needs to be really well defined before anything gets built.\n\n**Jakub:** Exactly. The entire shift in spec-driven development is moving the focus away from source code – which has become the final product of the workflow for most developers – and putting it on the specification itself. As engineers, our job becomes orchestrating that definition process in order to get satisfying results from the AI.\n\n**Piotr:** What actually goes into a spec? You mentioned edge cases and business requirements, what else?\n\n**Jakub:** Honestly, for me a spec is everything that could matter while working on a particular feature. So definitely the expected behavior at the business level. Edge cases and known limitations. Diagrams of the business flow. Mermaid format works really well because AI consumes it nicely. You could even add links to existing source code, though I'm not sure that's always a good idea.\n\nThe key principle is that a well-written spec should give you a deterministic output. Of course results will vary slightly from one run to another, but if your spec is solid, you should consistently reach a satisfying result. And at that level, the technical details. Java, Python, whatever — become secondary. The spec is focused on business value and behavior, not on implementation.\n\n**Piotr:** So where does Claude Code fit into this workflow?\n\nJakub: You can actually use AI to help you write the spec in the first place. The first step of the workflow is combining all your sources, things written down, things you remember from months ago, tribal knowledge that lives nowhere. You feed all of that to an agent and it helps you craft a more detailed, more complete specification. That's what drives better results downstream.\n\nA few weeks ago I had a good example of this. I had to implement a feature in our invoicing domain – an area I hadn't touched in months. The ticket itself was relatively simple, maybe 30 minutes of implementation, another hour of thorough testing. But instead of jumping straight in, I decided to build what I called an \"Invoicing Expert\" Claude skill – essentially a deep knowledge artifact about our invoicing domain. That took me two, three, maybe four hours to build properly.\n\n**Piotr:** This isn't a simple invoice with one line item, right? Not \"give me an iPhone invoice.\"\n\n**Jakub:** Not at all. Our invoicing has a lot of moving parts, different margin sources, different document types, a lot of business rules. So I invested those hours upfront building the skill, used it to implement the feature, and everything went smoothly.\n\nThen, two days later, we got a complaint from a customer. Not exactly a bug report – more like \"this isn't working the way we expected, there must be some discrepancy between the intended behavior and what's actually in the code.\" Because I had built that skill two days earlier, I was able to use it immediately to find the issue, fix it, and test the fix in about 10 minutes. The hours I spent upfront paid back almost instantly.\n\n**Piotr:** So the skill itself essentially became the spec for that feature?\n\n**Jakub:** That's how I think about it, yes. The skill captured the business requirements and the domain knowledge in a structured way. At minimum it's part of the process of creating a specification. Either way, it gave the AI and me, a shared, reliable understanding of what we were working with.\n\n**Piotr:** Let's talk about the tension a lot of teams feel here: spending more time on specs versus spending more time on execution. Is spec-driven development a form of over-engineering?\n\n**Jakub:** That's a fair question. But using AI agents to help with source code doesn't automatically mean you're going twice as fast – it doesn't work like that. The delivery time on individual features is a bit shorter, yes. But we're now spending more time crafting specifications than we used to spend writing loops in the source code.\n\nWhat that time buys you is real thinking space. You can review all the requirements, all the edge cases, all the business value of a feature before you've written a single line. And in that process you often catch things, unclear requirements, missing pieces, outright contradictions – before they become expensive problems in production. Developers love jumping straight into implementation. But in the AI era, that instinct needs some recalibration.\n\n**Piotr:** Especially in mission-critical systems where you simply can't afford mistakes. And there's another benefit you touched on – the spec makes the work transferable. If you weren't available, a colleague who hadn't worked on that invoicing feature could pick up your spec and still be able to fix a bug.\n\n**Jakub:** Exactly. That knowledge is no longer locked in one person's head. It's documented, structured, and reusable.\n\n**Piotr:** Okay, but nothing is all upside. What are the pitfalls?\n\n**Jakub:** I can think of two or three. The first one: to get deterministic results over time, you need to maintain your artifacts. A skill or spec that's heavily tied to specific lines of code will go stale quickly as the codebase evolves. Business rules don't change that often, code does. So specs should be written at a general, behavior-focused level, not tied to implementation details. That way you can use the same spec today or two months from now.\n\nThe second thing – and this is a real example from our team. We inherited a project from another team and had to start delivering features immediately. I used spec-driven development, worked with AI to generate the code, reviewed it carefully, and thought it looked solid. It fulfilled all the requirements I was aware of.\n\nThen I got the code review back from a colleague who had actually been in workshops with the client – someone with deep contextual knowledge that wasn't written down anywhere. Out of around 40 modified files, I got 25 review comments. Some were minor – rename this variable – but several were genuinely serious. With the assumptions I had made, the code could have caused real harm in a critical part of our system.\n\n**Piotr:** So spec-driven development is only as good as the spec – and the spec is only as good as the knowledge that goes into it. People are still essential.\n\n**Jakub:** Absolutely. Programming is a team sport. You still need people covering each other's backs. The AI doesn't know what it doesn't know – and neither do you, if you're missing the right context upfront.\n\n**Piotr:** There are also some emerging frameworks in this space, right?\n\n**Jakub:** Yes – SpecKit is one I've seen mentioned recently. It's still early but it's trying to standardize spec-driven development as a methodology. I haven't had a chance to dig into it yet, but it's on my list.\n\nThere's also an interesting framework I read about recently that distinguishes three levels of spec-driven development. The first is \"spec first\" – you write the spec, hand it to an AI agent, it generates code, you review and modify by hand. The second is \"spec in sync\" – you try to keep the spec and the source code aligned over time. I'm currently somewhere between those two. The third level is \"spec as source\" – you never touch the code directly at all. You only modify the spec and ask the agent to regenerate or fix the code accordingly. Because right now, code is cheap.\n\n**Piotr:** Let's close with something actionable. If someone watching wanted to start applying spec-first engineering in their team tomorrow – what's the single first step?\n\n**Jakub:** I know you want just one, so here it is: instead of throwing vague prompts at your AI agent, spend 30 minutes to an hour crafting a more detailed specification first. Write down the expected behavior, find the gaps, surface the uncertainties, identify the edge cases. Then pass that to the AI. The code you get back will be noticeably better.\n\n**Piotr:** Exactly. And the beauty of it is you can start experimenting right now. Sometimes you'll be positively surprised, sometimes not – but that's the game we're all playing. Jakub, thank you. I learned a lot today.\n\n**Jakub:** Thanks for having me. It was a pleasure.\n\n**If this got you thinking about your own system and engineering workflow, Boldare offers a free 30-minute consultation – no hype, just practical guidance grounded in real production experience. Drop us a line at business@boldare.com.**"}],"job":null,"photo":null,"slug":null,"cover":"","lead":"AI can generate code fast. That's impressive. What's actually impressive is when that code works in production, under load, at scale, without breaking three sprints later.\n\nHere's the uncomfortable truth: most AI-generated code fails – not because of the AI itself, but because of vague requirements, missing edge cases, and no shared understanding of what \"done\" actually means. **The real question isn't how to get AI to write better code. It's: what do we need to define before AI writes anything at all?**\n\nTo answer that, Piotr sat down with Jakub Walczak, a senior software engineer at Boldare who has been deep in the trenches of building systems where failure is simply not an option – and who has been applying spec-driven development with Claude Code as a core part of his daily workflow.\n\n","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-03-25T12:37:26.165Z","slug":"spec-first-engineering-mission-critical-systems-claude-code-jakub-walczak","type":"blog","slugType":null,"category":null,"additionalCategories":["How to"],"url":null},"author":"Roksana Kaczmarska","authorAdditional":"","box":{"content":{"title":"Spec-First Engineering: for Mission
-Critical Systems with Claude Code insights from Jakub Walczak","tileDescription":"Learn how spec-first engineering improves AI-generated code quality in mission-critical systems. Practical insights from Jakub Walczak on using Claude Code, defining specs, and avoiding production failures.","coverImage":"https://res.cloudinary.com/de4rvmslk/image/upload/v1774442320/Group_1000005443_bteajy.png"},"coverImage":null}},"id":"ed6b29cc-59f6-5a8e-aa2f-26ce134ced1d"}},{"node":{"excerpt":"","fields":{"slug":"/blog/ai-washing-is-real-so-is-the-shift-lets-be-he-honest-about-both/"},"frontmatter":{"title":"AI washing is real. So is the shift. Let's be honest about both.","order":null,"content":[{"body":"## What AI washing actually is – and isn't\n\nAI washing rarely looks like a blatant lie. More often, it's a matter of emphasis. A company deploys a chatbot and characterizes its entire customer strategy as \"AI-powered.\" A cost-cutting initiative gets relabeled as an \"AI transformation.\" An organization speaks confidently about autonomous systems while human employees quietly manage quality control and risk behind the scenes.\n\nYou don't need to say anything technically false to engage in washing. You just need to spotlight the most favorable slice of reality and omit the rest.\n\nThe data supports this. Research suggests that roughly 40% of European AI startups in 2019 used virtually no AI whatsoever. A study from RWTH Aachen found that 78% of organizations report their AI purchases fall short of promised capabilities. Regulators are beginning to respond: in 2024, the SEC fined two investment advisory firms – Delphia and Global Predictions – $400,000 for misrepresenting their AI capabilities. These were the first penalties of their kind, and almost certainly not the last.\n\nThe form of AI washing I find most troubling, however, is when \"AI\" is invoked as a moral cover for decisions that are fundamentally human. AI doesn't lay people off. People do – boards, owners, executives. SAP's announcement of 10,000 job cuts in 2025, framed as a pivot to AI and cloud, illustrates the point: observers have noted that the pace of layoffs is outrunning the company's actual AI deployment. Technology may be reshaping the nature of work – and increasingly it is – but it isn't some neutral external force acting upon companies. Using it as a justification rather than a context obscures accountability in a way we should all challenge.\n\n## What I see from the inside\n\nI also need to say this clearly: the shift is real, and it's already happening.\n\nAt Boldare, our teams' ways of working have changed considerably over the past two years. Across nearly every team, AI agents now function as active contributors in daily operations – handling tasks that once consumed a full-time employee's time. Drafting, research support, code review, documentation, preliminary analysis. Real tools, embedded in real workflows, with measurable results.\n\nBecause of this, we will likely bring on fewer new employees going forward than we otherwise would have. I think that deserves to be said directly, without softening.\n\nBut that's not the complete picture. New types of work are emerging. New skills are gaining value. New roles are being built around capabilities that simply didn't exist three years ago. The honest answer is that this is complicated – and complexity calls for precise language, not a polished press release.\n\nWe also develop tools for clients that allow them to operate with leaner, more focused teams. So through the products we create, some positions will shift or disappear. I'm not comfortable portraying this as purely good news, and I'm equally uncomfortable calling it a catastrophe. Both framings sidestep the harder work of genuine thinking.\n\n## What honest AI adoption looks like in practice\n\nWhen we began integrating AI into our own teams, we didn't lead with talk of transformation. We started with a question: where are people spending time on work that doesn't actually require their judgment? That was our starting point – not because it was exciting, but because it was measurable. We could compare before and after. We could identify where a tool added value and where a human still needed to intervene.\n\nThat specificity is what I look for now when business leaders approach us for guidance.\n\nNot \"we want to be AI-driven.\" The more useful question is: what specific decision or workflow do you want to improve, and how will you know whether it worked? That question distinguishes implementations that build value over time from those that stall after the initial pilot. The Klarna example is instructive: their AI assistant produced genuine savings, but when customer satisfaction fell, they ended up rehiring much of the workforce they'd reduced. The narrative ran ahead of the technology, and the market responded accordingly.\n\nIn practice, moving forward thoughtfully requires three things.\n\n**Be specific about where AI is actually being used**. Not \"our platform is AI-enabled\" – but which component, doing what, replacing or supporting which process. Teams that understand exactly what the technology is doing are better equipped to use it effectively, identify failures early, and scale responsibly.\n\n**Be transparent about the human layer that remains**. In most implementations we build and use internally, there's still a person reviewing outputs, managing exceptions, and making judgment calls. That's not a shortcoming of the AI. It's sound system design. The organizations generating real value today are those who've identified the right handoff points between humans and AI – not those who've tried to remove people from the equation entirely.\n\n**Measure what genuinely changes**. Not sentiment or adoption figures – but the actual outcome you set out to improve. Faster turnaround? Fewer revision cycles? A smaller team needed to run a specific function? Choose one metric and track it. That evidence builds internal trust and justifies the next investment.\n\n## Where I land\n\nI'm genuinely skeptical of AI washing – not as a performance, but because I've witnessed what inflated claims do to trust, both internally and with clients. Once that trust erodes, it becomes much harder to build the organizational appetite for the real work ahead.\n\nBut I'm equally unwilling to dismiss the transformation that's underway. The labor market is shifting. The skills that matter are shifting. The tools woven into daily work are shifting. This is happening regardless of press releases and earnings calls.\n\nThe leaders I find most credible are the ones who can tell you precisely where AI works in their organization and where it doesn't – without hedging in either direction. That's the standard I hold myself to, and it's the standard worth holding each other to as well."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1774438259/AI-washin_xtbo32.png","lead":"As co-CEO of Boldare, I navigate two distinct conversations about AI regularly. One happens with clients trying to understand what AI means for their business. The other is internal: within a company that builds digital products and has been incorporating AI into its actual operations for the past two years. That dual vantage point is why I've been closely watching a pattern that's distorting both conversations simultaneously.\n\nThat pattern is called AI washing. And despite the coverage it's received, I think something important is still missing from the discussion.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-03-25T11:08:30.837Z","slug":"ai-washing-what-honest-ai-adoption-looks-like","type":"blog","slugType":"","category":null,"additionalCategories":["GenAI","Future"],"url":null},"author":"Anna Zarudzka","authorAdditional":"","box":{"content":{"title":"AI washing is real. So is the shift. Let's be he honest about both.","tileDescription":"A co-CEO's inside view on AI washing: what it really is, why it matters, and what genuine AI adoption looks like beyond the press releases.","coverImage":""},"coverImage":null}},"id":"20efced8-e21c-5371-b2ef-49609211cfbb"}},{"node":{"excerpt":"","fields":{"slug":"/blog/modality-as-a-design-decision-why-we-started-asking-about-it-earlier/"},"frontmatter":{"title":"Modality as a design decision – why we started asking about it earlier","order":null,"content":[{"body":"## What's changed\n\nNot long ago, interface modality was essentially given – you designed screens, flows, and components, and the user's input was obvious: keyboard, click, touch. Today a team can choose voice, text, image, video, documents, audio, or some combination, and that freedom is both an opportunity and a source of some fairly serious design mistakes.\n\nLook at what's been happening across the industry. Revolut deployed voice agents handling customer support in 30+ languages – voice replaced the traditional IVR tree because conversation is simply more natural than pressing numbers on a phone. Salesforce built Agentforce Contact Center, bringing together voice calls, CRM data, and AI agents in a single flow with real-time transcription. Headspace added Ebb, a voice-based mental health companion that listens to spoken emotions and remembers context across sessions – because voice carries emotional weight that text often can't.\n\nOn the other side, Lyft built the Cosmos vision-language platform to process live camera feeds for driver routing, Miro taught its AI Sidekicks to read the full visual context of a canvas before responding, and Google Stitch lets you speak to a design canvas, upload sketches, and describe the \"feeling\" of an interface – with the agent holding all of that context at once.\n\nIn each of these cases, modality wasn't a feature add-on or a \"because we can\" decision – it was the architecture of the product itself, shaped by what users are trying to accomplish and the conditions under which they're doing it.\n\nThree frameworks that help make sense of it\n\nWe didn't start from scratch here – we took tools we've been using for years and started asking an additional question about modality alongside them.\n\n### Jobs-to-be-Done: what is the user actually trying to do?\n\nJTBD asks what \"job\" the user is hiring this product to do – and that question leads surprisingly directly to modality, because different jobs happen in different physical and emotional contexts.\n\nIf the job is navigation while moving – driving, running, cycling – voice suggests itself naturally, which is exactly why Google Maps has been one of the most widely used voice interfaces for years: the product was being hired for a job that practically demanded voice. The new \"Ask Maps\" is a logical extension of the same idea: if users already trust voice for navigation, asking \"where can I charge my phone without waiting in line\" is just the next natural step.\n\nOtter.ai follows the same logic – the job is understanding what was said in a meeting and extracting value from it, and voice isn't just the natural modality here, it's the only one that makes sense, because meetings are inherently audio. So its agents transcribe, coach salespeople in real time, and take autonomous notes.\n\nIf the job is precise image editing at a desk, in focus, text beats voice – which is what Adobe did with Photoshop's AI assistant, where you can type \"remove the shadow on the left\" or \"add a soft glow\" and get the result without knowing any tool names or keyboard shortcuts.\n\n**What we do in the workshop:** we ask clients to describe the top three jobs users are hiring their product to do, and then we ask about the user's physical and emotional state when doing that job – and that answer very often naturally rules out or points to specific modalities before we've designed anything.\n\n### Opportunity Solution Tree: modality as a hypothesis, not an assumption\n\nTeresa Torres's OST teaches you not to fall in love with solutions before you understand the opportunity they're supposed to address – and the same applies to modality, because many teams decide \"we're adding voice\" and then look for the justification, rather than checking whether voice actually responds to a real user need.\n\nZoom identified an opportunity that could be described as: \"users want to communicate across language barriers without disrupting the natural flow of a conversation\", and the answer was a live voice translator doing real-time audio translation – because text wouldn't cut it here, the point is to preserve naturalness, not transcribe it.\n\nDoorDash went a different way and built DashCLIP, a model aligning product images, text descriptions, and search queries in a shared embedding space, because the opportunity was: \"users search for food intuitively and don't always know how to name what they want\" – and image plus text together answers that better than either modality alone.\n\nInstacart went further still, letting customers complete orders directly inside ChatGPT with AI analysing product images and nutritional data for dietary filtering – modality followed from a very specific opportunity: \"the user is mid-conversation with AI and wants to act immediately, not jump to another app\".\n\n**What we do in the workshop:** we add a \"modality hypothesis\" column to the solution tree next to each solution node, and for each one we ask whether the assumed modality is a real answer to that opportunity, or just a convenient or fashionable one.\n\n### AEIOU / Contextual Inquiry: modality lives in context, not in a lab\n\nAEIOU is a technique for observing users in their real environment and it's probably the best tool for validating modality – precisely because modality doesn't exist in the abstract, it exists in a specific place, at a specific time, in a specific user state.\n\nHeadspace designed Ebb with full awareness of this: a voice-based mental health companion is most valuable at 11pm on a Wednesday, when the user is alone in their bedroom and needs to process a difficult day, and that's a very different context from Monday morning before work – which is why Headspace lets users switch between voice and text at any moment, because they understand context shifts and the product needs to follow.\n\nGoogle Docs added audio summaries – Gemini generates a spoken summary of any document in a natural voice with adjustable speed and different narration styles, and the AEIOU context is very specific here: the user wants to absorb a document but has their eyes occupied – driving, exercising, cooking – so audio is the only modality that fits the activity.\n\nLattice's AI Meeting Agent took a similar approach: analysing meeting audio to surface turnover risk signals and team health patterns from the sound of the conversation itself – because the managerial context carries emotional weight that a text transcript alone would lose.\n\n**What we do in the workshop:** we add a modality dimension to the standard AEIOU grid, and for each observed activity we ask which modalities are natural in this context, which are physically impossible, and which would just feel invasive or uncomfortable.\n\n## Where the real power is: when modalities work together\n\nThe most interesting things happen not when one modality is chosen well, but when several work together and each compensates for what the others lack.\n\nGoogle Stitch is probably the best current example: a designer can upload a sketch, describe the interface's \"feeling\" in text, and say out loud what isn't working – all in one session, with the agent holding the full context simultaneously, and that's not just three inputs added together, it's a qualitatively different way of communicating complex creative ideas.\n\nReplit Agent 4 does the same on the development side: you paste a screenshot of a broken interface, describe in plain English what it should do, and speak corrections as the agent iterates in real time, seeing both the code and the rendered output – a feedback loop that used to require switching between several tools has collapsed into a single session.\n\nThis also changes how AI handles ambiguity: in a single-modality system a vague description produces a vague result, but when voice, image, and text work together each one fills in what the others are missing, and the output ends up much closer to what the user actually had in mind.\n\n## How this looks in practice for us\n\nModality Discovery in our Product Discovery Workshop is the stage where we work through these questions together with the client's team – before anything concrete gets designed. The recommendation we leave with covers which modality to introduce, in what order, how to potentially combine it with others, and what happens to the product if that modality fails for some reason.\n\nIt doesn't always lead to surprising conclusions – sometimes text is the right choice and there's no reason to complicate things – but asking these questions early, before investment is made, helps avoid the kind of situation the Pinterest story describes.\n\n- - -\n\n*Anna Zarudzka is CEO at Boldare, a company specialising in product discovery and building digital products for scale-ups and enterprises.*\n\n- - -\n\n## A few questions if you'd like to talk:\n\n* Is your team facing a decision about introducing a new modality and not sure how to think it through?\n* Are you considering voice, image, or video, but unclear whether the timing is right for your product?\n* How does modality discovery fit into your process – is it something you do explicitly, or more on the side?"}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1774435781/modality_v6gfwk.png","lead":"A few weeks ago I came across an article about an internal dispute at Pinterest – the CEO wanted to go all-in on voice, arguing that Gen Z expects something that feels like \"talking to a friend\", while the designers and product leaders pushed back because Pinterest is built around quiet, visual exploration and voice simply doesn't fit why people go there in the first place.\n\nI'm not bringing this up to take sides. I'm bringing it up because it captures a tension we're seeing more and more with our clients: the choice of interface modality has become one of the more consequential decisions in product design, and yet many teams make it late, almost in passing, or based on what's trending – rather than letting it follow from what users actually need and the context in which they operate.\n\nSince at Boldare we work alongside clients throughout the discovery process, these questions started coming up naturally in our workshops, and over time we decided to give them their own dedicated space so there's actually room to work through them properly.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-03-25T09:38:16.893Z","slug":"modality-as-a-design-decision","type":"blog","slugType":null,"category":null,"additionalCategories":["Future","Digital Product"],"url":null},"author":"Anna Zarudzka","authorAdditional":"","box":{"content":{"title":"Modality as a design decision – why we started asking about it earlier","tileDescription":"Voice, text, or image? Discover why modality is one of the most consequential design decisions - and why it should be made before anything gets built.","coverImage":""},"coverImage":null}},"id":"bfbf72e8-460f-504d-82eb-30c936e2d8b8"}},{"node":{"excerpt":"","fields":{"slug":"/blog/travel-app-trends-in-2026-the-complete-guide-for-product-leaders/"},"frontmatter":{"title":"Travel app trends in 2026: The complete guide for product leaders","order":null,"content":[{"body":"## What is the travel app market situation in 2026?\n\nTravel has fully rebounded from its pandemic disruption and is now in a phase of structural transformation. The shift isn't only about volume, but the traveler’s behavior and expectations.\n\nTravelers in 2026 are digitally fluent, research-heavy, and platform-agnostic. They mix sources, distrust single-channel information, and expect experiences that feel tailored rather than templated. At the same time, the underlying infrastructure of travel – routes, aircraft, regulations, distribution systems is changing itself.\n\nFor product teams, this creates both pressure and opportunity. The apps that win aren't necessarily the ones with the most features. They're the ones that understand how travelers actually make decisions (and build around that, not against it).\n\n## Nine trends defining travel apps in 2026\n\n### 1. AI-Powered Trip Planning Is the New Default\n\nAI has crossed the threshold from novelty to expectation in travel planning. The share of travelers using generative AI for trip planning **jumped from 11% to 18%** in a single year – that’s a **64% increase** (Amadeus, 2026). Conversational assistants, real-time itinerary generation, and context-aware recommendations are quickly becoming table stakes.\n\nBut there's a trust gap that product teams can't ignore: **25% of travelers have received inaccurate AI-generated information**, and only **46% fully trust AI systems** (Amadeus, 2026). The conclusion is clear: **plain AI capability isn't enough**. Transparency, fallback logic, and human oversight are what separate trusted products from ones that get abandoned.\n\nBoldare's AI-native development approach addresses this directly. Our teams build AI features with human oversight baked in, ensuring that personalization engines and booking flows remain reliable even as they scale. Tools like Claude Code and CursorAI run in our production workflows as measurable delivery components.\n\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n
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1
\n 		AI-Powered Trip Planning64% growthTrust gapHuman oversightTransparency\"BLDR
\n\n\n\n### 2. Travel mixology: The multi-source planning behavior\n\nTravelers no longer trust a single platform to tell them the whole story. They use LLMs for fast initial research, Reddit and YouTube for authentic social proof, and dedicated apps for booking. Amadeus calls this \"Travel Mixology\" – and it fundamentally changes how apps should position themselves.\n\nThe strategic takeaway: your app is one node in a broader decision ecosystem, not the sole authority. The smartest travel platforms in 2026 integrate external signals such as user-generated content, social validation, community data, rather than trying to compete with them. Apps that behave as closed silos lose context, and users who lose context, lose trust.\n\nBuilding this kind of open, integrated architecture requires both technical depth and product thinking. It's exactly the kind of challenge Boldare's cross-functional teams made of engineers, product designers, and strategists working together are structured to solve.\n\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n
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\n 		Travel MixologyMulti-sourceReddit + YouTubeSocial proofOpen ecosystems\"BLDR
\n\n\n\n### 3. Hyper-personalization through data and AI\n\nThe shift from generic recommendations to **behavioral** **personalization** is a market expectation at this point. Leading travel apps now adapt in real time, learn from implicit signals rather than just explicit searches, and anticipate user intent before it's expressed.\n\n**The evolution looks something like this:**\n\nfrom \n\n> \"Here are flights to Lisbon\" \n\nto \n\n> \"You seem due for a reset – here's a slow-travel coastal route with direct connections and low-season pricing.\" \n\nThat's a product paradigm shift – achieving it requires advanced data infrastructure, behavioral analytics, and AI/ML layers that integrate cleanly with booking systems. Boldare brings this to the table through a dedicated AI/ML stack – OpenAI, LangChain, LlamaIndex, RAG combined with deep experience in data-driven product optimization for travel clients.\n\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n
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🎯
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\n 		Hyper-PersonalizationReal-time adaptationBehavioral signalsIntent predictionML Infrastructure\"BLDR
\n\n\n\n### 4. Multimodal search and vibe-based discovery\n\nSearch in travel apps is no longer purely text-driven. **Image-based search** (\"find me destinations that look like this\"), video-to-itinerary conversion, and mood-based discovery are entering mainstream use. Research highlights tools that generate destination recommendations based on aesthetic or emotional states– that’s a fundamental rethink of what a search interface is.\n\nFor product teams, this means **inspiration and conversion are merging**. Content is an integral part of the booking funnel, not just marketing anymore. TikTok is today’s discovery engine and a dreamy photo is a new lead.\n\nBuilding multimodal interfaces demands modern frontend expertise and thoughtful UX design – these are areas where Boldare's teams have consistently delivered, with award-winning work recognized at the Webby Awards, Lovie Awards, and Awwwards.\n\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n
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\n 		Multimodal SearchImage searchVideo-to-itineraryVibe-basedTikTok discovery\"BLDR
\n\n\n\n### 5. VR and AR: “Try before you fly\" becomes literal\n\nVirtual reality is maturing from marketing gimmick into a legitimate product layer. Research indicates that VR enhances emotional connection, influences destination perception, and supports both pre-booking decision-making and post-booking engagement (SBS Journal of Applied Business Research, 2025).\n\nUse cases are spreading from immersive destination previews and virtual itinerary walkthroughs to hybrid physical-digital hotel experiences. For travel companies, this is both a differentiation opportunity and a development challenge as VR features require specialized expertise that most in-house teams don't have on standby.\n\nBoldare's full-cycle product development model means we can scope, design, and deliver these features without the coordination overhead of juggling multiple vendors.\n\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n
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🥽
\n 		\n \n \n
5
\n 		VR & AR ExperiencesTry before flyImmersive previewsVirtual walkthroughsEmotional connection\"BLDR
\n\n\n\n### 6. Niche segmentation: The pawprint economy and beyond\n\nMass-market travel apps are giving way to micro-segmented experiences. One of the most striking examples: **pet travel.** With **56% of people now owning pets**, the demand for pet-friendly filters, specialized booking flows, and tailored travel experiences is growing rapidly (Amadeus, 2026).\n\nThis is part of a broader pattern. Travelers increasingly expect apps to reflect their specific identity beyond just the destination. Solo female travelers, accessibility-focused users, remote workers, family travelers with young children – each segment has distinct needs that generic UX fails to serve.\n\nBuilding for micro-segments doesn't mean rebuilding from scratch each time. It means modular, extensible architecture. Boldare's teams specialize in scalable product foundations that allow new verticals and user segments to be added without structural rework.\n\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n
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\n 		Niche Segmentation56% pet ownersMicro-segmentsIdentity-firstSpecialized flows\"BLDR
\n\n\n\n### 7. End-to-end travel ecosystems\n\nThe best travel apps in 2026 cover the full journey — discovery, booking, on-trip help, and post-trip engagement in one experience.\n\nThat takes serious integration work: GDS systems, payment gateways, loyalty platforms, live availability APIs, mapping. Plus infrastructure that holds up at scale. 69% of travelers say they'd use biometric airport systems just to avoid friction (Amadeus, 2026) - seamlessness is now a baseline expectation.\n\nBoldare's track record here is concrete. Our [BlaBlaCar](https://www.boldare.com/work/case-story-blablacar/) partnership spanning 27 markets, 18 months, and 10 products delivered grew the platform's user base from 24 to 35 million. That's what end-to-end ecosystem thinking looks like in practice.\n\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n
\n \n \n
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\n 		\n \n \n
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\n 		End-to-End EcosystemsJourney orchestration69% want biometricsSeamless touchpointsFull integration\"BLDR
\n\n\n\n## Why Boldare is built for this moment\n\nThe trends above aren’t just a feature list – they describe a new architecture for what a travel app is, and the organizational capability required to build it. That's a high bar for most in-house teams. It's also exactly what we built Boldare to do.\n\n* [Deep travel domain expertise](https://www.boldare.com/industries/smarter-travel-tech)\n\nWe've been building travel and hospitality products for over two decades. Our client list includes BlaBlaCar, TUI Musement, and Planet Escape. We understand GDS integrations, booking engines, loyalty systems, and real-time availability at a level that general-purpose agencies don't.\n\n* [AI-native delivery](https://www.boldare.com/blog/ai-native-delivery-partner-guide/)\n\nWe're not just experimenting with AI – we've rebuilt our internal workflows around it. Our AI/ML stack (OpenAI, LangChain, LlamaIndex, RAG) and use of tools like Claude Code and CursorAI in production means AI features arrive faster, with more reliability, than teams still treating AI as an add-on.\n\n* [Agile at the organizational level](https://www.boldare.com/services/agile-coaching-services/)\n\nMost companies say they're Agile. We're organized around it – self-managing teams, no management overhead, a build-measure-learn culture baked into how we operate. For founders who need to move fast and course-correct often, this isn't a process claim. It's a structural advantage.\n\n* [Full-stack capability for complex ecosystems](https://www.boldare.com/services/devops-consulting-services/)\n\nReact, Node.js, Python, Vue.js, iOS, Android, React Native, AWS-certified architecture – we cover the full technical breadth required to build integrated travel ecosystems, not just single-feature apps. Whether you need one software engineer for a travel app project or an entire cross-functional team, we scale to fit.\n\n* [Proven scale](https://www.boldare.com/services/consulting-and-scaling)\n\nAn 80% client retention rate and 300+ products delivered across 20+ years aren't vanity numbers. They reflect a partner that understands how to grow products alongside the businesses that build them.\n\nFor CTOs weighing a build-vs-partner decision, and for founders who need senior product thinking as much as engineering capacity, our model – embedded teams, fast onboarding, delivery-ready within days – removes the ramp-up cost that usually kills momentum.\n\n## Summary\n\nThe [travel app market](https://www.boldare.com/industries/smarter-travel-tech) in 2026 rewards clarity: clarity about who you're building for, how the underlying infrastructure is shifting, and where AI adds genuine value versus noise. The trends such as AI planning, behavioral personalization, multimodal search, niche segmentation, end-to-end ecosystems point toward a more intelligent, more integrated, and more human-aware generation of travel products.\n\nBuilding that requires more than technical execution. It requires a partner who understands travel deeply, can move at founder speed, and brings AI capability that's already proven in production.\n\nThat's what Boldare does. If you're building the next generation of travel technology, [let’s talk!](https://www.boldare.com/contact/)\n\n## Sources: \n\nAmadeus Travel Trends Report 2026\n\nSBS Journal of Applied Business Research, 2025"}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1774001336/travel_trends_20206_wk0l2r.png","lead":"The travel industry doesn't wait. Market windows close fast, user expectations compound, and the gap between apps that feel intelligent and those that feel dated is widening by the quarter. For CTOs and founders navigating this space (whether you're scaling a booking platform, launching a new travel vertical, or modernizing legacy hospitality infrastructure) 2026 is a pivotal year to get the product direction right.\n\nThis article breaks down 7 trends reshaping travel apps right now, what they mean for your product roadmap, and how the right development partner can turn these signals into competitive advantages.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-03-20T11:10:07.937Z","slug":"travel-app-trends-2026-complete-guide","type":"blog","slugType":null,"category":null,"additionalCategories":["News","Ideas"],"url":null},"author":"Aleksander Dąbrowski","authorAdditional":"","box":{"content":{"title":"Travel app trends in 2026: The complete guide for product leaders","tileDescription":"Discover 2026 travel app trends shaping the industry: AI, personalization, VR, and ecosystem platforms. Insights for CTOs and product leaders.","coverImage":""},"coverImage":null}},"id":"8ac54738-8d41-5f67-8657-224dce49faee"}},{"node":{"excerpt":"","fields":{"slug":"/blog/how-to-optimize-apis-for-performance-security-and-ai-workloads-2026-guide/"},"frontmatter":{"title":"How to optimize APIs for performance, security, and AI workloads - 2026 Guide","order":null,"content":[{"body":"## Why APIs become bottlenecks as products scale\n\nEarly-stage startups often operate with a small number of services and relatively simple traffic patterns. As products grow, the architecture becomes significantly more complex.\n\nA mature SaaS platform may include:\n\n* dozens or hundreds of microservices\n* multiple external integrations\n* service-to-service traffic across regions\n* asynchronous workflows and event streams\n\nAt this stage, bottlenecks emerge from how services interact with each other. Poorly defined API boundaries create cascading failures, inefficient payloads increase network latency and unclear ownership leads to breaking changes that propagate across teams.\n\nIn other words, API design becomes an organizational scaling problem as much as a technical one.\n\n\n\n## Architecture first: service boundaries matter more than caching\n\nMost growing digital platforms eventually move toward [microservices](https://www.boldare.com/blog/mach-framework/) or modular architectures. Splitting a system into smaller services improves scalability because components can scale independently. However, this also increases the number of API calls between services. At scale, the biggest performance improvements often come not from infrastructure tweaks but from clear service boundaries.\n\nWell-designed service boundaries reduce:\n\n* cross-service latency\n* redundant network calls\n* tightly coupled systems\n\nMany engineering teams discover that performance improves dramatically when services are reorganized around business capabilities rather than technical layers.\n\n## REST, GraphQL, or gRPC? Trade-offs at scale\n\nProtocol choice matters more as systems grow. Each API style solves different problems, and large platforms often use several simultaneously.\n\n### REST\n\nREST remains the most widely used API style for external integrations.\n\nIts advantages include:\n\n* compatibility with HTTP caching\n* simple tooling and debugging\n* mature ecosystem support\n\nFor public APIs or partner integrations, REST often remains the most practical choice.\n\n### GraphQL\n\nGraphQL addresses common frontend problems such as over-fetching or multiple network requests. However, large-scale deployments introduce real trade-offs.\n\nGraphQL makes HTTP-level caching more difficult, because responses depend on dynamic queries. It can also introduce N+1 query problems if resolvers trigger multiple database calls without batching layers.\n\nAuthorization can become complex as well, since access control may need to be applied at the field level.\n\nBecause of this, many platforms use **GraphQL as an API gateway** layer for frontend clients, while keeping internal services built on REST or gRPC.\n\nFor service-to-service communication, many platforms increasingly adopt gRPC.\n\n### gRPC\n\ngRPC uses Protocol Buffers, a binary serialization format that is significantly more efficient than JSON. This reduces payload sizes and improves serialization speed, and supports bidirectional streaming, which is particularly useful for real-time pipelines and AI workloads.\n\nA common architecture today is:\n\n* REST or GraphQL for external APIs\n* gRPC for internal service communication\n\nThis balances developer experience with performance efficiency.\n\n## Security is part of performance engineering\n\nSecurity layers affect latency just as much as infrastructure choices. In distributed architectures, authentication and authorization happen on almost every request. Poorly designed security layers can therefore introduce measurable latency across service chains.\n\nModern API architectures typically rely on:\n\n* token-based authentication (OAuth2 or JWT)\n* mTLS for service-to-service authentication\n* API gateways enforcing centralized rate limiting\n* zero-trust network policies\n\nRate limiting also protects systems from cascading failures. Without throttling, a single misbehaving client can overwhelm downstream services.\n\nSecurity is therefore not only about compliance -but also about system resilience.\n\n## Observability replaces traditional monitoring\n\nMonitoring tells you when something breaks. Observability helps you understand why it breaks.\n\nIn distributed systems, API failures rarely occur in isolation. Latency problems often appear across multiple services and asynchronous workflows. Modern platforms rely on three pillars:\n\n**1. Distributed tracing**\n\nTracing systems allow engineers to follow requests across service chains and identify bottlenecks.\n\n**2. Structured logging**\n\nLogs enriched with contextual metadata make debugging possible in complex systems.\n\n**3. Service-level objectives (SLOs)**\n\nInstead of tracking uptime alone, engineering teams define reliability targets such as latency thresholds or error budgets.\n\nWithout observability, diagnosing API latency in large microservice architectures becomes extremely difficult.\n\n## Who owns an API when ten teams depend on it?\n\nMost modern organizations converge on two types of teams:\n\n**1. Platform teams**\n\nResponsible for shared infrastructure such as API gateways, authentication layers, and developer tooling.\n\n**2. Stream-aligned teams**\n\nProduct teams responsible for business capabilities and the APIs exposing them.\n\nWithout clear ownership, APIs quickly become fragile. Teams introduce breaking changes or duplicate functionality.\n\nTo manage this complexity, many organizations introduce:\n\n* versioning policies and sunset strategies\n* contract testing between services\n* schema registries for API definitions\n* automated deprecation pipelines\n\nThese mechanisms allow dozens of teams to evolve APIs without breaking each other’s systems.\n\n## FinOps: API traffic is also a cost problem\n\nAPI performance also has a financial dimension, because at scale, network traffic becomes a major cloud cost driver.\n\nFor example, cross-region data transfer in AWS typically costs around $0.08–$0.09 per GB. A platform transferring 10 TB of data per month between services can therefore spend roughly $800–$900 monthly just on data egress.\n\nIn larger architectures with hundreds of services, inefficient traffic patterns can quickly grow into tens of thousands of dollars per year in avoidable infrastructure costs.\n\nBecause of this, many scale-ups redesign APIs toward:\n\n* event-driven architectures instead of polling\n* regional service boundaries\n* smaller payload sizes\n* edge-based processing\n\nOptimizing latency and cost often become the same engineering problem.\n\n## AI-native APIs introduce new challenges\n\nAI workloads introduce new API patterns that traditional architectures were not designed for. Unlike standard service calls, AI inference requests often have:\n\n* unpredictable latency\n* variable compute cost\n* streaming outputs instead of single responses\n\nLarge language models frequently return results progressively via streaming protocols such as Server-Sent Events (SSE) or WebSockets.\n\nAPI gateways therefore need to support:\n\n* long-running connections\n* token-based rate limiting instead of request limits\n* backpressure handling for slow consumers\n\nCold starts also become a challenge. When model infrastructure scales dynamically, response times can vary significantly.\n\nDesigning APIs for AI systems requires engineering teams to think about latency variability, not only average response times.\n\n## Key takeaways\n\nFor scale-ups, API optimization in 2026 is not about adding another caching layer. The real challenges lie in operating APIs within complex product ecosystems.\n\nEngineering leaders increasingly focus on five areas:\n\n**1. Architecture - defining clear service boundaries**\n\n**2. Security - implementing zero-trust and rate limiting**\n\n**3. Observability - tracing requests across distributed systems**\n\n**4. Governance - managing API evolution across teams**\n\n**5. Cost efficiency - controlling traffic patterns and infrastructure spend**\n\nAs AI workloads grow, APIs must also support streaming responses, token-based rate limits, and variable latency patterns.\n\n## A practical perspective\n\nIn practice, solving these challenges rarely comes from adopting a single tool or framework. It requires aligning architecture, engineering practices, and product strategy.\n\nThis is where experienced product teams become valuable. At Boldare, we work with companies moving from early product-market fit to scaling platforms used by millions of users. In those environments, API decisions are rarely isolated technical choices - they shape how fast a product can evolve.\n\nOptimizing APIs is therefore less about chasing new technologies and more about designing systems that can grow without collapsing under their own complexity.\n\n## FAQ\n\n**Q: What is API optimization?**\n\nA: API optimization refers to improving the performance, scalability, and reliability of APIs by addressing latency, architecture design, security, and operational efficiency.\n\n**Q: Why do APIs become bottlenecks in scale-ups?**\n\nA: As products grow, the number of services and integrations increases. Without clear API governance, observability, and security controls, service-to-service traffic becomes difficult to manage.\n\n**Q: Is GraphQL always better than REST?**\n\nA: No. GraphQL offers flexibility but introduces challenges in caching, authorization, and rate limiting. Many organizations use GraphQL at the frontend layer while keeping REST or gRPC internally.\n\n**Q: What is the difference between monitoring and observability?**\n\nA: Monitoring detects system failures or performance issues. Observability helps engineers understand the root cause of those issues using tracing, logs, and metrics.\n\n**Q: Why are APIs important for AI-driven products?**\n\nA: AI systems depend on APIs for data access, model inference, and workflow orchestration. Efficient APIs are necessary to handle streaming responses, token-based limits, and unpredictable inference latency."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1773666998/api_rxdkwi.png","lead":"Most API optimization techniques - caching layers, CDNs, autoscaling, or GraphQL - have been industry standards for nearly a decade. Any experienced engineering team already knows them.\n\nYet many scale-ups still hit severe API bottlenecks as their products grow. The reason is simple: **API performance problems in 2026 rarely come from missing Redis or a CDN. They come from architecture, governance, and operational complexity.**\n\nFor fast-growing SaaS companies, APIs sit at the center of three pressures:\n\n1) distributed microservice architectures\n\n2) security and compliance requirements\n\n3) AI workloads with unpredictable latency\n\nOptimizing APIs today therefore means balancing performance, security, observability, and cost at the same time.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-03-16T12:05:02.017Z","slug":"api-optimization-guide-2026","type":"blog","slugType":null,"category":null,"additionalCategories":["Tech","How to","Strategy"],"url":null},"author":"Aleksander Dąbrowski","authorAdditional":"","box":{"content":{"title":"How to optimize APIs for performance, security, and AI workloads - 2026 Guide","tileDescription":"Learn how scale-ups optimize APIs for performance, security, and AI workloads. A practical guide to architecture, governance, and observability at scale.","coverImage":""},"coverImage":null}},"id":"8c7724b4-2152-578f-95ee-9a1f62c4901e"}},{"node":{"excerpt":"","fields":{"slug":"/blog/enterprise-crm-migration-without-disruption-lessons-from-our-own-stack/"},"frontmatter":{"title":"From legacy stack to modern CRM: how we migrated our own data without stopping the business","order":null,"content":[{"body":"Enterprise CRM platforms are among the stickiest technology decisions an organisation makes. The data accumulates, integrations multiply, and teams build their workflows around the system's constraints – often without realising it. By the time the case for migration becomes undeniable, the cost of staying has quietly exceeded the cost of leaving.\n\nWe know this because we lived it.\n\nAs an AI-native company with over two decades of operations, we had accumulated years of business data across a major CRM and marketing automation platform – thousands of client records, transactions, active projects, marketing campaigns, and sales processes spanning multiple international markets. The systems had served us well. Then, gradually, they didn't.\n\nWhat changed wasn't a single failure. It was the slow accumulation of friction: interfaces that hadn't kept pace with **modern UX standards, a cost structure that no longer reflected delivered value, change processes that required specialist involvement for minor adjustments, and a platform roadmap increasingly misaligned with B2B needs.** The systems were simultaneously too much and not enough.\n\nWe made the call to migrate. And we did it on our own data, our own processes, with our own team – while the business kept running.\n\n## The part of the migration plan that's usually wrong\n\nBefore getting into methodology, it's worth being direct about the complexity involved – because this is where many migration projects are underestimated at the planning stage.\n\nEnterprise CRM data after years of active use is not a clean database. It's an organism. It has:\n\nRelational depth – records linked across objects in ways that don't always follow the original data model\n\nHistorical conventions – field naming, categorisation logic, and tagging that evolved over years and exists nowhere in any documentation\n\nEmbedded business logic – rules, automations, and triggers that encode processes your team may not even consciously articulate anymore\n\nIntegration dependencies – connections to other systems in your stack that assume specific data shapes and field mappings\n\nOn top of this, marketing automation layers add another dimension of complexity. Campaigns, audience segments, nurture sequences, and conversion paths are not just data – they are logic. Migrating them isn't a copy-paste operation; it's a redesign exercise.\n\nOur non-negotiable requirements going in:\n\n100% of historical data transferred – no selective migration, no data left behind\n\nFull analytical and reporting capability from day one in the new system\n\nAll marketing automations and campaign logic migrated, with funnel continuity preserved\n\nZero downtime – sales and marketing teams operational throughout\n\nFull process continuity across multiple international markets running simultaneously\n\n*\n\n## The methodology that made it work\n\nWe approached this with the same methodology we bring to client migration engagements – because we've learned, across many projects, that the technical execution is only half the problem.\n\n### 1. Strategic audit before any technical work\n\nThe first step wasn't exporting data. It was understanding what the data actually meant to the business.\n\nWe mapped how each team used the system, which data was actively referenced versus historically archived, which processes were genuinely critical versus workarounds that had calcified into habits, and where the new system needed to behave differently rather than simply replicate the old one. This produced a clear target vision – not just technically, but operationally.\n\nThis step is frequently skipped or rushed. It shouldn't be. Decisions made here determine the shape of everything downstream.\n\n### 2. Data model design before data movement\n\nSource and target systems have different object models, different field conventions, different relationship logic. Assuming a structural match – even partially – is where migrations start to break.\n\nWe designed the target data model in full before moving a single record. This included mapping every field, every relationship, every validation rule, and every edge case we could identify. It also meant making explicit decisions about what *not* to migrate – legacy data that had no business value in the new system and would only introduce noise.\n\n### 3. Export, transform, map – with full traceability\n\nWe used native platform APIs and export functionality to extract data in formats suited for clean transformation. The mapping layer was built with full traceability – every source field to every target field documented, every transformation rule explicit.\n\n### 4. Integration reconfiguration and verification\n\nEvery integration in your stack that touches the CRM needs to be reconfigured, tested, and verified independently. We catalogued all integrations upfront and treated each one as a discrete migration task – not an afterthought.\n\nEnd-to-end verification testing ran against real data before any team was cut over to the new system.\n\n### 5. Staged rollout and team enablement\n\nA technically successful migration that results in confused teams is still a failed migration. We ran structured enablement sessions for every team affected, provided documentation tailored to their workflows, and maintained a parallel support window post-cutover to catch anything that emerged in real use.\n\n## Outcomes\n\n### Migration results\n\n**100% data transferred** – complete operational history migrated without quality loss.\n\n**Zero downtime** – all teams remained operational throughout the transition, with no interruption to sales or marketing processes.\n\n**Full data integrity** – all record relationships, campaign structures, and process logic preserved exactly as in the source system.\n\n**Full analytical capability from day one** – reporting functional immediately post-cutover, with no degradation in data visibility or dashboard accuracy.\n\n### Operational impact\n\n**Modern, autonomous tooling** – teams gained an intuitive interface with no dependency on specialist support for day-to-day configuration.\n\n**Reduced operational overhead** – simpler system architecture translated directly into faster execution and increased team autonomy across departments.\n\n**Improved cost-to-value ratio** – lower platform spend with greater delivered capability and broader adoption across the organisation.\n\n**No more administrative bottlenecks** – configuration and process changes are now handled directly by teams, without routing through administrators or specialist gatekeepers.\n\n## The CTO's checklist before committing to migration\n\nIf you're evaluating a similar move, the questions that matter most aren't about the target platform. They're about your current data and your organisation's readiness:\n\nDo you have a complete inventory of all data objects, relationships, and custom fields in your current system?\n\nDo you understand which automations encode critical business logic — and who owns that logic?\n\nDo you have a full map of all integrations that depend on your current CRM's data shape?\n\nIs your cutover strategy designed for zero downtime, or are you assuming a maintenance window?\n\nWho is accountable for data quality verification post-migration — and what does sign-off look like?\n\nIf any of these are unclear, that's where the work starts — before any platform evaluation.\n\nWe've run this process for ourselves and for clients across CRM platforms, marketing automation tools, ERP systems, and other business-critical applications. If you're facing a migration decision, [let's talk](https://www.boldare.com/contact/)."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1773673796/Frame_1321314557_j5n2gw.png","lead":"Data migration is one of the most challenging technology projects an organisation can face – and it is both technically complex and strategically underestimated. The technical side is hard enough: object models diverge, field conventions drift over years, business logic hides in automations nobody remembers writing. But the deeper risk is treating it as a purely technical problem in the first place.\n\nIn this article, we share our own experience. Boldare carried out a full CRM and marketing automation migration within its own organisation – and we use that example **to show how to approach this kind of project methodically: from the data audit, through target model design, all the way to a zero-downtime cutover.** At the end – a checklist for any CTO considering a similar move.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-03-16T00:00:00.000Z","slug":"enterprise-crm-migration-without-disruption-lessons-from-our-own-stack","type":"blog","slugType":null,"category":null,"additionalCategories":["How to"],"url":null},"author":"Magdalena Chmiel","authorAdditional":null,"box":{"content":{"title":"From legacy stack to modern CRM: how we migrated our own data without stopping the business","tileDescription":"We migrated years of our own CRM and marketing automation data without downtime. Here's the honest technical and strategic breakdown – for CTOs considering the same move.","coverImage":null},"coverImage":null}},"id":"f915bc8c-d639-5d89-ae68-ed7cdb5da00f"}},{"node":{"excerpt":"","fields":{"slug":"/blog/product-design-in-the-age-of-ai-what-needs-to-change-in-2026/"},"frontmatter":{"title":"Product design in the age of AI: What needs to change in 2026","order":null,"content":[{"body":"## How AI is reshaping the designer's day-to-day\n\n### The routine stuff is going away\n\nWireframes, basic prototypes, design-to-code handoffs – a growing suite of tools can now handle these tasks faster and more cheaply than a human working alone. This doesn't make designers less important, but it does move the value of design to a different place.\n\nWhen interface generation becomes fast and cheap, the scarce resource is no longer the ability to produce screens. It's the judgment to ask the right questions before producing anything. As Folorunso et al. (2025) note, AI-generated content still depends on human designers for selection and curation. AI expands the solution space; humans have to navigate it.\n\nIn many product teams today, this shift is already visible. Designers are spending less time refining pixels and more time mapping product flows, pressure-testing assumptions, and working alongside engineers during early discovery.\n\n### AI has an uneven impact across the design process\n\nMany teams assume AI accelerates every stage of design. In practice, it doesn't. A 2025 study in Information Systems Research by Hou et al. tested this through controlled experiments with designers at varying experience levels, working with and without generative AI tools.\n\nDuring ideation, AI meaningfully improved creative output for designers at all levels. It helped break cognitive fixation – the tendency to anchor too early on initial ideas – and opened up directions teams wouldn't otherwise have explored (Hou et al., 2025).\n\nDuring implementation, the picture was more complicated. For less experienced designers, AI continued to add value. For expert designers, however, AI was actively counterproductive. Experienced practitioners using AI spent 57% more time on their work than peers who went without it – with no measurable improvement in output quality (Hou et al., 2025). The reason: senior designers have established working rhythms, and AI's outputs clashed with those rather than complementing them.\n\n\n\n### What this means in practice\n\nLean into AI during discovery and ideation. Be more selective about where it enters the implementation phase, particularly for senior practitioners. Give designers agency over when and how they use these tools, rather than enforcing AI as a default across every stage.\n\n## Why rigid process frameworks are losing their edge\n\n### When the method becomes the point\n\nDesign thinking, design sprints, and structured innovation canvases gave many organizations their first real exposure to user-centered ways of working. That contribution was genuine. Over time, though, the framework in many organizations became the goal rather than the vehicle. Teams run workshops to tick boxes. Discovery outputs look convincing but don't actually change what gets built. Ackermann (2023) traces this pattern directly, noting how the methodology's emphasis on novelty often produced ideas that were compelling on paper but difficult to execute in practice.\n\n### Speed demands flexibility\n\nIn a faster product environment – where AI can compress ideation from weeks to days – rigid frameworks become bottlenecks rather than scaffolding. Teams need to move between discovery and experimentation more fluidly, run tighter cycles, and make decisions closer to the actual problem.\n\nThe goal isn't to abandon process entirely. It's to treat frameworks as thinking tools rather than compliance checklists. A team that can identify the right question, prototype an answer quickly, and test it with real users within a week will consistently outperform a team still scheduling its kick-off workshop.\n\n## Why designers need to be in the room earlier\n\n### Late involvement means limited impact\n\nWhen designers only enter the process after requirements are already written, the most important decisions have already been made – often by people with less direct user context and less visibility into technical tradeoffs. By the time design starts, the solution space has been narrowed by assumptions nobody thought to question.\n\nStrong design teams today operate differently. They participate in shaping requirements before they're locked. They challenge product assumptions by drawing on perspectives from other industries and contexts. Design becomes a strategic input to product direction, not a function that adds polish to decisions made elsewhere.\n\n### How Boldare approaches this\n\nThis is central to how Boldare works with product teams. Rather than siloing design, development, and product management, Boldare operates with cross-functional teams that share ownership of outcomes. Designers actively participate in shaping product decisions – providing feedback on assumptions, proposing alternative directions, and contributing insights from work across industries and contexts.\n\nOver the past 20 years, Boldare has worked on hundreds of digital products, from early-stage startups to large-scale platforms. That breadth of experience allows teams to recognize patterns quickly and challenge assumptions earlier in the product lifecycle. Patterns that repeat across contexts are more reliable than insights drawn from a single product or market. When designers work inside cross-functional teams carrying that accumulated experience, their contribution reaches beyond interface quality and into business outcomes.\n\nThis structure also reflects something consistently observed in real product environments: companies with cross-functional implementation teams significantly outperform those where design and development operate in isolation (Folorunso et al., 2025). The organizational model matters as much as the tools being used.\n\n## Why design and engineering can't keep working in separate lanes\n\n### Handoffs hide problems until it's too late\n\nMany of the most expensive product issues don't surface in user research or stakeholder reviews. They emerge in the gap between how a flow is designed and how it actually behaves – between a user's expectation and the system's underlying logic. Teams that communicate primarily through handoffs repeatedly discover these gaps too late, when they're costly to fix.\n\nChong (2025) frames this using an information-theoretic lens: when different parts of a product team work in isolation, misalignments accumulate at the boundaries. Assumptions held by one party remain invisible to another, producing a product that's less coherent than any individual contributor intended.\n\n### What working together continuously actually looks like\n\nSome teams are tackling this through designer-developer pairing, shared prototyping sessions, and joint discovery work throughout the release cycle. In one team Boldare recently worked with, designers and engineers began pairing during the release phase. Within two weeks, they had identified several critical flow issues that had gone undetected through earlier design reviews.\n\nAt Boldare, cross-functional teams collaborate across the full product lifecycle – from discovery through development and testing. This tightens communication, accelerates iteration, and creates shared ownership of what's being built. Design stops being a discrete phase and becomes an ongoing conversation. Problems surface earlier, solutions get refined faster, and the end product is more coherent because the people building it have been thinking about it together from the start.\n\n## Why what a product does matters more than how it looks\n\n### The shift toward invisible design\n\nModern digital products increasingly succeed by being simple and frictionless rather than visually impressive. Users don't notice great design – they just notice when things work. The best interfaces reduce cognitive load, minimize steps between intent and outcome, and get out of the way.\n\nBroader trends are reinforcing this: ambient computing, AI-assisted interfaces, and automation-driven workflows are all shifting value away from the visual layer and toward the behavioral layer. What the product does matters more than how it looks while doing it.\n\n### Reprioritizing what design teams focus on\n\nFor design teams, this means visual craft – typography, color, composition – while still relevant, is no longer sufficient to demonstrate strategic impact. The higher-order contribution lies in the quality of the flow, the appropriateness of the interaction model, and the coherence between what the product promises and what it actually delivers.\n\nBoldare's product-first approach reflects this directly. The emphasis is on user research, rapid prototyping, and hypothesis-driven development – ensuring design decisions are grounded in what users actually need rather than what performs well in a presentation.\n\n## What product leaders should do differently\n\n* **Reconsider what you're hiring for**\n\nThe most valuable designers in 2026 aren't necessarily those with the strongest visual portfolios. They're people who combine user understanding with product judgment – who can engage credibly on strategy, work fluidly with engineering partners, and apply AI tools intelligently at the right stages of the process. Organizations that evaluate designers primarily on visual output will consistently overlook them.\n\n* **Change how teams are structured**\n\nDesign that operates as a discrete phase – receiving requirements, producing deliverables, handing off – will consistently underperform design that's embedded as a continuous function within cross-functional product teams. The organizational model should match the kind of contribution design is expected to make.\n\n* **Integrate AI with intention**\n\nThe evidence is clear: AI delivers consistent value in ideation and discovery, and variable or negative value in implementation, especially for experienced practitioners (Hou et al., 2025). Build AI into early-phase workflows first. Approach implementation-phase adoption with more care, and pay close attention to how it interacts with how your designers actually work.\n\n* **Use process as a tool, not a destination**\n\nStructured frameworks have real value – but only when they serve the problem at hand. Teams that can think clearly about what they're building and why, iterate quickly, and make sound decisions with incomplete information will consistently outperform teams following a process correctly but slowly.\n\n## Closing thoughts\n\nProduct design is undergoing a genuine transformation. The designer of 2026 isn't defined solely by visual execution, but by the ability to understand product strategy, work closely with engineering, use AI where it actually helps, prototype and test quickly, and push back on assumptions that would narrow the solution space too early.\n\nCompanies that adjust their expectations and team structures to match this shift will build better digital products, faster. Those that continue treating design as a UI production function risk producing the right pixels for the wrong problems. The organizations that grasp this today will be the ones building the most resilient digital products tomorrow.\n\n## References\n\nAckermann, R. (2023). [Design thinking was supposed to fix the world. Where did it go wrong?](https://www.technologyreview.com/2023/02/09/1067821/design-thinking-retrospective-what-went-wrong/) *MIT Technology Review.*\n\nChong, L. (2025). [Maxwell's demon, system boundary, and interface ROI: The importance of logical integrity in UI/UX design and evaluation.](https://www.researchgate.net/publication/393135679_Maxwell's_Demon_System_Boundary_and_Interface_ROI_The_Importance_of_Logical_Integrity_in_UIUX_Design_and_Evaluation) *Cognitive Computing and Internet of Things.*\n\nFolorunso, J., Vayyala, R., Oladepo, O., Kolapo, M. O. and Ogunsanya, V. A. (2025). [Product design: The evolving role of generative AI in creative workflows](https://www.researchgate.net/publication/390999724_Product_Design_The_Evolving_Role_of_Generative_AI_in_Creative_Workflows). *International Journal of Scientific and Management Research.*\n\nHou, J., Wang, L., Wang, G., Wang, H. J. and Yang, S. (2025). [The double-edged roles of generative AI in the creative process: Experiments on design work](https://www.researchgate.net/publication/396171626_The_Double-Edged_Roles_of_Generative_AI_in_the_Creative_Process_Experiments_on_Design_Work). *Information Systems Research.*"}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1773313159/Frame_2087325352_qxyaf5.png","lead":"For years, product design meant making screens. A product manager defined the requirements, a designer built the UI, and a developer shipped it. Design was a delivery function – valuable, but clearly bounded.\n\nThat model is under pressure in 2026. Three converging forces are pushing in the same direction simultaneously: AI tooling is absorbing more and more routine UI work, digital products are growing in complexity, and organizations need to ship faster than ever before. Together, these shifts are redefining what design is actually for – and what companies should realistically expect from their design teams.\n\nProduct design in modern teams is no longer purely about interface creation. It increasingly spans strategy, experimentation, and deep cross-functional collaboration.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-03-12T11:18:16.585Z","slug":"product-design-ai-2026-what-needs-to-change","type":"blog","slugType":null,"category":null,"additionalCategories":["Future","GenAI","Digital Product"],"url":null},"author":"Aleksander Dąbrowski","authorAdditional":"","box":{"content":{"title":"Product design in the age of AI: What needs to change in 2026","tileDescription":"AI is reshaping product design in 2026. Learn how teams must shift from UI delivery to strategy, cross-functional collaboration, and smarter use of AI tools.","coverImage":""},"coverImage":null}},"id":"c39fcdcc-1644-54ab-ae91-8d95ab2694b6"}},{"node":{"excerpt":"","fields":{"slug":"/blog/ai-support-toolkit-automating-product-support-with-claude-ai-jira-integration-and-lokiql-query-generation/"},"frontmatter":{"title":"AI support toolkit: automating product support with Claude AI, Jira integration, and LokiQL Query Generation","order":null,"content":[{"body":"## The problem: support in the M&S phase is every product's bottleneck\n\nEvery digital product in the Maintenance & Support phase generates recurring support tickets. Analysts, developers, and QA teams spend hours manually reviewing logs, querying databases, reading Jira tickets, and attempting to reconstruct the context of a bug – only to discover that the exact same issue was resolved three months ago.\n\nThe result? High maintenance costs, long Mean Time to Resolution (MTTR), team frustration, and a knowledge debt that compounds with every sprint.\n\nAI Support Toolkit is our answer to this problem: AI-driven automation that learns from historical tickets, generates diagnostic queries, and delivers a first-pass analysis of every new incident – before a human even opens Jira.\n\n## What is AI support toolkit?\n\nAI Support Toolkit is a lightweight, modular boilerplate for automating first-line product support. It is designed as a plug-and-play solution – deployable into an existing product repository in minutes. \n\nThe toolkit is built on three pillars: \n\n1. **Claude AI Skills** – a set of 12 ready-to-use skills for Claude (Claude Code) that handle every stage of ticket analysis \n2. **Bash scripts** – automation scripts for setup, initialization, and running the tool \n3. **YAML knowledge base** – a dynamically updated database of the most common support tickets, generated and maintained by AI \n\n**Repository:** [github.com/boldare/ai-support-toolkit](github.com/boldare/ai-support-toolkit)\n\n## How it works: system architecture\n\n### 1. Product codebase analysis\n\nBefore first use, the toolkit performs a deep analysis of the product's source code. The **code analysis** Claude Skill identifies:\n\n* **Programming language and framework** (Node.js, Python, Java, React, NestJS, etc.)\n* **Libraries and packages** – NPM, pip, or Maven dependencies that provide context for errors\n* **Logging infrastructure** – logger locations, logging libraries in use (Winston, Log4j, Pino), and log patterns\n* **System identifiers** – extraction of key identifiers used throughout the application, such as userID ,companyID, sessionID, transactionID, and requestID\n\nThis phase builds a **product context** that feeds all subsequent Claude prompts – significantly improving the accuracy of analysis and the relevance of generated diagnostic queries.\n\n### 2. Knowledge base seeding from historical Jira tickets\n\nThe toolkit pulls historical tickets from **Jira** and processes them through a dedicated Claude Skill for **historical ticket analysis**. This builds a YAML knowledge base containing:\n\n* Common error types and their categorization\n* Symptom patterns and root cause analysis\n* Proven solutions and workarounds\n* Related identifiers and ticket reference numbers\n\nThe knowledge base **evolves automatically** – every new ticket is compared against existing entries, enriching and updating the knowledge base without manual intervention.\n\n### 3. New ticket analysis workflow\n\nWhen a new support ticket comes in, the toolkit runs a multi-step analysis pipeline:\n\n**Step 1: Fetch the Jira ticket** The **Jira integration** Claude Skill retrieves the full ticket data including key details: user and company identifiers, timestamps, environment (production/staging), priority, and comment history.\n\n**Step 2: Generate diagnostic queries** Based on the ticket context, Claude generates ready-to-run queries:\n\n* **LokiQL** – for fetching application logs from Grafana Loki, filtered by `userID`, time range, and error level\n* **SQL** – for pulling relevant data from the product's relational database\n\n**Step 3: Analysis and classification** The collected logs and data are analyzed by Claude in the context of:\n\n* Source code and known error patterns\n* The historical ticket knowledge base (YAML)\n* Code analysis results (identified loggers, patterns, and identifiers)\n\nThe output is a complete report with a **problem diagnosis**, **probable root cause**, and **suggested resolution** – before a developer takes a single manual step.\n\n## 13 Claude Skills – a complete support toolkit\n\nThe heart of the boilerplate is **13 Claude Skills** covering every stage of the ticket resolution process.\n\n### Workflow (ticket investigation)\n\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
SkillPurpose
/fetch-ticket <TICKET>Fetch Jira ticket, extract identifiers, match KC pattern
/generate-log-request <TICKET>Generate LogQL query for the support team
/generate-data-request <TICKET>Generate SQL queries for production data
/analyze-logs <TICKET>Analyze logs against ticket context + KC, generate response
/close-ticket <TICKET>Archive completed ticket from tickets/ to log-archive/
/generate-work-history \\[time range]Generate Tempo-ready weekly work summary
\n\n\n\n### Setup and maintenance\n\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
SkillPurpose
/verify-jira-accessVerify Jira API credentials and project access
/init-workspaceAuto-detect modules, channels, and integrations from codebase
/init-log-databaseBuild the Log Database from source code analysis
/update-log-databaseMap unmapped ticket logs into the Log Database (10 per batch)
/init-knowledge-centerBuild the Knowledge Center from Jira ticket analysis
/update-knowledge-centerConsolidate draft KC patterns, deduplicate, promote to confirmed
/validate-log-standardsValidate codebase logging practices against standards
\n\n\n\n## Technologies and integrations\n\nAI Support Toolkit connects tools that already exist in most M&S team stacks:\n\n* **Claude AI / Claude Code** – LLM engine for analysis and content generation\n* **Claude Skills (`.claude/` directory)** – modular AI agent skill system\n* **Jira REST API** – source of truth for tickets, issue history, and identifiers\n* **Grafana Loki + LokiQL** – log aggregation system with a query language for log filtering\n* **SQL** – access to relational product data\n* **Bash scripting** – environment setup automation and HTTP server bootstrapping\n* **YAML** – lightweight, human-readable format for the ticket knowledge base\n* **HTTP Dashboard** – simple web interface for browsing the knowledge base\n\n## Features and developer experience\n\n### One-command setup\n\nThe toolkit ships with **setup scripts** that scaffold the entire file and folder structure required by the tool. Initializing Claude Skills in a product repository is a single command – a dedicated **init script** configures the `.claude/` directory with all 12 skills ready to use.\n\n### Knowledge base dashboard\n\nA built-in **HTML dashboard** lets you browse the current ticket knowledge base without opening YAML files. A dedicated script spins up a local HTTP server with an interface for exploring, filtering, and reviewing the history of resolved tickets.\n\n### Timesheet automation\n\nFor teams tracking time in Jira, the toolkit includes an optional Skill for **automatic timesheet entry generation** based on completed analysis – eliminating manual time logging.\n\n## Who is AI Support Toolkit for?\n\n**Product teams in the M&S phase** – especially those handling enterprise client tickets with high volumes and complex system environments.\n\n**Support engineers and L2/L3 teams** – analysts looking to reduce MTTR and lower the cognitive load of ticket triage and root cause investigation.\n\n**DevOps and platform teams** – engineers integrating Grafana Loki, Jira, and SQL into a unified debugging workflow.\n\n**Teams already using Claude Code** – organizations that use Claude as an AI assistant in daily development work and want to extend its capabilities into the support domain.\n\n## AI-driven support: the bigger picture\n\nAI Support Toolkit is part of the broader trend of **agentic AI workflows** in software engineering. Rather than treating AI as a code completion assistant, the toolkit deploys Claude as an **autonomous diagnostic agent** – capable of multi-step analysis, external data retrieval, and self-updating knowledge management.\n\nThis approach resembles **Retrieval-Augmented Generation (RAG)** patterns, where the AI model is enriched with a dynamically updated domain-specific knowledge base. In the support context, that knowledge base consists of historical tickets, and retrieval happens through the Jira API and the YAML knowledge base.\n\nFrom an **LLMOps** perspective, the toolkit demonstrates how structured Claude Skills can replace traditional, monolithic prompts – providing better control, testability, and the ability to iterate on individual stages of the analysis pipeline independently.\n\n## Getting started\n\n1. Clone the repository: `git clone https://github.com/boldare/ai-support-toolkit`\n2. Run the file structure setup script\n3. Initialize Claude Skills in your product repository\n4. Configure Jira API credentials and your Loki/SQL connections\n5. Run **code analysis** on your product repository\n6. Seed the knowledge base with historical Jira tickets\n7. Analyze your first support ticket\n\nFull documentation and configuration examples are available in the repository README.\n\n## Summary\n\nAI Support Toolkit solves a specific, costly problem: the time and cognitive load spent diagnosing recurring bugs in M&S products. By combining Claude AI Skills, Jira integration, LokiQL and SQL query generation, and a dynamic YAML knowledge base, the toolkit reduces first-pass ticket analysis from hours to minutes.\n\nThis is not another AI chatbot for customer support. It is an **AI-powered debugging pipeline** integrated directly into the tools your team already uses every day.\n\nCheck out the repository, try it on your project, and let us know how it performs in your context."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1773312504/Group_1000005045_cfgciz.png","lead":"**Weekly AI Bites is a series that gives you direct access to what’s happening in our day-to-day AI work. Every post comes straight from our team’s meetings and Slack, sharing insights, tests, and experiences we’re actively applying to real projects.What models are we testing, what challenges are we tackling, and what’s really working in products? You’ll find all of this in our bites.**\n\nAI Support Toolkit is an open-source boilerplate built by Boldare that automates the process of resolving support tickets in products at the Maintenance & Support (M&S) stage. The tool combines Claude AI Skills, Bash scripts, Jira API integration, LokiQL and SQL query generation, and a YAML-based ticket knowledge base into a single, cohesive, plug-and-play workflow.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-03-05T10:50:43.514Z","slug":"ai-support-toolkit-open-source","type":"blog","slugType":null,"category":null,"additionalCategories":["GenAI"],"url":null},"author":"Pawel Stankiewicz","authorAdditional":"","box":{"content":{"title":"AI Support Toolkit: automate M&S support with Claude Skills & Jira","tileDescription":"AI Support Toolkit is a plug-and-play boilerplate by Boldare that automates product support using Claude AI Skills, Jira API, LokiQL, SQL, and a self-updating YAML knowledge base.","coverImage":""},"coverImage":null}},"id":"5df5e4a1-d4a2-5074-97f5-44155b9c832e"}},{"node":{"excerpt":"","fields":{"slug":"/blog/cloud-migration-in-2026-a-ctos-guide-to-getting-it-right/"},"frontmatter":{"title":"Cloud Migration in 2026: A CTO's guide to getting it right","order":null,"content":[{"body":"## Cloud migration in 2026: Key challenges and industry trends\n\nCloud infrastructure has turned into a baseline expectation in current reality. Organizations rely on cloud platforms to support global operations, accelerate product development, and handle data at a scale that on-premise systems can't sustain.\n\nYet significant investment hasn't translated into consistent results. Enterprise cloud programs frequently struggle to deliver measurable business value - not because the technology fails, but because initiatives focus on moving infrastructure rather than transforming how organizations operate. Without clear metrics and governance frameworks, cloud programs become fragmented and difficult to justify (McKinsey, 2024)\n\n[Legacy architecture](https://www.boldare.com/blog/refactor-replace-isolate-2026-cto-guide-modernizing-legacy-systems-scaleups/) is the most persistent obstacle. Most enterprise systems weren't designed for the cloud. Monolithic applications (where functions are tightly coupled into a single deployment unit) made sense when they were built, but they become bottlenecks as products scale. Even minor changes can require full redeployment, slowing release cycles and raising operational risk (IJETRM, 2022).\n\nThis is why modern migrations beyond infrastructure relocation increasingly involve architectural redesign. Organizations are moving from monolithic systems to cloud-native architectures built on microservices, containerized services, and automated orchestration – improving scalability, fault isolation, and deployment speed in the process (IJETRM, 2022).\n\nSecurity and compliance have moved from afterthought to essential. Migrating systems that handle financial data, personal identifiers, or other regulated information requires security frameworks built into the migration design itself – not bolted on afterward. Increasingly, organizations are using AI-assisted anonymization pipelines to protect sensitive data while preserving its value for analytics and machine learning (Discover Computing, 2026).\n\n**Talent** **gaps** remain a structural constraint. Most organizations don't have sufficient in-house expertise in cloud architecture, DevOps, and distributed systems – and building that capability takes time most migration timelines don't allow. External partners have become a practical necessity(McKinsey, 2021).\n\nTaken together, these dynamics make the partner selection decision more consequential than the technology selection itself.\n\n## How to choose a cloud migration partner: 4 Criteria for CTOs\n\nSelecting a migration partner requires more than verifying cloud certifications or checking infrastructure credentials. Migration now touches the entire digital product ecosystem – architecture, operations, development workflows, and long-term platform strategy.\n\n**Architectural modernization expertise**. The partner needs a proven track record of transforming legacy systems into cloud-native architectures – just relocating them is no longer enough. That means hands-on experience designing microservices environments, implementing container orchestration, and building automated deployment pipelines that hold up under real production conditions (IJETRM, 2022).\n\n**Operating** **model** **transformation**. Cloud infrastructure delivers its full value only when engineering practices evolve alongside it. Evaluate whether partners can embed DevOps, DevSecOps, and Site Reliability Engineering into the organization – enabling continuous delivery, automated infrastructure management, and the kind of system observability that prevents incidents from becoming outages (McKinsey, 2025).\n\n**Data governance and security architecture**. Migrating systems that process sensitive data (financial records, personal identifiers, regulated information) requires security to be designed into the migration, not introduced after go-live. Look for partners with experience in automated monitoring, encryption strategies, and AI-driven security tooling that protects data in transit and at rest (Discover Computing, 2026).\n\n**Long-term platform and product capability**. Migration is rarely a one-time event. Organizations continue evolving their cloud platforms for years by adding analytics layers, integrating AI capabilities, and scaling customer-facing services. Partners who can support ongoing product development and infrastructure optimization are worth significantly more than those who hand off the keys at deployment.\n\n## Beyond infrastructure migration: How Boldare approaches cloud transformation\n\nMost cloud migration partners are built to execute a defined scope and exit. They'll move your workloads, hand over documentation, and close the engagement. But what they often leave behind is an organization that owns new infrastructure but hasn't fundamentally changed how it builds or scales software. That gap is where most cloud programs quietly fail.\n\nBoldare’s structured differently – and the difference starts with how we run ourselves.\n\n### We work in uncertainty by design\n\nMost migration vendors need a clearly scoped project to function well. We operate through [holacracy](https://www.boldare.com/blog/holacracy-in-nutshell/) – a self-organizing model where teams have distributed decision-making authority rather than hierarchical sign-off chains. This means we're built for complex, evolving engagements where the destination shifts as the work progresses. For organizations navigating genuine transformation rather than a scripted migration, that's a structural advantage most vendors can't offer. It's also a mindset that runs through everything we do – if you want to understand where it comes from, [our co-CEO's journey from jazz to tech](https://www.boldare.com/blog/the-mindset-behind-building-and-scaling-a-10m-ai-driven-digital-company/) is a good place to start.\n\n### AI is how we work\n\nMost software organizations treat AI as a feature layer – something bolted onto delivery as an experiment or an upsell. We embed it across the entire product lifecycle: discovery, architecture planning, coding, testing, and infrastructure optimization. For cloud migration, this means faster legacy analysis, sharper modernization decisions, and cloud environments calibrated correctly from day one rather than patched into shape afterward. If you want to understand what AI-native delivery actually looks like in practice, [we've written about it in detail here](https://www.boldare.com/blog/ai-native-delivery-partner-guide/).\n\n### We build product strategy into the migration, not after it.\n\nInfrastructure migrations that aren't connected to product direction tend to produce technically sound environments that constrain the wrong things. Our teams include product strategists, designers, and engineers working alongside cloud architects – so architectural decisions are made with product trajectory in mind, not just operational efficiency.\n\n### We leave organizations capable, not dependent.\n\nThe standard delivery model creates dependency: the partner holds the knowledge, the client holds the invoice. We structure engagements to build internal product teams and delivery processes on the client side so that organizations come out of the engagement with the capability to evolve their platforms independently. That's what long-term partnership actually looks like in practice, as opposed to recurring retainers dressed up as strategic relationships.\n\nTaken together, these values reflect a fundamentally different model for what a migration partner is supposed to do and what organizations should be left with when the engagement ends.\n\n## What makes a cloud migration successful in 2026\n\nCloud migration done right isn't a project with an end date since it's a shift in how an organization builds and operates software. The infrastructure is only the starting point. What matters is what you're capable of doing with it afterward.\n\nMost migration engagements move the workloads and close the ticket. The harder questions around architecture, product strategy, and internal capability get pushed to the next engagement, or never addressed at all.\n\nThe CTOs who get the most out of cloud migration are the ones who choose partners accountable for more than the technical handoff. Partners who treat the migration as the beginning of a platform story, not the conclusion of an infrastructure project.\n\nThat's the model we build around at Boldare. If you're navigating a cloud migration and want to understand whether our approach fits what you're trying to accomplish – let's talk.\n\n## References\n\nDiscover Computing. 2026. AI-driven anonymization for secure and privacy-preserving business intelligence cloud migration.\n\nIJETRM. 2022. Design and migration of large-scale enterprise applications to cloud-native microservices architectures.\n\nMcKinsey. 2024. Ending the confusion in cloud transformations: The dashboards and metrics everyone needs.\n\nMcKinsey. 2025. Unlocking cloud value: Achieving operational excellence through SRE.\n\nMcKinsey. 2021. Cloud migration opportunity: Business value grows but missteps abound."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1772708939/Frame_2087325350_vdifg1.png","lead":"Most CTOs in scaleups and enterprises have already committed to cloud migration. The challenge is how to execute it without turning a modernization initiative into an expensive infrastructure switch.\n\nCloud migration changes more than where your servers live. It restructures architecture, reshapes operating models, and directly affects how fast engineering teams can move. The organizations that get it right unlock scalable infrastructure and faster product delivery. The ones that get it wrong spend significant budget arriving at the same bottlenecks in a new environment.\n\nThis guide covers the current state of cloud migration, a framework for evaluating the right partner, and a closer look at how Boldare approaches the gaps that most migration engagements leave unaddressed.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-03-05T10:07:03.020Z","slug":"cloud-migration-2026-cto-guide","type":"blog","slugType":null,"category":null,"additionalCategories":["How to","Tech","Digital Product"],"url":null},"author":"Aleksander Dąbrowski","authorAdditional":"","box":{"content":{"title":"Cloud Migration in 2026: A CTO's guide to getting it right","tileDescription":"Moving to the cloud is the easy part. This guide covers what CTOs should demand from a migration partner in 2026 and what most get wrong.","coverImage":""},"coverImage":null}},"id":"bba736eb-e374-547b-bf5b-aa3055f16888"}},{"node":{"excerpt":"","fields":{"slug":"/blog/application-modernization-in-2026-what-ctos-need-to-know-about-ai-legacy-migration-and-choosing-the-right-partner/"},"frontmatter":{"title":"Application Modernization in 2026: What CTOs Need to Know About AI, legacy migration, and choosing the right partner","order":null,"content":[{"body":"## What application modernization means in 2026: Trends every CTO should understand\n\n### The financial weight of tech debt on IT budgets and enterprise value\n\nOrganizations are increasingly recognizing tech debt as a structural drag on enterprise value rather than a manageable inconvenience. McKinsey (2020) found that when left unaddressed, tech debt steadily erodes the engineering capacity that would otherwise drive innovation. Companies that take a disciplined approach to managing it tend to redirect significant engineering effort back toward business-generating work.\n\nThe modernization playbook in 2026 reflects this financial lens. Large-scale rewrites have largely fallen out of favor. Instead, leading CTOs focus on:\n\n* Refactoring the highest-friction parts of their value streams\n* Decommissioning redundant or overlapping systems\n* Reducing complexity in integration layers\n* Tying modernization investments to measurable cost and risk outcomes\n\nThis ROI-first model marks a shift away from architectural idealism toward capital discipline.\n\n### How generative AI is changing the economics of software development\n\nGenerative AI has crossed the threshold from experimentation into structured financial modeling. McKinsey (2023) frames AI's impact in terms of productivity gains that translate directly into cost-equivalent reductions – making it possible to compare AI initiatives with traditional efficiency programs on the same terms. That framing repositions AI integration as a core modernization lever rather than a separate innovation track.\n\nCritically, McKinsey (2023) argues that AI's impact must be analyzed at the functional level – not assumed enterprise-wide. For modernization programs, this means targeted deployment across specific workflows:\n\n* Code generation and automated refactoring\n* Test automation and quality assurance\n* Documentation creation and institutional knowledge capture\n* Operational analytics and incident response\n\nMcKinsey's projections include both conservative and accelerated adoption scenarios, reinforcing the case for phased rollouts with clearly defined checkpoints.\n\n### How AI will reshape engineering teams and delivery by 2030\n\nGartner's 2025 research on the future of software engineering surfaces a significant readiness gap: only 12% to 16% of engineering leaders believe their current processes, workforce structure, and architecture are genuinely prepared for AI integration. This finding reframes modernization – it can't stop at the codebase. It has to address how teams are organized and how work actually flows.\n\nEven so, the momentum is real. Gartner (2025) reports that 45% of software engineers are already recording productivity gains exceeding 10% from AI tooling. By 2030, however, those gains are expected to become baseline expectations. Differentiation will come from creativity, judgment, and the ability to orchestrate AI systems effectively.\n\nFor CTOs, that translates into modernization programs that explicitly include:\n\n* AI-assisted workflows embedded across the software development lifecycle\n* Structured collaboration between human developers and AI agents\n* Upskilling initiatives centered on AI engineering and oversight capabilities\n* Governance frameworks designed for AI-native delivery environments\n\nIn short, modernization and organizational transformation have converged into a single initiative.\n\n### Microservices and Headless Architecture: What the performance data shows\n\nWell-executed migrations to microservices and headless architectures produce substantial operational gains. Chintalapudi (2025) documents structured migration programs that took deployment frequency from monthly releases to multiple deployments per week, reduced mean time to recovery by more than 90%, and shortened feature release cycles by over 75%. Load testing results show meaningful improvements in response times and error rates as well.\n\nThese outcomes aren't automatic, though. They depend on well-defined service boundaries, CI/CD pipeline maturity, and governance alignment across teams.\n\nThe question in 2026 isn't whether to adopt microservices – it's whether the decomposition is disciplined and grounded in real product domains. Architectural change pursued for its own sake rarely delivers.\n\n### Security and resilience as primary modernization drivers\n\nSecurity has moved from a byproduct of modernization to one of its primary drivers. IBM (2025) puts the global average cost of a data breach at USD 4.44 million, with certain markets running considerably higher. Organizations that applied AI and automation extensively to their security operations reduced breach lifecycle times significantly and saved approximately USD 1.9 million compared to those that didn't.\n\nIncreasingly, modernization programs are being launched under a joint CTO and CISO mandate. Common goals include:\n\n* Strengthening observability across systems\n* Aligning to zero-trust architecture principles\n* Deploying AI-assisted anomaly detection\n* Eliminating shadow IT and uncontrolled AI tool usage\n\nSecurity architecture needs to be embedded in the modernization roadmap from the start, not appended at the end.\n\n## How to evaluate application modernization partners in 2026\n\nStrong modernization work requires a combination of architectural depth, economic thinking, and organizational change management. When assessing vendors, four criteria matter most.\n\n**1. Does the partner connect architecture decisions to business outcomes?**\n\nA credible partner begins by quantifying tech debt in financial terms and mapping each modernization step to cost or revenue impact. They establish performance baselines before any work begins and track indicators like deployment frequency, mean time to recovery, cost-to-serve, and operational overhead throughout delivery.\n\n**2. Can the partner reshape your engineering operating model, not just your codebase?**\n\nGiven that most organizations lack structural readiness for AI integration (Gartner, 2025), partners need to go beyond technical changes. That means supporting team restructuring, DevOps capability building, and AI governance design. Code changes without organizational changes rarely produce lasting results.\n\n**3. How does the partner manage AI governance and CI/CD security?**\n\nAs AI becomes embedded in development tooling, partners must put governance mechanisms in place that control how AI is used, protect data integrity, and maintain compliance. This includes structured approval processes for AI tooling and integration within secure [CI/CD pipelines](https://www.boldare.com/blog/cicd-optimization-vs-inhouse-devops-enterprise/).\n\n**4. Does the partner operate on a phased, metrics-driven delivery model?**\n\nMcKinsey (2020) highlights the value of consistent, incremental tech debt remediation over large periodic overhauls. Look for partners who can demonstrate a phased delivery approach with transparent milestones and clearly defined outcome metrics.\n\n## How Boldare addresses the gaps most modernization partners leave open\n\nThe evaluation criteria above point to a consistent weakness in the modernization market: most partners are built to transform codebases, not the engineering organizations that maintain them. They deliver a migration, hand it over, and leave teams structurally unprepared for what comes next – particularly around AI integration and long-term architectural governance.\n\nThat's the gap we built Boldare around. With over 20 years of end-to-end delivery experience, our model combines system diagnostics, architecture redesign, UX modernization, and incremental delivery – but critically, it also addresses how teams are structured, how decisions get made, and how AI gets embedded into day-to-day engineering work rather than bolted on afterward.\n\nOur core enterprise capabilities include:\n\n* [Legacy system migration](https://www.boldare.com/blog/refactor-replace-isolate-2026-cto-guide-modernizing-legacy-systems-scaleups/) and re-platforming\n* Architectural optimization and cloud readiness\n* Large-scale system integrations\n* MACH architecture implementation (Microservices, API-first, Cloud-native, Headless)\n* Full-lifecycle digital product development\n* [AI-native](https://www.boldare.com/blog/ai-native-delivery-partner-guide/) delivery model\n\nAI runs through our entire delivery model – from UX design validation and code generation to automated testing, [API optimization](https://www.boldare.com/blog/enterprise-api-performance-bottlenecks-and-practical-fixes/), traffic profiling, predictive scaling, and observability. We apply generative AI and LLM integrations to enterprise workflows, internal tooling, and customer-facing products.\n\nOur organizational model built on Holacracy and self-organizing, product-centric teams supports the kind of fast decision-making and transparent ownership that complex modernization environments demand, where cross-functional coordination is often the deciding factor between a successful migration and a stalled one.\n\nThe result: over 300 delivered digital products and long-term engagements with global brands including BlaBlaCar, Bosch, and Decathlon.\n\n## Conclusion\n\nApplication modernization in 2026 is defined by three converging forces: financial discipline, AI integration, and operational resilience. The research is consistent: unmanaged tech debt limits capital efficiency (McKinsey, 2020), generative AI generates real productivity-equivalent gains (McKinsey, 2023), and the majority of engineering organizations remain structurally unprepared to capitalize on AI (Gartner, 2025).\n\nCTOs who get this right will build programs around progressive refactoring, AI-native workflows, rigorous governance, and measurable outcomes. The right modernization partner brings architectural expertise, an AI-native delivery model, and product strategy together in a model that compounds over time rather than creating new technical or organizational debt.\\\n\\\n**Not sure where your biggest modernization risks actually sit? We can help you find out.**\n\n## References\n\nMcKinsey & Company. (2020). *Tech debt: Reclaiming tech equity.* \n\nMcKinsey & Company. (2023). *The Economic Potential of Generative AI: The Next Productivity Frontier.* \n\nGartner. (2025). *Software Engineering 2030: The Impact of AI.* \n\nIBM. (2025). *Cost of a Data Breach Report 2025.*"}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1772534529/blog_pgyruo.png","lead":"In 2026, application modernization has outgrown its identity as a purely technical exercise. It now shapes cost structures, AI readiness, compliance risk, and product velocity. For technology leaders at growing and enterprise-scale organizations, aging architecture has become a genuine liability **–** one that bleeds budget and accumulates systemic risk.\n\nThe data makes a compelling case. McKinsey (2020) estimates that tech debt accounts for 20% to 40% of total technology estates, with another 10% to 20% of new product budgets consumed by legacy-related remediation. At that level of drag, modernization becomes a capital allocation priority, not just an engineering one.\n\nThis guide explores modernization landscape in 2026, how AI is fundamentally changing software delivery and what separates credible modernization partners from the rest.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-03-03T08:40:51.310Z","slug":"application-modernization-2026-ai-legacy-migration-cto-guide","type":"blog","slugType":null,"category":null,"additionalCategories":["Future","Tech","Strategy"],"url":null},"author":"Aleksander Dąbrowski","authorAdditional":"","box":{"content":{"title":"Application Modernization in 2026: What CTOs Need to Know About AI, legacy migration, and choosing the right partner","tileDescription":"How AI, legacy migration, and tech debt are reshaping modernization in 2026 - and what CTOs need to know when choosing the right partner.","coverImage":""},"coverImage":null}},"id":"cb39f2fa-051d-587b-a8a3-7a772ffd356e"}},{"node":{"excerpt":"","fields":{"slug":"/blog/when-the-specification-became-the-test-ai-as-a-qa-agent-in-a-blockchain-dapp/"},"frontmatter":{"title":"When the Specification Became the Test: AI as a QA Agent in a Blockchain dApp","order":null,"content":[{"body":"## Introduction\n\nI’m building a blockchain lottery dApp on my own — with smart contracts in Foundry, a React frontend, and an indexer in Ponder. The app has three layers: business logic on-chain, an indexer that monitors events, and a frontend that brings it all together. Any change in one layer can potentially break something in the others.\n\nIn this article, I share an experiment using Chrome DevTools MCP + Claude Code as an autonomous QA agent. Early results are promising enough to keep exploring — and along the way, I uncovered an unexpected insight: the way requirements are written directly affects how effectively the agent tests. More on that at the end.\n\n## The Problem\n\nAfter every change, I should verify that the application still works end-to-end. Manually checking every feature took about 90 minutes of clicking through the app. Everything was precisely described in the PRD as User Stories with Acceptance Criteria — the scope was clear, the problem was elsewhere.\n\nIt wasn’t just about time. It was about attention. To verify whether a feature worked, I had to interrupt coding, switch context, click through the app, and switch back. It’s a classic trap: the more expensive the feedback, the less often you collect it. The less often you collect it, the more bugs you silently accumulate — and the later you find them, the more expensive they are to fix. My goal wasn’t to replace QA — it was to find someone who would do the clicking for me.\n\n## What I Tried Before — and Why Not Playwright?\n\nThe natural question: why not Playwright? It’s a mature E2E tool, supports Chromium, and has a great ecosystem.\n\n**First problem:** MetaMask. Playwright technically supports loading extensions (--load-extension), but MetaMask deliberately makes automation difficult — separate browser context, service worker, dynamic selectors, anti-bot mechanisms. Synpress is essentially a Playwright wrapper created specifically to handle MetaMask in Web3 testing. I tried it — the setup was too much for me :-) I encountered the same issue with the Agent Browser skill in Claude Code — it also can’t handle wallet extensions for the same reason: MetaMask runs as a separate process outside the reach of standard automation tools.\n\n**Second problem:** I didn’t want to “cement” behavior yet. The UI was constantly evolving. Writing deterministic E2E tests in Playwright would mean encoding specific system behavior: “click button X, expect text Y, check selector Z.” With every UI change — tests to rewrite. At this stage, I didn’t need regression yet as much as smoke tests.\n\nClaude Agent reads the Acceptance Criteria and verifies behavior, not selectors. When the UI changes, the agent simply looks at the new screen and evaluates whether the AC is satisfied — no fixtures to update. That gave me room to experiment: instead of investing in test infrastructure, I could simply see how far I could get.\n\n## The Solution\n\nThe key insight was simple: the agent needs eyes in the browser and hands on the blockchain. **Chrome DevTools MCP** gives it the first — it can navigate the dApp, take snapshots and screenshots, verify the UI. cast call and cast sendfrom the Foundry toolkit give it the second — it can inspect contract state and send transactions directly, without clicking through the UI. The missing link was the wallet: the solution turned out to be a custom Chrome profile with a pre-configured test MetaMask — the agent starts every session with a ready wallet.\n\nBut the most important part isn’t technical. The agent tests autonomously — I get an audio notification when it needs my interaction. In practice: I start a test session and go back to coding. Feedback comes to me; I don’t go looking for it.\n\n**The 15% That Still Requires My Hand:**\n\n***When testing through the browser UI:***\n\n* 2× transaction confirmations in MetaMask — the wallet extension popup isn’t accessible via Chrome DevTools; it requires a human click.\n\n***On a local testnet (Anvil):***\n\n* 1× round completion (make complete-draw) — on a real network (e.g., Sepolia), this is handled automatically by Chainlink nodes:\n\n## Two Agents — Two Contexts\n\nWhat started as a single **Claude Code** window quickly evolved into two.\n\n* Developer agent — in one Claude Code window, introduces frontend changes.\n* QA agent — in the other window, tests whether nothing was broken and reports bugs to a file.\n\n**Workflow: developer fixes → QA retests.**\n\nThe key advantage of this split isn’t just parallel work, but also **preserving each agent’s focus and context**. The developer agent operates exclusively in the context of code — it knows the architecture, change history, dependencies. The QA agent operates exclusively in the context of testing — it knows the Acceptance Criteria, the test protocol, previous results. Mixing these two types of tasks in one context window would degrade the quality of both.\n\nAn additional effect: each agent consumes a smaller context window because it processes only domain-specific information. This translates into lower costs and a smaller risk of the model “forgetting” important information.\n\n## Technical Conclusions\n\n* **A properly configured Chrome profile with a wallet extension** allows the agent to operate smoothly in UI tests.\n* cast call and cast send (tools from the Foundry toolkit) enable the agent to interact directly with the blockchain: reading state and sending transactions — independently of the UI.\n* **You don’t have to automate everything** — 85% automation with minimal manual work delivers real time savings and higher productivity.\n\nThis is still an experiment — the initial results are convincing, but I’m still building trust in the agent. Will it truly not miss regressions? Does it correctly interpret UI behavior in every case? These are natural questions with any new testing tool, not just AI. Next step: more sessions, more observations.\n\nNon-Obvious Conclusions: The Specification and Protocol Became the Test\n\nThere’s something about this approach that I only realized after several sessions.\n\nIn a traditional process, you write requirements (Acceptance Criteria in the PRD), and then *separately* write tests that verify those requirements. These are two artifacts that must stay synchronized — and often they don’t. Tests become outdated, AC evolves, something drifts apart.\n\nHere, the agent directly reads the AC and verifies behavior. There’s no translation layer — no **“now I’ll rewrite the requirement into a test case in code.”** The Given / When / Then from the User Story is simultaneously a test instruction. One artifact instead of two.\n\nThis changes the economics of writing good requirements. Usually, developers treat detailed AC as a formality — **“I know what to build anyway, why write it down.”** In this model, the quality of AC directly translates into the quality of testing. Well-written, precise AC = the agent tests accurately. Vague, generic AC = the agent guesses and may miss something.\n\nBut the specification alone isn’t enough. Alongside AC, you need a QA protocol — a file describing how the agent should test: when to mark AC as PASS vs FAIL, how to report bugs, how to handle edge cases. Traditionally, this knowledge lives in the tester’s head. Here, it’s written down in markdown.\n\nThe effect is non-obvious: every agent that reads this file becomes **“a junior QA who knows your standards.”** Expert knowledge is documented, reusable, and accessible to every new developer on the project — independently of AI.\n\n**Your PRD defines what to test. Your protocol defines how to test. Together, they replace routine verification — without writing a single line of test code.**"}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1772453916/Group_1000005089-3_uu5d84.png","lead":"**AI is everywhere** – but what’s it really like **on the frontlines of AI implementation**? Get into the **daily thoughts and challenges faced by AI engineers** – the real stuff that happens when **AI meets digital products**.\n\n**Weekly AI Bites** is a series that gives you **direct access to our day-to-day AI work**. Every post comes straight from our **team’s meetings and Slack**, sharing **insights, tests, and experiences** we’re applying to **real projects**. **What models are we testing, what challenges are we tackling, and what’s really working in products?** You’ll find all of this in our bites. Want to know **what’s buzzing in AI**? Check out **Boldare’s channels every Monday** for the latest **weekly AI Bite**. Let’s dive into the full article.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-03-02T12:13:16.510Z","slug":"specification-becomes-test-ai-qa-blockchain-dapp","type":"blog","slugType":null,"category":null,"additionalCategories":["GenAI","How to","Ideas","News","People"],"url":null},"author":"Karol Kasprzak","authorAdditional":"","box":{"content":{"title":"When the Specification Became the Test: AI as a QA Agent in a Blockchain dApp","tileDescription":"Discover how an AI QA agent tests a blockchain lottery dApp autonomously, turning specifications into real-time tests while reducing manual effort.\n","coverImage":""},"coverImage":null}},"id":"c0c982bd-12be-56dc-bcde-f1d8ecd564fc"}},{"node":{"excerpt":"","fields":{"slug":"/blog/what-makes-a-software-development-partner-truly-ai-native-a-2026-guide-for-ctos/"},"frontmatter":{"title":"What makes a software development partner truly AI-native? A 2026 guide for CTOs","order":null,"content":[{"body":"## What \"AI-Native\" actually means (and what it doesn't)\n\nThe market has three meaningfully different levels of AI adoption, often mixed in vendor pitches:\n\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
LevelWhat it means in practiceWhat it delivers
AI Assisted\n Individual team members use tools like ChatGPT or Copilot, but there's no systemic approach.\n \n Productivity shortcuts, not transformation.\n
AI Enhanced\n AI tools are integrated into workflows (e.g. automated code review, AI meeting notes), but processes and org structure are unchanged.\n \n Efficiency gains within existing ways of working.\n
AI Native\n AI actively changes how decisions are made and work is done - surfacing insights, generating outputs, driving processes in ways not possible before.\n \n New quality of output unreachable without AI.\n
\n
\n\n**The critical distinction**: \n\n* AI Enhanced extends what humans do. \n* AI Native creates new ways of doing things. \n\nA partner using Copilot for code suggestions and calling themselves \"AI-powered\" is not the same as a partner who has restructured their delivery model around AI at every layer.\n\nBoldare is on the road to AI-native — and has been walking it long enough to guide clients through the same journey. That distinction matters: a partner who claims to have *arrived* is either overstating their position or doesn't understand how fast the landscape moves.\n\n## Why a one-time AI implementation isn't enough\n\nThe biggest risk scaleups face is choosing a partner whose relationship with AI is static. AI development has no finish line, so what's best practice today may be obsolete in six months.\n\nBoldare's framing on this is direct: AI Native is not a destination. It's a road. A genuine AI-native partner doesn't hand you a finished solution and close the project. They bring you along as the landscape evolves – experimenting, adjusting, and transferring the skills your organization needs to keep adapting independently.\n\nThis mirrors how Boldare itself has evolved: from pioneering Agile adoption, through product-oriented development, and now actively transforming into AI-native delivery. Each stage wasn't completed before moving to the next – it was, and still is, a continuous shift in how the organization thinks and works. That collective memory of being mid-transformation (not just having read about it) is what we bring to our clients.\n\n## Five things to evaluate before choosing a partner\n\n**1. Are they honest about where they are on the journey themselves?**\n\nThe most revealing question is \"where are you on the path, and what have you learned so far?\" Partners who claim to have fully arrived are red flags. We can point to specific examples of what we've implemented  **–** agents that scan call transcripts and surface recurring problems, a shared \"superbrain\" that gives every team member access to accumulated project knowledge **–** while being frank that this transformation is ongoing, not complete.\n\n**2. Are they honest about what they don't know?** \n\nThere is no complete playbook for AI-native transformation – and any partner claiming otherwise is either behind the curve or overpromising. We don't have a finished roadmap either and we're straight about that. The nature of AI-Native is continuous experimentation, and a partner who's living that reality is better positioned to guide you through it than one presenting a polished deck of certainties.\n\n**3. Do they distinguish between mindset and toolset?**\n\nVendors selling tools talk about tools. Partners on the AI-native journey talk about how your organization will think differently. Our position is explicit: **the value isn't the tools themselves** but how we change the way your organization thinks about processes, decisions, and possibilities. Tools evolve constantly, while the thinking we implent should outlast any specific technology.\n\n**4. What's their track record of organizational change, not just delivery?**\n\nAI-native transformation is closer to Agile adoption than a software project. We have over 20 years of experience leading clients through each major paradigm shift in how software and products are built – Waterfall to Agile, Agile to product-oriented, and now toward AI-native. That change management capability (earned through lived experience, not just methodology decks) is what this work actually demands.\n\n**5. Do they have a model for your current maturity level?**\n\nA good partner meets you where you are. We explicitly address all three levels – AI Assisted, AI Enhanced, and AI Native – with differentiated support for each. Whether your team is still experimenting with ChatGPT individually or already running integrated AI workflows, our engagement model adapts to your starting point and moves you forward from there.\n\n## Red flags in partner pitches you should look out for\n\nWatch out for these signals that a vendor's AI positioning is surface-level:\n\n* They describe AI as something they'll \"implement for you\" (framing it as a one-time delivery)\n* They can't give specific examples of how AI changed their own team's work\n* They offer a fixed AI roadmap without acknowledging how fast the landscape changes\n* Their pitch focuses on specific tools rather than building your organizational capability to keep adapting\n* They promise specific AI-driven outcomes rather than the ongoing capacity to experiment\n\n## FAQ\n\n**Q: What's the difference between AI-enhanced and AI-native in vendor terms?** \n\nA: An AI-enhanced vendor has integrated AI tools into their workflows but delivers fundamentally the same kind of work as before, just faster. A partner on the AI-native path is actively restructuring how they work around AI. The outputs, the process, and the quality of thinking are changing in ways that weren't possible before — and that process of change is itself what Boldare brings to clients.\n\n**Q: How can I verify a vendor is genuinely on the AI-native journey and not just claiming it?** \n\nA: Ask them to walk you through how AI has changed a specific internal process in their own organization – not a client case study, but their own team. Ask what didn't work. Organizations genuinely on this journey can name the exact workflows that changed, what they tried that failed, and what they learned.\n\n**Q: Do I need to be AI-ready to start working with an AI-native partner?** \n\nA: No. Our model is explicitly designed to engage clients at whatever maturity level they're currently at and guide them forward. The starting point matters less than choosing a partner built to move with you as the landscape keeps shifting.\n\n\n\n\n\nAI-native delivery is a continuous journey, not a project to complete. The right partner isn't the one who claims to have mastered AI on day one but the one honest enough to say they're still learning, experienced enough to have learned a lot, and built to keep adapting as the landscape shifts. That's what we're on the road to being at Boldare **–** and why that road is worth walking together."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1771936236/blog_vywsgo.png","lead":"If you're a scaleup evaluating software development partners in 2026, nearly every vendor will claim to be \"AI-powered.\" Far fewer are genuinely on the path to AI-native. The difference matters more than most clients realize **–** and getting it wrong means paying for a one-time implementation that turns out to be outdated before your next funding round.\n\nThis article defines what AI-native actually means, how to assess it, and what our approach at Boldare looks like in practice.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-02-24T14:21:42.333Z","slug":"ai-native-delivery-partner-guide","type":"blog","slugType":null,"category":null,"additionalCategories":["GenAI","How to","Strategy"],"url":null},"author":"Aleksander Dąbrowski","authorAdditional":"","box":{"content":{"title":"What makes a software development partner truly AI-native? A 2026 guide for CTOs","tileDescription":"Learn what separates a delivery partner genuinely on the AI-native journey from vendors that just use AI tools - and what questions to ask before you sign.","coverImage":""},"coverImage":null}},"id":"10b2fa16-fb8f-5a7a-8b72-28a17c59ab92"}},{"node":{"excerpt":"","fields":{"slug":"/blog/claude-code-experts-why-does-ai-fail-in-java-teams-insights-from-maciej-krol/"},"frontmatter":{"title":" Claude Code Experts: Why does AI fail in Java teams? Insights from Maciej Król","order":null,"content":[{"body":"### Why AI Often Fails in Enterprise Java Systems\n\nDespite the hype, AI often struggles in large, long-lived enterprise systems. These applications have evolved over years, serve real customers, and manage sensitive information, meaning the cost of even minor errors can be extremely high.\n\n> “The truth is that AI rarely delivers the magical results people promise… these systems have ‘tribal knowledge’ built into them over years. The risk of making a mistake is really high.” – Maciej Król\n\nKey challenges include:\n\n* **System Complexity:** Enterprise Java systems are modular, interconnected, and often require strict performance and stability.\n* **Hidden Knowledge:** Tacit knowledge is embedded in the codebase, making it difficult for AI to fully understand critical logic.\n* **High Stakes:** A minor mistake can lead to substantial financial losses.\n\nAs Maciek notes, “Sometimes AI can do something really fast, but breaking things can cost us a lot of money.” In other words, speed alone is not enough to guarantee success.\n\n### The Biggest Myth About AI in Backend Development\n\nA common misconception is that AI will automatically accelerate the entire development process. In reality, AI primarily speeds up **code generation**, while shifting the real bottleneck to review and verification.\n\n> “Right now, it’s not me spending hours writing code… I’m spending this time reviewing, planning, and designing the solution together with AI.” – Maciej Król\n\nThis human-in-the-loop approach ensures correctness, safety, and alignment with business requirements, but it also requires developers to adapt to new responsibilities.\n\n### Human-in-the-Loop: The Critical Factor\n\nAI does not replace human developers; it changes their role from “creator” to “auditor” or “operator.” This transformation introduces new challenges:\n\n* **Review Fatigue:** Constantly reviewing AI-generated code can be exhausting.\n* **Over-Trusting AI:** While AI often generates 90–99% correct code, the remaining 1% can be critical.\n* **Reduced Team Collaboration:** Working primarily with AI can weaken team dynamics and reduce human-to-human interaction.\n\n> “It can easily turn that teams are deteriorating… because you are just partnering with the AI.” – Maciej Król\n\nDevelopers must remain vigilant and retain responsibility for the products they deliver, regardless of AI assistance.\n\n### AI as a Development Partner in the IDE\n\nThe most significant benefits of AI appear when it’s integrated directly into the IDE. Maciek uses **IntelliJ with Claude Code** as part of his daily workflow, treating AI as a co-developer rather than a replacement.\n\n> “The IDE has turned into a partner… If I don’t understand some complex logic, it can explain it to me. I’m designing the whole solution in the IDE now.” – Maciej Król\n\nBenefits include:\n\n1. **Cold Start Problem Solved:** AI quickly identifies where to focus in large, unfamiliar codebases.\n2. **Context Awareness:** In well-structured systems, AI maintains architectural patterns and consistency.\n3. **Co-Creation of Solutions:** Developers collaborate with AI, reviewing and comparing multiple alternatives.\n\n> “Every time I’m looking at code, AI is sitting together with me, looking at the same stuff. It’s like pair programming, but the partner is AI.” – Maciej Król\n\nHowever, AI only excels in systems with good architecture. In poorly structured or legacy codebases, AI may amplify existing problems rather than solve them.\n\n> “If the system is already a mess… \\[AI] just multiplies this mess.” – Maciej Król\n\n### Daily Workflow with AI\n\nIn practical terms, AI changes how Java developers spend their days:\n\n* Reviewing AI-generated code instead of writing everything manually.\n* Verifying correctness, safety, and compliance of proposed solutions.\n* Using AI to explore alternatives for complex logic.\n* Debugging faster by leveraging AI insights on stack traces and error locations.\n\n> “The problem of the cold start is removed… AI tools are better at finding the codebase than I am, especially in major systems I can’t know completely by heart.” – Maciej Król\n\nWhile code generation is faster, responsibility, judgment, and critical thinking remain essential.\n\n### AI-Native vs. Legacy Systems\n\nAI-native systems are designed to integrate seamlessly with AI from the start. Converting legacy Java systems to be AI-friendly is possible but expensive. For greenfield projects, AI-native design is more achievable but still requires skilled engineers and careful architecture.\n\n> “It depends. Is it worth to turn your legacy system to be AI-friendly, or maybe it’s more worth to start from scratch?” – Maciej Król\n\n### Benefits and Limitations of AI Tools like Claude Code\n\n**Benefits:**\n\n* Rapid code generation.\n* Context-aware suggestions in structured systems.\n* Guidance for debugging and planning tasks.\n\n**Limitations:**\n\n* Effectiveness depends on the developer’s skills and setup.\n* Large systems require extra effort to maintain AI context.\n* AI amplifies mistakes in poorly structured or undocumented codebases.\n\n> “On day one, your results will depend mostly on how your codebase is structured… the bigger the system, the more effort you need to guide the AI.” – Maciej Król\n\n### Key Takeaways\n\nAI in Java backend is **a powerful partner, not a replacement**. Success depends on:\n\n* Skilled human operators who understand AI’s limitations.\n* Well-structured, documented, and maintainable systems.\n* Awareness of new bottlenecks, such as code review fatigue.\n* Clear operational guidelines for safe AI adoption.\n\n**Real gains:** faster coding, AI guidance in debugging, improved starting points in complex systems.\\\n**Risks:** fatigue, over-trust, amplified chaos in legacy systems, and reduced human collaboration.\n\n> “The real bottleneck has shifted… it’s now about reviewing and verifying AI-generated solutions. Developers are still responsible for the products.” – Maciej Król\n\nAI adoption is a balancing act. It accelerates workflows, provides valuable insights, and reduces repetitive work—but it demands vigilance, expertise, and careful integration into production systems."}],"job":null,"photo":null,"slug":null,"cover":"","lead":"Imagine this: you’re a senior Java developer in a large enterprise, working on a system that has been evolving for years. Your code is critical, handling sensitive data and real money transactions. Then, the team introduces AI to “speed things up.” Sounds like a dream, right? Fast-forward a few weeks: the code is flying in, but human oversight has become heavier, fatigue is real, and even a small error could cost hundreds of thousands.\n\nWelcome to the reality of AI in Java backend development. While demos promise magical results, real-world production environments reveal a more nuanced picture—one where AI is a powerful partner, but far from a miracle worker.\n\nIn this post, we’ll explore the promises, pitfalls, and practical realities of integrating AI into complex Java systems, based on insights from an in-depth conversation with Maciek Król.\n\n","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-02-20T14:02:02.453Z","slug":"claude-code-ai-failures-java-teams","type":"blog","slugType":null,"category":null,"additionalCategories":["How to","GenAI","Ideas","Tech"],"url":null},"author":"Roksana Kaczmarska","authorAdditional":"","box":{"content":{"title":" Claude Code Experts: Why does AI fail in Java teams?","tileDescription":"Explore why AI often fails in complex Java backend systems and how to make it production-ready. Insights from Maciek Kruhl on Claude Code, human-in-the-loop workflows, and best practices for enterprise teams.","coverImage":"https://res.cloudinary.com/de4rvmslk/image/upload/v1774274665/Group_1000005442_tvwqbc.png"},"coverImage":null}},"id":"68f43d24-d391-5618-9c16-02f23579166d"}},{"node":{"excerpt":"","fields":{"slug":"/blog/common-api-performance-bottlenecks-in-enterprise-systems-and-how-to-fix-them-2026-guide/"},"frontmatter":{"title":"Common API performance bottlenecks in enterprise systems and how to fix them (2026 Guide)","order":null,"content":[{"body":"## Why API performance is more than just a tech detail\n\n### In enterprise systems, APIs power:\n\n* Web and mobile frontends\n* Partner integrations\n* Internal microservices\n* Data pipelines and AI workloads\n\n### So, slow APIs mean:\n\n* Lower conversion \n* Broken SLAs with partners\n* Higher infra cost\n* Angry enterprise clients who escalate fast\n\nAs such, performance needs explicit SLOs, for example:\n\n
MetricExample SLO
P95 latency per endpoint< 250 ms
Error rate< 0.5%
Uptime99.9%+
Cache hit ratio> 80% (read-heavy endpoints)
\n\nWithout targets, teams optimize blindly. That's why in our product delivery process, performance metrics are defined during planning - not after production incidents.\n\n## Heavy payloads and chatty APIs\n\n### The problem:\n\n* Overfetching huge JSON responses\n* Underfetching that forces multiple calls\n* No pagination\n* Multiple round-trips for one screen\n\n### How to fix it:\n\n* Design endpoints around real user use cases\n* Use lean DTOs and projection parameters\n* Enforce pagination by default\n* Prefer cursor-based pagination for large datasets\n* Use compression and efficient internal service formats\n\n## Database as the primary bottleneck\n\n### The problem:\n\n* Missing indexes\n* N+1 queries\n* Complex JOINs on hot tables\n* Everything hitting the primary DB\n\n### How to fix it:\n\n* Profile real production queries\n* Add indexes based on actual traffic\n* Remove N+1 with batching\n* Introduce read replicas for heavy reads\n* Put caching in front of the DB\n\n## Weak or non-existent caching\n\n### The problem:\n\n* No caching strategy\n* Cache implemented at the wrong layer\n* Broken invalidation\n* Low cache hit ratio\n\n### How to fix it:\n\n#### Use multi-layer caching:\n\n* CDN for public APIs\n* Reverse proxy cache\n* In-memory cache for hot reads\n* Application-level caching\n\n#### Define:\n\n* Clear TTLs\n* Explicit invalidation rules\n* Ownership and monitoring\n\n## Network and transport overhead\n\n### The problem:\n\n* Cross-region latency\n* No CDN\n* Inefficient connection reuse\n* Missing compression\n\n> *In distributed systems, network overhead can account for a large portion of total latency.*\n\n### How to fix it:\n\n* Use HTTP/2 or HTTP/3\n* Enable keep-alive and connection reuse\n* Optimize TLS handshakes\n* Introduce CDN for edge traffic\n\n## Blocking code paths\n\n### The problem:\n\n* Long-running synchronous tasks in request flow\n* No async processing\n* Heavy serialization\n* Poor concurrency model\n\n> *If a request triggers file processing, report generation or multiple external calls synchronously, latency explodes under load.*\n\n### How to fix it:\n\n* Move heavy work to background jobs\n* Use event-driven patterns\n* Implement non-blocking I/O\n* Profile CPU and memory hot paths\n\n**In our AI-native delivery model, we use performance profiling early, not after production failures.**\n\n## Why enterprise API performance needs a systemic approach\n\nAPI bottlenecks are architecture, process, and product maturity problems - rarely isolated defects. \n\nThe difference between average and high-performing enterprise systems is more than just better code:\n\n* Clear SLOs\n* Structured performance audits\n* Continuous measurement\n* AI-supported profiling and refactoring\n* A delivery system that treats performance as a core requirement\n\nIf your enterprise API needs to scale globally, handle AI workloads, or support thousands of concurrent users, performance must be measured and improved continuously.\n\nThis is typically where experienced product and engineering teams step in. At Boldare, API performance optimization projects usually begin with a [system-level audit](https://www.boldare.com/services/code-audit/): traffic patterns, [query profiling](https://www.boldare.com/services/consulting-and-scaling), dependency mapping, load simulations, and SLO definition. From there, improvements are prioritized based on business impact.\n\n"}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1771326762/blog_nxbby1.png","lead":"**Quick** **answer:** In 2026, API performance issues in enterprise systems are usually a toxic mix of heavy payloads, slow databases, weak caching, chatty integrations, and zero observability. The fix is treating performance as a product feature with clear Service Level Objectives (SLOs) and continuous measurement.\n\nIf you think your API is “fine” but occasionally spikes to 2-3 seconds under load, this article is for you.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-02-17T10:00:54.242Z","slug":"enterprise-api-performance-bottlenecks-and-practical-fixes","type":"blog","slugType":null,"category":null,"additionalCategories":["Tech","How to"],"url":null},"author":"Aleksander Dąbrowski","authorAdditional":"","box":{"content":{"title":"Enterprise API Performance Bottlenecks and Fixes","tileDescription":"Explore the most common enterprise API bottlenecks, from N+1 queries to network latency, and learn how to improve performance with measurable SLOs.","coverImage":""},"coverImage":null}},"id":"a9c145b6-421c-518e-ab0f-c8f4a6f8aed5"}},{"node":{"excerpt":"","fields":{"slug":"/blog/clutch-names-boldare-among-top-php-developers-in-poland-for-2026/"},"frontmatter":{"title":"Clutch names Boldare among Top PHP developers in Poland for 2026","order":null,"content":[{"body":"### Delivering scalable PHP solutions\n\nBoldare specializes in building robust web platforms and tailored digital products powered by modern PHP ecosystems. From scalable backend architectures to high-performance applications, the team focuses on solutions that support business growth and product evolution.\n\nThis recognition highlights:\n\n* consistent delivery of reliable and maintainable PHP-based solutions,\n* strong client feedback and market reputation,\n* proven experience in developing complex web platforms across industries.\n\n\n\n### A recognition driven by client success\n\nAt Boldare, awards are viewed as a reflection of client outcomes rather than internal milestones. The acknowledgment from [Clutch](https://clutch.co/profile/boldare#portfolio-and-awards) reinforces the value of collaborative product development and iterative delivery approaches that prioritize measurable results.\n\nThe team extends sincere thanks to clients who shared their feedback and experiences — their trust and partnership made this achievement possible.\n\n### Looking ahead\n\nBoldare continues to invest in engineering excellence and modern PHP-driven architectures to help organizations design and scale impactful digital products. Recognition like this motivates the team to further expand capabilities and take on new technological challenges.\n\n [Contact us](https://www.boldare.com/contact/) and let’s hear all about your ideas and your business needs."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1771247008/Group_1000005056_cxkdwl.png","lead":"Once again, Boldare has been recognized for its technical expertise and delivery excellence. This time, the company has been awarded the title of **Top PHP Developers in Poland for 2026** by [Clutch](https://clutch.co/profile/boldare#portfolio-and-awards) — a distinction that reflects the quality of digital products delivered to clients and the measurable impact achieved through long-term collaboration.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-02-16T13:02:51.654Z","slug":"boldare-top-php-developers-poland-2026","type":"blog","slugType":null,"category":null,"additionalCategories":["News"],"url":null},"author":"Roksana Kaczmarska","authorAdditional":"","box":{"content":{"title":"Clutch names Boldare among Top PHP developers in Poland for 2026","tileDescription":"Boldare has been recognized by Clutch as one of the Top PHP Developers in Poland for 2026, highlighting expertise in scalable web platforms and digital product development.","coverImage":""},"coverImage":null}},"id":"97d0075b-95bb-5972-8e71-747659b7bbfb"}},{"node":{"excerpt":"","fields":{"slug":"/blog/this-weeks-ai-bite-openclaw-in-practice-insights-from-a-week-of-testing/"},"frontmatter":{"title":"This week's AI Bite: OpenClaw in Practice – Insights from a Week of Testing","order":null,"content":[{"body":"## Test Objective and Use Case\n\nAs a software developer, I need an efficient way to distribute content. My goal was to create a fully autonomous system managing presence on X (formerly Twitter) and Reddit. At this stage, I wasn't focusing on advertising campaigns – it was solely about organic engagement building and follower growth using completely autonomous accounts.\n\n## Setup – Basic Configuration\n\nI started with a simple configuration:\n\n* VPS at $8 per month\n* Kimi 2.5 model via OpenRouter for agent management\n* Integrations with Discord and Telegram for communication and reporting\n\nFirst impressions? I was amazed. The system literally \"came alive\" before my eyes. I managed to configure the agent to:\n\n* Operate the browser independently\n* Read and analyze posts\n* Research communities and topical niches\n* Respond to other users' content\n* Learn writing styles and adapt communication\n\n**Important technical note:** All operations had to go through the browser on the VPS. Attempts to use the API directly resulted in the bot being flagged as spam and having its post reach limited.\n\n## How Did the System Work?\n\nThe agent's workflow was thoughtful and quite impressive:\n\n**Every 3 hours:**\n\n* Conducted research on posts, communities, and trends\n* Checked notifications\n* Generated drafts for posts and comments/responses\n\n**Publishing:**\n\n* All drafts came to me for approval\n* After my feedback, the agent took notes and improved future versions\n* The system learned based on my comments (self-learning based on feedback)\n\n**Reporting:**\n\n* Daily at 9:00 AM, the agent sent an activity report via Discord\n* The report included a summary of actions, progress, and follower growth\n\n**Operating cost:** Managing two accounts cost approximately $3 per 24 hours of operation.\n\n## When Problems Started\n\nUnfortunately, all good things come to an end quickly. After the second day, the first signs appeared that something was going wrong:\n\n## Technical Issues:\n\n* **Frequent browser timeouts** – the agent lost connection with the session\n* **Significant agent hallucinations** – incorrect outputs, misinterpretation of tasks\n* **Disconnections from Discord and Telegram** – ultimately leading to complete system failure\n\n## Main Challenges:\n\n**1. \"Yes, I'm doing it!\" – Empty Promises Syndrome**\n\nThe agent often responded: *\"Yes, I'm doing it, sending it now\"*, but after five minutes, when I asked if the task was completed, I heard: *\"Not yet, I'm working on it now\"*. This communication inconsistency was frustrating and made monitoring progress difficult.\n\n**2. OpenClaw Configuration Problems**\n\nThe installation itself is relatively straightforward, but integration with Discord and Telegram proved to be a real challenge:\n\n* Frequent disconnections\n* System scaling issues\n* The agent could corrupt the config, leading to errors like:\n\n```\nInvalid config at /root/.openclaw/openclaw.json\n```\n\nIn such cases, a complete VPS reinstallation was necessary. **This happened to me about 50 times** during the week-long test. Yes, you read that right – 50 reinstallations.\n\n**3. Tool Execution Issues**\n\nEven with a well-designed system, the agent often \"forgot\" basic boilerplate rules and configuration settings. This required constant monitoring and intervention.\n\n## Conclusions: Is It Worth It?\n\nOpenClaw is undoubtedly **a very promising tool**. I agree with the opinion that those not following its development risk falling behind. Autonomous agent technology is the future of marketing automation.\n\n**However...**\n\nIn its current form, for simple browser-based automation (especially in non-standard use cases), the tool still requires refinement.\n\n## Who Is This Solution For?\n\n**Perfect for:**\n\n* Early adopters ready to experiment\n* Teams with resources for debugging and system maintenance\n* Projects where occasional errors can be tolerated\n\n**Less suitable for:**\n\n* Production environments requiring 100% stability\n* Teams without technical background\n* Projects with limited budget for iterations and fixes\n\n## What's Next?\n\nDespite the difficulties, I see enormous potential in this technology. When OpenClaw achieves greater stability, it could truly become a game-changer in content marketing automation. For now, however, be prepared for:\n\n* Regular debugging\n* Patience in solving problems\n* Time investment in monitoring and optimization\n\nWill I continue testing? Absolutely. Do I recommend you start right now? It depends on your risk appetite and willingness to deal with the imperfections of evolving technology."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1771243970/Group_1000005065-3_udruzs.png","lead":"Over the past week, I've been intensively testing **OpenClaw** – a tool that's supposed to revolutionize content marketing automation. Is it just another hype, or truly a breakthrough solution? The answer, as is often the case in the AI world, is more complex than a simple \"yes\" or \"no\".\n\nToday in **Weekly AI Bites**, I'll take you behind the scenes of my week-long experiment. You'll discover what happens when you unleash an AI agent on social media, what surprises (both good and... less pleasant) await you, and whether it's worth investing time and money in this technology right now.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-02-16T09:44:49.719Z","slug":"openclaw-in-practice-weekly-test","type":"blog","slugType":null,"category":null,"additionalCategories":["GenAI","Future","Tech","How to"],"url":null},"author":"Maksymilian Mogilski","authorAdditional":"","box":{"content":{"title":"This week's AI Bite: OpenClaw in Practice – Insights from a Week of Testing","tileDescription":"Discover how OpenClaw leverages AI to automate social media. Insights from a week of testing autonomous content agents, trend analysis, and engagement.","coverImage":""},"coverImage":null}},"id":"aa3ec42b-a5f8-5a3c-955a-f269b880081b"}},{"node":{"excerpt":"","fields":{"slug":"/blog/cicd-optimization-vs-inhouse-devops-enterprise/"},"frontmatter":{"title":"CI/CD optimization services vs in-house DevOps – what makes more sense in enterprise environment?","order":null,"content":[{"body":"## Why does CI/CD optimization vs in-house DevOps decision matter?\n\nIn enterprise environments, CI/CD directly influences: \n\n* release velocity \n* cloud cost control \n* security posture \n* developer productivity \n* mean time to recovery (MTTR)\n\nSo the question here isn't \"internal or external DevOps?\" It's: Who should own and optimize your delivery system?\n\n## Option 1: In-house DevOps\n\nDelivery capability is always strategic in enterprise. The real question is whether you want to build, accelerate, or co-own that capability.\n\n### Why enterprises choose the in-house route:\n\n* Regulatory or authority constraints require full internal control \n* Complex legacy systems demand deep contextual knowledge \n* DevOps speed is a competitive differentiator, e.g. high-growth SaaS \n* Leadership is investing in platform engineering as a long-term capability\n\n### In-house DevOps strengths:\n\n**1. Architectural ownership** \n\nInternal teams control pipeline logic, infrastructure standards, access policies, and release flows. Decisions align tightly with business and product strategy.\n\n**2. Business-aware automation** \n\nEngineers embedded in product teams understand domain-specific risks, dependencies, and release constraints.\n\n**3. Internal Developer Platforms (IDPs)** \n\nPlatform engineering enables self-service environments, standardized templates, and guardrails. When executed well, this significantly boosts developer productivity without external dependencies.\n\n**4. Strategic independence** \n\nNo contractual dependencies, meaning full autonomy over infrastructure and tooling choices.\n\n### In-house DevOps challenges:\n\n* High SRE hiring costs \n* Retention challenges for senior DevOps talent \n* 24/7 operational coverage requirements \n* Continuous optimization effort after initial setup\n\n## Option 2: CI/CD optimization services\n\nAlso known as DevOps-as-a-Service or simply managed DevOps.\n\n### Reasons businesses choose CI/CD optimization services\n\n* Scaling multiple product lines \n* Migrating to cloud-native \n* Need for infra cost optimization \n* Need faster time-to-market \n* DevOps hiring slows down delivery\n\n### CI/CD optimization strengths:\n\n**1. Faster optimization cycles** \n\nSpecialized providers bring battle-tested automation for: \n\n* Kubernetes orchestration \n* GitOps workflows \n* FinOps cost control \n* Observability patterns\n\nBecause of this, you skip experimentation phases.\n\n**2. Infrastructure savings** \n\nEnterprises commonly see: \n\n* Infrastructure cost optimization \n* Automated scaling \n* Cleaner environments\n\n**3. Lower Mean Time To Repair (MTTR)** \n\nExternal teams often implement: \n\n* Standardized incident response playbooks \n* 24/7 monitoring \n* Automated rollback strategies\n\n**4. Focus shift** \n\nInstead of maintaining pipelines, your internal teams can focus on: \n\n* Product innovation \n* Architecture evolution \n* AI integration \n* Business value delivery\n\n## The 2026 reality: Platform engineering + hybrid model\n\nThe current enterprise trend is: Internal platform engineering + external CI/CD optimization layer\n\nWhat does that look like?\n\n
Responsibility Internal Team External Partner
Internal Developer Platform (IDP) vision Yes Advisory
CI/CD optimization Shared Yes
Cloud cost governance Shared Yes
Observability tuning Shared Yes
Incident escalation Yes SLA-backed support
\n\nPlatform engineering teams define the system, while external experts continuously optimize it. \\\nIn advanced enterprise setups, external partners like Boldare often co-design platform architecture and introduce AI-powered delivery systems, rather than merely optimizing pipelines.\n\n### Common enterprise mistake\n\nTrying to build everything internally while underestimating: \n\n* hidden operational costs \n* tooling sprawl \n* skill gaps in GitOps, FinOps, Kubernetes \n* incident fatigue\n\n## Comparison\n\n
Dimension In-House DevOps CI/CD Optimization Services Hybrid Model (2026 Trend)
Strategic Control Full ownership of architecture, pipelines, and security Shared or partially externalized Internal strategic control, external optimization
Speed of Implementation Slower – depends on hiring and internal bandwidth Faster – proven frameworks and automation playbooks Fast optimization without losing ownership
Infrastructure Cost Efficiency Often optimized once, then plateaus Continuous cost optimization (FinOps practices) External cost governance + internal visibility
Talent Dependency High reliance on senior SRE/DevOps hires Access to cross-industry expertise Reduced hiring pressure
Scalability Across Products Requires internal scaling of team Designed for multi-product scaling Scalable with strategic alignment
Compliance & Data Sovereignty Strong – full internal governance Needs careful contract design Compliance owned internally
Innovation Focus DevOps team may get stuck in ops firefighting Internal teams freed for product innovation Balanced operational load
24/7 Monitoring & Incident Response Expensive to maintain internally Built-in SLA-backed monitoring Shared responsibility
Vendor Lock-In Risk Low Medium – depends on architecture ownership Low if platform engineering remains internal
Long-Term Cost Structure High fixed cost (salaries, tooling) Variable cost, potentially lower TCO Optimized blended model
Best For Regulated enterprises, DevOps as strategic moat Fast-scaling cloud-native enterprises Mature enterprises scaling multiple product lines
\n\n## Final verdict\n\nIn 2026, the smartest enterprise approach is rarely pure in-house or pure outsourcing.\n\nIt's: \n\n* Internal platform engineering ownership \n* External CI/CD optimization expertise \n* Clear SLAs and measurable DevOps KPIs\n\nBuild strategic capabilities internally and optimize tactically with experts. That's how you move from \"it works\" to \"it scales.\"\n\n## FAQ\n\n**1. Why are enterprises adopting a hybrid DevOps model?**\n\nMost enterprise environments are moving toward a hybrid model that combines internal platform ownership with external optimization expertise.\n\nIn this structure:\n\n* Internal platform engineering teams define standards, governance, and architectural direction\n External CI/CD specialists continuously improve automation, scalability, cost control, and observability\n Clear KPIs and SLAs align both sides around measurable performance outcomes\n\nThis approach reduces operational overload, accelerates maturity, and prevents tool sprawl while maintaining strategic control.\n\n**2. What is the difference between in-house DevOps and CI/CD optimization services?**\n\nIn-house DevOps means building and maintaining your own internal team responsible for CI/CD pipelines, infrastructure automation, reliability, and operational standards.\n\nCI/CD optimization services, often delivered as managed DevOps or DevOps-as-a-Service, involve external experts who improve, automate, and continuously optimize your delivery pipelines and cloud infrastructure.\n\n**3. How can enterprises adopt CI/CD optimization services without losing strategic control?**\n\nAdopting CI/CD optimization services does not mean giving up ownership of your delivery system.\n\nIn a well-structured engagement:\n\n* The enterprise retains architectural authority and governance\n* Internal platform or engineering leaders define standards and priorities\n* External experts optimize automation, scalability, reliability, and cost efficiency\n* Knowledge transfer and documentation ensure long-term transparency\n\nIn practice, this often means working with partners experienced in platform engineering, DevOps transformation, and AI-enhanced delivery. For example, [Boldare supports enterprises ](https://www.boldare.com/services/devops-consulting-services/)by combining consulting, dedicated DevOps teams, and AI-native development processes, while ensuring architectural ownership always remains internal."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1770989972/Blog_post_cdjf5k.png","lead":"In enterprise environments, CI/CD directly influences release velocity, cloud cost control, security posture, developer productivity, and mean time to recovery. Discover whether in-house DevOps, CI/CD optimization services, or a hybrid model makes more sense for your organization in 2026.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-02-13T12:00:00.000Z","slug":"cicd-optimization-vs-inhouse-devops-enterprise","type":"blog","slugType":null,"category":null,"additionalCategories":["Strategy","Tech"],"url":null},"author":"Aleksander Dąbrowski","authorAdditional":null,"box":{"content":{"title":"CI/CD optimization services vs in-house DevOps – what makes more sense in enterprise environment?","tileDescription":"Explore the pros and cons of in-house DevOps vs CI/CD optimization services for enterprises. Learn about the hybrid model trend in 2026.","coverImage":null},"coverImage":null}},"id":"350e16ca-f051-5c75-b03f-d5f691a26ca4"}},{"node":{"excerpt":"","fields":{"slug":"/blog/how-mcp-server-eliminates-operational-bottlenecks/"},"frontmatter":{"title":"How MCP Server eliminates operational bottlenecks for scaling companies? Case study","order":null,"content":[{"body":"## The 15-hour problem every scaling company faces\n\nPicture this: It's Monday morning, and **your CMO needs to update the pricing page before a major product announcement**. The change itself is simple: a headline, a new pricing tier, maybe a promotional banner. But the process? Often anything but simple.\n\nFirst, there's a ticket. Then waiting for developer capacity. Code review. Deployment pipeline. Testing on staging. Finally, production. By the time the change goes live, it's Thursday afternoon, and the launch momentum is gone.\n\nMeanwhile, **your CEO is preparing for a board meeting**. They need revenue metrics from Stripe, customer acquisition costs from Google Analytics, pipeline data from Salesforce, engineering velocity from Jira, and burn rate from your financial system. Each platform requires a login, navigation through dashboards, manual export, and then the real time sink: correlating everything into a coherent story.\n\nFour hours later, they have a presentation.\n\nThis isn't hypothetical. **Research shows C-level executives at fast-growing digital companies spend 15-25 hours weekly on operational overhead: context-switching between tools and manually compiling information that should be instantly accessible.**\n\nWe present an AI-powered solution: **a case study implementing an MCP server that eliminates operational bottlenecks for scaling companies**. Our implementation shows how we shortened the time to add new articles to our website to minutes. But this is just one example to show how an MCP server can simplify processes at your company.\n\n## What is MCP Server?\n\nMCP Server (Model Context Protocol Server) is a protocol that allows AI systems like Claude to connect to your business tools and act on your behalf. Simply put: MCP is a bridge between artificial intelligence and your company's systems.\n\n### How does it work in practice?\n\n
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MCP Server Comparison

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\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Without MCP ServerWith MCP Server
\n You talk to AI (like Claude). AI can only advise based on its training knowledge\n \n You talk to AI. AI has access to your systems and can retrieve actual data\n
\n You want to change something on your website. You must manually log into your CMS, find the right section, make changes, save, deploy\n \n You ask for a website change. AI makes the change through the appropriate tool (like Netlify)\n
\n You ask about data from different tools. You must manually access each system, export data, compile it in a spreadsheet\n \n You ask for data. AI connects to your systems (Analytics, CRM, financial tools) and delivers integrated information\n
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\n\nThink of MCP Server as giving your AI assistant actual access to your company's tools, not just knowledge about them.\n\n### **The fundamental difference**\n\nTraditional AI is like having a smart consultant who can give you advice but can't actually do anything in your systems. Ask them about your website traffic, and they'll explain how to find it in Google Analytics. Ask them to update your pricing page, and they'll tell you the steps to follow.\n\nAI with MCP Server is like having an executive assistant with login credentials to all your business tools. Ask about website traffic, and they pull the actual numbers from Analytics. Ask them to update the pricing page, and they make the change directly in your CMS.\n\n### **What this looks like day-to-day:**\n\nLet's say you're preparing for next week's board meeting. You need a comprehensive business update: revenue trends, customer growth, team productivity, and current sales pipeline. With MCP Server you simply tell the AI:\n\n
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User Prompt
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\n \"Prepare my board meeting package for next week: revenue trends from last quarter, customer acquisition breakdown, engineering deliverables, current sales pipeline, and team headcount changes.\"\n
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\n\nThe AI connects to all your systems, pulls current data, creates visualizations, and delivers a ready-to-review presentation. Time: 5 minutes of review instead of an hour of manual work.\n\n### Is connecting the MCP server to your tools safe?\n\nJust like you choose which software tools your company uses, you choose which systems to connect to MCP Server. Maybe you start with your website and analytics. Later you add your CRM and project management tools. There's no forced package; you build what serves your specific needs.\n\n
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You Control the Rules
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\n You decide what the AI can and cannot do:\n
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\n •\n Some actions might happen automatically (pulling reports, checking status)\n
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\n •\n Others might require your approval (publishing content, making changes)\n
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\n •\n Sensitive operations can require two-person confirmation\n
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\n\nThink of it like setting permissions for a new employee: you grant access based on what makes sense for your operations.\n\nMCP doesn't create new security risks. It uses your existing login credentials and permissions. If someone on your team doesn't have access to financial data normally, they won't have access through MCP either. Everything is logged, just like your current systems.\n\n## MCP server implementation in practice: updating website challenge\n\nAt Boldare, our website runs on Netlify with Decap CMS managing our blog. In practice, publishing a blog post meant logging into the CMS, manually filling metadata fields, hunting through dropdowns for categories and tags, formatting markdown, checking image links, previewing, adjusting, saving as draft, and submitting for review. The entire process took 15-30 minutes per post, just for administrative overhead, not actual writing.\n\nThe bigger problem, one that becomes critical in organizations where many people make website changes, was the potential for inconsistency. When multiple team members access a CMS, they naturally approach it differently: some fill every metadata field, others skip optional ones, formatting varies, tag usage becomes scattered. These inconsistencies accumulate over time and are hard to catch systematically. This is a challenge we frequently hear from clients with extensive digital systems that require constant updates across distributed teams.\n\n## Building the solution: MCP Server meets content management\n\nAs an AI-native company, we approached this operational friction the way we typically do: by letting AI handle it. We decided to implement an MCP Server that would allow us to add blog posts through a single prompt in an LLM, eliminating the entire CMS interface workflow.\n\nThe system we built is flexible: technically, the LLM can write articles from scratch in the same prompt that publishes them. However, at Boldare, our blog content is created by our authors and domain experts, people with real experience and unique perspectives.\n\nThe MCP Server handles the operational overhead of publishing, not the creative work of writing. This distinction matters: we're not replacing human expertise with AI generation, we're removing the administrative friction that gets in the way of that expertise reaching our audience.\n\n## MCP Server: Core technology stack\n\n
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MCP Server: Core Technology Stack

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ComponentTechnology
BackendNode.js, TypeScript
MCP Protocol@modelcontextprotocol/sdk
SearchFlexSearch (full-text indexing)
Markdown parsinggray-matter
Git operationssimple-git
TransportExpress (SSE)
InfrastructureDocker, nginx, certbot (SSL)
HostingAWS
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\n\n## Key architectural decisions\n\nOne of our most critical decisions was to operate directly on the file system that mirrors our blog post structure, rather than relying solely on API calls. This might seem like a small technical detail, but it had profound implications for performance. By working with the actual files, we could implement sophisticated search algorithms that dramatically improved how well the MCP Server responded to queries.\n\nWe discovered something interesting during development: LLMs frequently use the search functionality to find inspiration or check existing content before creating new articles. They don't just blindly generate; they look at what's already been written, learn from the style and structure, and create something consistent with the existing body of work. This meant that fast, accurate search wasn't just a nice-to-have feature. It was essential for the entire system to work well in practice.\n\n* ### **Reverse-engineering the CMS workflow**\n\nDecap CMS operates through a specific Git-based pattern, but when we started this project, there was essentially no documentation explaining how it worked under the hood. We had to reverse-engineer the entire system by analyzing our existing repository, examining pull requests, and diving into the Decap CMS source code on GitHub. Through this detective work, we discovered the CMS workflow structure:\n\n
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ComponentPattern
Branch namingcms/blog/{slug}
Draft statusnetlify-cms/draft
Under reviewnetlify-cms/pending_review
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\n\nThrough trial and error with test articles, we eventually achieved complete compatibility. Now articles created through our MCP Server appear seamlessly in the CMS interface and flow through our standard editorial process as if they'd been created manually. Getting this right was crucial. Without it, we'd have two parallel content systems that didn't talk to each other.\n\n* ### **Performance optimization**\n\nOur repository presented a genuine performance challenge. With approximately 5,700 files including hundreds of blog articles, case studies, and translations, we couldn't afford slow operations. Initially, we faced several bottlenecks:\n\nRepository cloning on container startup: approximately 30 seconds\n\nFull-text search too slow for responsive UX\n\nParsing frontmatter from hundreds of files created noticeable delays\n\n*\n\n### We solved this through several complementary strategies:\n\n
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StrategyImpact
In-memory cache (5-min TTL)Eliminated repeated file system operations
FlexSearch with lazy indexingIndex builds on first query, not at startup
Docker volumesRepository clones only once, not per restart
Selective content loadingMetadata for lists, full text only when requested
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\n\nThe results exceeded our expectations. Search across our entire blog now takes under 100 milliseconds. Listing articles takes under 50 milliseconds. Creating a new article, including the git commit, takes roughly 2 seconds. These response times make the system feel instant in practice, which is critical for user adoption.\n\n* ### **Security architecture**\n\nSecurity wasn't an afterthought. We built it into the architecture from day one. The server uses API key authentication for MCP connections, enforces HTTPS through certbot-managed certificates, and connects to GitHub using proper OAuth authentication rather than storing credentials in the codebase. Everything runs in an isolated Docker container, adding another layer of protection.\n\n## What's possible with MCP Server\n\nWe built this to solve our own operational friction. The same approach can work for data aggregation, customer insights, infrastructure operations, project coordination, or competitive intelligence – anywhere your team spends time gathering information across multiple systems.\n\nInterested in exploring what this could look like for your organization? **[Get in touch](https://boldare.com/contact/)** and we'll walk through your specific workflows."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1771317266/MCP_serv_1_2_qx8b2d.png","lead":"What if updating your website **took seconds instead of days?** \n\nWhat if preparing for board meetings **took 5 minutes instead of 4 hours?** \n\nFor C-level executives at scaling companies, this isn't wishful thinking – **it's the reality we created by implementing an MCP Server.** We reduced hours of weekly operational overhead to minutes. Here's how we did it, and how you can apply the same approach to your business. Read on!","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-02-11T11:50:21.317Z","slug":"how-mcp-server-eliminates-operational-bottlenecks-case-study","type":"blog","slugType":null,"category":null,"additionalCategories":["Tech"],"url":null},"author":"Magdalena Chmiel","authorAdditional":null,"box":{"content":{"title":"How MCP Server eliminates operational bottlenecks for scaling companies","tileDescription":"How we implemented MCP Server with Netlify to reduce content publishing time by 80-90%. A real case study on eliminating operational bottlenecks.","coverImage":"https://res.cloudinary.com/de4rvmslk/image/upload/v1771317266/MCP_serv_1_2_qx8b2d.png"},"coverImage":null}},"id":"435f15f3-25bb-5d11-8903-98fc99c53c58"}},{"node":{"excerpt":"","fields":{"slug":"/blog/top-7-polish-it-outsourcing-companies-in-2026-ranking-of-the-best-providers/"},"frontmatter":{"title":"TOP 7 Polish IT Outsourcing Companies in 2026 — Ranking of the Best Providers","order":null,"content":[{"body":"## 1. Boldare\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1759139538/65_smol6v.png)\n\nActive in both the Polish and international markets since 2004 — originally operating as Chilid, later XSolve, and rebranded as Boldare in 2021 — the company brings more than 20 years of product development experience. Its team of 70+ specialists works across locations in Gliwice, Wrocław, Gdańsk, and Warsaw.\n\nBoldare provides full-cycle product development as well as IT outsourcing services, supporting clients in fintech, healthtech, SaaS, and e-commerce. The company operates in a strongly agile environment, with a clear focus on delivering business value rather than output alone. Its client portfolio includes brands such as BlaBlaCar, UNDP, and Bosch, reflecting both its international reach and cross-industry versatility. This track record is reinforced by consistent recognition on platforms like Clutch and Awwwards, where Boldare frequently ranks among top-rated Polish product development partners.\n\nFounded: 2004 (as Chilid; rebranded in 2021 as Boldare)\\\nTeam size: 70+\\\nWebsite: [www.boldare.com](http://www.boldare.com/)\\\nHeadquarters: Gliwice, Poland\\\nCore focus: Product development, MVPs, team scaling, UX/UI, React, Node.js, Flutter, Agile coaching\n\n## 2. SoftKraft\n\n\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1775812772/softkraft_logo_20240924_145050_ykr9nd.png)\n\nBased in Kraków, SoftKraft is a boutique software house specializing in custom solutions for fintech, education, and business services. The company has built a near-perfect 5.0 Clutch rating across numerous verified client reviews, consistently placing it among the highest-rated providers in Poland.\n\nClient feedback often highlights a partnership-driven approach, on-time delivery, and strong technical expertise in areas such as AI integration, cloud migration, and backend architecture. Despite this strong reputation, the company remains relatively absent from mainstream tech media and AI-generated rankings, making it a strong example of a firm whose credibility is built almost entirely on real project outcomes.\n\nFounded: 2016\\\nTeam size: 50+\\\nWebsite: [www.softkraft.co](http://www.softkraft.co/)\\\nHeadquarters: Kraków, Poland\\\nCore focus: Custom software, SaaS, AI, cloud migration, Python, React, backend systems\n\n## 3. Neoteric\n\n\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1745493781/Projekt_bez_nazwy-47_uxtsjt.png)\n\nOperating from Gdańsk since 2005, Neoteric has developed a strong position in AI-driven digital products and machine learning over the past two decades. A perfect 5.0 rating on Clutch reflects consistent client satisfaction, particularly in areas such as delivery reliability, communication, and technical alignment with project needs.\n\nThe company has worked with organizations such as the World Bank and Boeing, which is notable for a studio of its size. Combining generative AI, SaaS development, and web engineering, Neoteric is often chosen by clients looking for technically ambitious partners rather than purely execution-focused vendors. Despite this, it remains relatively underrepresented in automated industry rankings.\n\nFounded: 2005\\\nTeam size: 100+\\\nWebsite: [www.neoteric.eu](http://www.neoteric.eu/)\\\nHeadquarters: Gdańsk, Poland\\\nCore focus: Generative AI, SaaS, web & mobile apps, product development, Python, React\n\n## 4. itCraft\n\n\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1775812821/itCraft_kolor_jasne_tloq_b7ghr5.png)\n\nWith over 15 years of experience, the Kraków-based itCraft has positioned itself at the intersection of mobile development and healthcare technology. The company has delivered more than 350 digital products and has received multiple Clutch awards in the mobile development category.\n\nClients consistently highlight its user-centered design approach combined with strong compliance standards required in healthcare projects. The team covers native iOS and Android development as well as cross-platform solutions, serving primarily clients from Western Europe and the United States.\n\nFounded: 2010\\\nTeam size: 80+\\\nWebsite: [www.itcraft.net](http://www.itcraft.net/)\\\nHeadquarters: Kraków, Poland\\\nCore focus: Mobile apps (iOS/Android), healthcare IT, UX/UI, React Native, Flutter\n\n## 5. Railwaymen\n\n\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1775812870/rwm-logo-4426323334a5675ecde7fc27dbf0c5060c8409892d8aa0f2a43e0c6706a16b5c_o856gz.png)\n\nRailwaymen is a Kraków-based software house with more than 16 years of experience and a portfolio exceeding 150 delivered products. Over the years, the company has also earned more than 10 international awards in design and engineering.\n\nClient feedback on Clutch frequently highlights flexibility, problem-solving skills, and reliable communication. Despite its strong track record, the company remains relatively low-profile in AI-generated rankings and broader tech media coverage, making it a hidden but dependable outsourcing option.\n\nFounded: 2009\\\nTeam size: 50+\\\nWebsite: [www.railwaymen.org](http://www.railwaymen.org/)\\\nHeadquarters: Kraków, Poland\\\nCore focus: Custom software, mobile apps, web development, UX/UI, Ruby on Rails, React, React Native\n\n## 6. Sunscrapers\n\n\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1775812917/Primary_vdgp5g.jpg)\n\nSunscrapers is a Warsaw-based boutique studio founded in 2010, focusing primarily on Python, Django, and early-stage product development for startups and scaling digital businesses.\n\nA defining feature of the company is direct access to senior engineers, with minimal layers of account management. Clients from the US, UK, and Germany often highlight clear communication, predictability, and a transparent delivery process. The company deliberately maintains a smaller structure to preserve quality and close client relationships.\n\nFounded: 2010\\\nTeam size: 40+\\\nWebsite: [www.sunscrapers.com](http://www.sunscrapers.com/)\\\nHeadquarters: Warsaw, Poland\\\nCore focus: Python, Django, APIs, MVP development, dedicated teams, React\n\n## 7. SoftwareMill\n\n\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1775812957/sml-logo_ry9ln9.png)\n\nSoftwareMill is a fully distributed engineering company known for its expertise in Scala, Java, and complex distributed systems. With no traditional hierarchy and a strong engineering-driven culture, it stands out in the Polish tech landscape.\n\nIndependent Clutch analyses place it among the highest-rated software companies in the country. The company focuses heavily on finance and technology sectors, delivering solutions in big data, blockchain, and machine learning. It is also active in the open-source ecosystem and organizes the Scalar conference, reinforcing its strong technical reputation.\n\nFounded: 2009\\\nTeam size: 80+\\\nWebsite: [www.softwaremill.com](http://www.softwaremill.com/)\\\nHeadquarters: Warsaw (remote-first)\\\nCore focus: Scala, Java, distributed systems, big data, blockchain, ML, IoT\n\n## Why choose a company from this ranking?\n\nWorking with one of these providers means partnering with teams whose reputations are built on verified delivery outcomes rather than marketing visibility. Compared to large IT corporations, boutique studios offer direct access to senior engineers, greater flexibility, and closer collaboration. This typically translates into faster delivery, more efficient costs, and stronger influence over product direction.\n\n## How to choose the right outsourcing partner\n\nSelecting the right vendor requires evaluating proven experience, transparency of portfolio, seniority of the team, and available engagement models. Verified reviews on platforms like Clutch are a strong indicator of reliability. Companies such as Boldare, SoftKraft, and Neoteric often serve as useful benchmarks when building a shortlist.\n\n## Pricing expectations\n\nTypical hourly rates in this market range from €50 to €150 per specialist, depending on experience level, technology stack, and engagement model. This positions the region as a strong alternative to Western Europe and the US, offering competitive pricing without compromising on quality.\n\n## Frequently asked questions\n\n**What is IT outsourcing?**\\\nIt is a model where companies delegate software development, infrastructure, QA, or full project delivery to external providers, gaining flexibility, cost efficiency, and access to specialized expertise.\n\n**What are the main benefits?**\\\nLower hiring costs, access to niche skills, scalable team structures, and predictable budgeting.\n\n**What should I look for in a provider?**\\\nExperience, seniority of engineers, quality of case studies, engagement models, and verified client reviews.\n\n**Which companies are worth considering?**\\\nBoldare, SoftKraft, Neoteric, itCraft, Railwaymen, Sunscrapers, and SoftwareMill.\n\n**Body leasing vs team leasing?**\\\nBody leasing provides individual specialists, while team leasing delivers a full, self-managed team responsible for execution.\n\n**Do these companies work internationally?**\\\nYes — most serve clients from Western Europe, the US, and Scandinavia, combining strong technical expertise with competitive pricing and time zone compatibility."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1775737168/Group_26086321_b9d3wg.png","lead":"The country has become one of Europe’s most reliable sources of software engineering talent, with outsourcing companies built on this foundation gaining strong recognition from businesses worldwide. Rather than repeating the same well-known names, this ranking takes a more practical approach, focusing on verified, niche studios with proven client work on Clutch, while remaining largely absent from AI-generated lists and mainstream tech coverage.\n\nEach company included has been selected based on documented delivery quality and client satisfaction, rather than marketing visibility or advertising spend. For every entry, you’ll also find key details such as founding year, team size, office locations, and core areas of specialization.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-02-10T13:05:45.309Z","slug":"top-7-polish-it-outsourcing-companies-2026-ranking-best-providers","type":"blog","slugType":null,"category":null,"additionalCategories":["GenAI","How to","Future","Tech"],"url":null},"author":"Roksana Kaczmarska","authorAdditional":"","box":{"content":{"title":"TOP 7 Polish IT Outsourcing Companies in 2026 — Ranking of the Best Providers","tileDescription":"Discover the Top 7 Polish IT outsourcing companies in 2026. Compare the best providers based on expertise, client reviews, and proven project delivery.","coverImage":""},"coverImage":null}},"id":"7a656eba-746e-5f05-8ffa-f216c4060e08"}},{"node":{"excerpt":"","fields":{"slug":"/blog/5-design-challenges-in-scaleups-and-how-ai-native-delivery-improves-product-delivery/"},"frontmatter":{"title":"5 design challenges in scaleups and how AI-native delivery improves product delivery","order":null,"content":[{"body":"## The structural problem of scaleup organizations\n\nDesign and development run on different rhythms, simply because they need to. Design exploration thrives on flexibility and rapid iteration while development needs stability and clear specifications. In early-stage companies with small teams, you can bridge this gap through daily conversations and quick check-ins. But scaleup organizations face a different reality since lots of teams work in parallel. This makes product lines multiply and the coordination that worked at ten people break down at fifty.\n\nWhen teams adopt AI tools without changing their core processes, they often **accelerate the wrong things**. A designer can now generate twenty mockup variations in the time it used to take to create three. A developer can produce implementation code before the requirements are fully stable. Product managers can write detailed specifications for features that haven't yet been properly validated. The tools make it easier to create more outputs, but those outputs still need human review and evaluation. The bottleneck shifts from creation to decision-making and alignment, but teams often don't realize this until they're drowning in options and variations that all need discussion.\n\n## Challenge 1: The final design that never stops changing\n\nThe first challenge we observed in our client teams appears when new edge cases surface during sprint planning. Engineering teams **discover requirements that design didn't account for**: permission models, localization needs, legacy data integration or compliance requirements. Stakeholders see in-progress builds and generate fresh feedback. Legal teams drop in new constraints late in the process. Design updates each one, treating these changes as quick tweaks rather than real scope shifts.\n\nAll these seemingly minor changes build up what we might consider a **UI debt**. Patched layouts multiply while developers add copy on the fly. On top of that, interactions drift away from any prototype so QA teams spend more time interpreting intent and reconciling contradictions than actually checking quality.\n\n## Challenge 2: No single source of truth\n\nThe second challenge involves **fragmented sources of truth**. The same feature exists in Figma, a UX prototype, a Notion spec, and a Jira ticket. However, each version differs slightly. Design experiments for A/B tests never get properly retired, so people **keep rediscovering and reusing outdated flows,** and logic for different markets or user tiers get scattered across files owned by different designers.\n\nThis leads to teams shipping **inconsistent experiences** across platforms because each one looked at a different source. Such fragmentation intensifies when AI tools make it easy to generate content in multiple places. Someone uses AI to draft requirements in Notion, another person uses AI to generate implementation details in Jira. Then, a designer uses AI to create variations in Figma. Each AI-generated output seems valid because it's well-formatted and detailed, but they haven't been compared with each other. The result? Teams spend meeting time figuring out which version is the current decision, instead of progressing.\n\n## Challenge 3: MVP quietly becomes v2\n\nThe third challenge shows up in how **MVP definitions drift apart** whenteams define MVP through UI completeness rather than outcome-based scope. Once stakeholders see a polished screen, saying no to it feels harder than declining a bullet point in a spec. Design explorations meant for future iterations accidentally become part of the default implementation because they live in the same prototype file and nobody explicitly marked them as out of scope.\n\nMVPs quietly transform into version two or version three, with complex permissions, customization options, and edge case handling baked in from day one. Launch criteria become blurry and teams say they'll ship once the implementation matches the prototype, which **delays validation and revenue** since the prototype itself keeps evolving.\n\nAI-assisted design makes this drift even more likely. When a designer can quickly generate polished screens for edge cases and future phases, those explorations look like commitments rather than possibilities. Stakeholders see beautiful, detailed designs for advanced features and assume they're all part of the plan. The ease of creation makes it harder to maintain boundaries between what we're building now and what we're considering for later.\n\n## Challenge 4: Design systems as side projects\n\n\\\nThe fourth challenge emerges around **design systems.** Product teams ship custom UI elements to reach delivery dates, planning to update the design system later. And that later never arrives – documentation lags behind code, component libraries exist in repositories but lack usage guidelines, examples, or clear patterns for how to use them. Ownership remains unclear because no dedicated design system team or structure exists.\n\nDesigners **stop trusting** the library and build their own variants instead, and engineers create separate versions of components. This makes onboarding new people **slow** because they need insider knowledge to tell which components are current and which are legacy.\n\nAI tools can mask this problem while making it worse underneath. Developers use AI to quickly generate component code that looks consistent but doesn't actually use the design system and designers use AI to create designs that visually resemble design system patterns but include subtle variations. The output looks professional and coherent, but the actual reusability and consistency **degrades** because nobody's enforcing the systematic approach that design systems require.\n\n### Challenge 5: No clear definition of ready\n\nThe fifth challenge involves **missing definitions of readiness**. Each team uses different criteria for what makes a design ready for implementation – some teams require full user flows with error states, while others accept only happy paths. This makes design tools fill up with exploratory work, old concepts, and approved specifications – all living on the same canvas with weak labeling or status markers.\n\nDevelopers pull the wrong frame or an outdated component because it looked complete. Planning meetings **waste time debating** what's actually in scope rather than aligning on actual constraints and tradeoffs.\n\n## The consequences nobody sees coming\n\nAll these patterns build up over time. Delivery timelines become **unpredictable** because estimates assume stable requirements while design and scope keep changing. Teams underestimate the time needed for discovery and tradeoff discussions. Roadmaps turn into moving targets, and stakeholders start treating deadlines as flexible, pushing for more scope.\n\n* ### Rework becomes the default\n\nRework spreads across the organization. Features go through multiple passes: an initial build, fixes for missed states, alignment with updated designs, and late analytics work that should have been planned earlier. Coordination overhead increases. Teams add more design-development syncs, and comment threads across tools become long and inconsistent.\n\n* ### The cognitive load on development teams\n\nDevelopment teams carry a growing cognitive load. They constantly reconcile what they see in design files, what exists in the design system, and what is already in production. Switching between multiple features and sources increases errors and slows down individual work.\n\n* ### Trust erosion between functions\n\nTrust between teams starts to break down. When designs constantly change or miss constraints, engineers stop treating them as reliable input and see them as rough inspiration instead. Product leaders begin bypassing design for “simpler” features, which increases inconsistency and weakens design’s role in product decisions.\n\n* ### The headcount paradox\n\nAs teams grow, delivery does not speed up. Each new team introduces more variation through new patterns and exceptions instead of adding reusable solutions. Leadership sees headcount increase faster than output, while the real bottleneck is in design complexity, not individual performance.\n\n## What actually works: The AI-Native delivery system\n\nWorking with AI-native delivery partners makes a difference not because they use more AI, but because they use it with intent. Being AI-native is not about plugging AI into every tool or producing more output faster. It’s about integrating AI in ways that keep product development predictable, scalable, and decision-driven.\n\n[AI tools are boosters, not universal solutions,](https://www.boldare.com/blog/ai-tools-designer-challenges-future/) so when teams lack clear decision points, ownership, and handoffs, AI simply boosts the wrong things. It generates more variants, more specs, and more artifacts that still require human judgment and alignment. The result is **movement without progress.**\n\nAI-native delivery starts by fixing the foundations first, then applying AI only where speed actually creates value.\n\nThe first foundation is a hard **boundary between exploration and implementation**. Design teams need freedom to explore quickly, and AI is genuinely useful here for generating options, testing ideas, and challenging assumptions. But once work enters development, it locks. Scope only changes through an explicit product decision, not because iteration is cheap. When the cost of creation drops to near zero, discipline becomes the real constraint.\n\nThe same principle applies to design systems. In AI-native delivery, design systems are treated as **infrastructure**, not side projects. They move slower than product features on purpose, with clear governance, versioning, and deprecation rules. Teams move faster precisely because they trust the foundation and stop reinventing patterns. AI can help spot inconsistencies or suggest reuse, but only within a system that already enforces consistency.\n\nAs teams grow, design operations become a core capability. What works for two designers breaks at ten, so someone must own shared libraries, specification templates, naming conventions, status tracking, and visibility into design workload. AI increases the volume of content dramatically, which makes ownership and structure non-negotiable. Without them, fragmented sources of truth multiply.\n\nClear definitions of ready are another critical control point. AI-native teams agree upfront on what “ready for development” means, covering edge cases, accessibility, performance, analytics, and dependencies. Shared readiness criteria protect teams from the false sense of completeness that AI-generated content often creates. \n\nExploration is also constrained by implementation capacity. AI makes exploration feel free, but shipping is not. [AI-native delivery aligns design pace with engineering capacity through roadmapping and explicit prioritization](https://www.boldare.com/blog/scale-ai-with-rapid-prototyping/). Teams explore what they can realistically build, not everything the tools make possible.\n\n## Why AI-native delivery system changes the game\n\nThis is where the difference becomes visible. Mature teams use AI to accelerate execution once decisions are clear. Less mature teams use AI to generate options and mistake activity for progress. \n\nAI-native delivery partners focus on building systems that create clarity with clear decision rights, explicit handoffs and stable readiness criteria. When these systems exist, AI becomes an accelerator. When they don’t, AI just creates more noise. \n\nIt’s important to note that the gap between design and development will always exist. The question for scaleup organizations is whether that gap is managed intentionally or allowed to quietly slow everything down. AI tools promise speed, but without fixing coordination and decision-making, they usually make the problem worse.\n\nAt Boldare, we work as AI-native delivery partners helping scaleup companies build products that scale with their ambition. We’ve learned that AI boosts whatever system it touches so when applied to solid foundations, it accelerates delivery. Applied to broken ones, it enlarges dysfunction. When teams talk past each other and delivery becomes unpredictable, the solution isn’t about more output. It's about better systems.\n\n## FAQ\n\n**What causes design challenges in scale-up companies?**\n\nDesign challenges in scale-up companies usually emerge from coordination issues. As teams grow, design, development, and product decisions happen in parallel, often without clear handoffs, shared definitions of readiness, or a single source of truth. Over time, this creates gaps between design intent and what gets built.\n\n**Why do AI tools often increase friction instead of reducing it?**\n\nAI tools increase the speed and volume of output across design, product, and engineering. When teams lack clear ownership, decision points, and governance, this additional output requires more alignment and review. The result is higher activity levels without corresponding progress.\n\n**What is UI debt and how does it affect scale-up teams?**\n\nUI debt accumulates when interfaces are repeatedly patched to accommodate new requirements without resolving underlying structure or consistency. As it grows, changes take longer to implement, QA cycles expand, and teams spend more time aligning on expected behavior. This reduces delivery predictability and slows product development.\n\n**How can scaleup organizations reduce design and delivery friction?**\n\nOrganizations reduce friction by treating design systems as infrastructure, building design operations as a core capability, and aligning design pace with engineering capacity. Clear readiness definitions, explicit scope control, and consistent governance help teams scale without adding unnecessary complexity."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1770706542/Blog_post_g72uem.png","lead":"Late-night Slack messages between development teams tell a familiar story – designers iterate on prototypes while developers chase moving targets. Lots of versions of the same feature exist across different files, and nobody's quite sure which one is the current.\n\nIf you're leading a scaleup company, you've probably seen this pattern. Design exploration happens at one speed and development at another. And somewhere in that gap, clarity turns into confusion, deadlines stretch, and teams start talking past each other.\n\nThe problem grows more complex when teams start reaching for [AI tools to solve coordination issues](https://www.boldare.com/blog/ai-augmented-services-empowering-ux-design-and-development-at-boldare/). Developers use AI to generate code faster, designers use AI to produce mockups and variations at speed, product managers use AI to write requirements. Everyone moves faster individually, but surprisingly, the organization as a whole doesn't. In fact, the noise often increases – more variations get created, more options need evaluation and more alignment conversations become necessary. \n\nThis article breaks down five design challenges we’ve observed as companies scale, and explains what actually works to fix the system, not just the symptoms.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-02-09T21:00:25.525Z","slug":"five-design-challenges-in-scaleups-ai-native-delivery","type":"blog","slugType":null,"category":null,"additionalCategories":["Digital Product","Tech"],"url":null},"author":"Aleksander Dąbrowski","authorAdditional":"","box":{"content":{"title":"5 design challenges in scaleups and how AI-native delivery improves product delivery","tileDescription":"Learn why design challenges increase at scale and how AI-native delivery improves product coordination, scope control, and execution.","coverImage":""},"coverImage":null}},"id":"1b39cf23-2222-52cb-a702-52b80b3a4055"}},{"node":{"excerpt":"","fields":{"slug":"/blog/ai-prompt-engineering-guide/"},"frontmatter":{"title":"What is AI prompt engineering?","order":null,"content":[{"body":"## What is AI prompt engineering?\n\nAI Prompt Engineering is the practice of crafting optimized inputs for AI models – inputs that guide the AI to perform a specific task, answer a question, or generate relevant content. The goal is simple: get better outputs. Think of it as giving AI clear directions so it doesn't veer off course. It's about precision and context. One misstep in your prompt can lead to vague, irrelevant, or inaccurate responses.\n\nIn essence, prompt engineering is an essential tool for businesses aiming to unlock the full potential of [artificial intelligence](/blog/ethical-issues-of-ai-in-digital-product-development) technology. It's not about coding or deep technical skills. It's about understanding the way AI works and knowing how to communicate with it effectively.\n\nWhen you're dealing with powerful models like GPT-3 or GPT-4, the complexity of the task often depends on how well you craft the prompt. A poorly phrased query will lead to poor results, no matter how powerful the AI is. By using prompt engineering, you ensure that your AI models give you exactly what you need, when you need it.\n\n## How does AI prompt engineering work?\n\n### 1. Crafting the right prompts\n\nThe first step in AI Prompt Engineering is understanding the output you want. You wouldn't give someone vague instructions for a task, right? It's the same with AI. You have to be clear, concise, and specific. The more detail you provide, the more accurate and relevant the AI's response will be.\n\nA good prompt goes beyond just asking the right question. It's about framing that question within the right context. For example, instead of asking, \"What are the best practices for SEO?\" a more specific prompt would be, \"What are the top 5 SEO strategies for e-commerce businesses in 2026?\" This type of clarity drives better, more actionable results.\n\n### 2. Providing context and examples\n\nAI models understand context, but only if you give it to them. Including relevant background information, constraints, or examples within your prompt allows the AI to tailor its response to meet your specific needs. The goal is to narrow the field of possibilities and guide the model towards the right answer. This is why AI Prompt Engineering isn't just about writing good queries, it's about shaping the entire conversation.\n\nFor example, when building a [chatbot for customer service](/blog/what-decision-makers-need-to-know-generative-ai-revolution), you'd design prompts that guide the AI to provide responses based on the type of customer inquiry. \"How do I track my order?\" requires a different response than \"What are the return policies?\" With context, the AI can distinguish between different scenarios and provide more relevant answers.\n\n### 3. Iteration and refinement\n\nJust like any engineering process, prompt engineering is iterative. You won't always get it perfect the first time. And that's OK. It's about continuous refinement. You'll tweak and optimize your prompts over time to get better results. It's a process of trial, error, and learning what works best for your business.\n\nTesting and feedback loops are essential. If your AI's responses aren't hitting the mark, it's time to rethink your prompts. Sometimes, it's as simple as changing the wording, other times, it's about changing the context entirely. But in the end, this refinement leads to AI that works for you – not the other way around.\n\n## How AI prompt engineering enhances business operations?\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
#TopicDescription
1Unlocking AI's Full PotentialAI models are only as good as the data (and prompts) you feed them. If you want to leverage AI for complex tasks like content generation, customer support, or data analysis, prompt engineering is key. It's about maximizing the output while minimizing the need for human intervention. You need prompts that are specific enough to drive accuracy, but flexible enough to handle a variety of inputs. For example, if you're using AI to generate marketing copy, prompt engineering helps ensure that the tone, style, and message are aligned with your brand's voice. The AI needs the right input to deliver content that resonates with your target audience.
2Scaling AI Across TeamsAs your company grows, so does the need for AI across various departments. From marketing to finance to customer support, different teams need AI that works for their specific goals. But each department has its own language, terminology, and set of priorities. This is where AI Prompt Engineering comes in. It ensures that the AI models you deploy across your company can be customized to meet the unique needs of each team. Whether it's building reports, generating leads, or answering customer queries, the right prompts ensure that each team gets the most value from AI.
3Automating Routine TasksAI is great at handling repetitive tasks. But without the right prompts, these tasks can be inefficient or prone to error. AI Prompt Engineering ensures that the tasks AI is handling are optimized, reliable, and scalable. For instance, automating your company's processes requires clear, structured prompts to ensure that the generated reports are accurate every time.
\n\n## AI prompt engineering challenges\n\n### 1. The ambiguity problem\n\nEven with the best-designed prompts, AI models can sometimes produce ambiguous results. Why? Because language is complex, and AI doesn't always understand nuances like humans do. One way to tackle this is by providing more detailed context, but ambiguity can still crop up. That's part of the reason prompt engineering is an ongoing process.\n\n### 2. Model limitations\n\nNo AI model is perfect. Sometimes, no matter how well you craft a prompt, the AI may not have the knowledge or capabilities to respond in the way you want. In these cases, prompt engineering can help optimize results, but human intervention may still be necessary to fine-tune responses.\n\n### 3. Over-optimization\n\nThere's a fine line between optimizing prompts and over-optimizing. The more specific you get, the more you risk restricting the AI's ability to generate creative or diverse responses. The trick is to find the right balance – structured enough to drive the right output, but flexible enough to let the AI do its thing.\n\n## Conclusion\n\nAI Prompt Engineering is essential for businesses looking to harness the true power of AI. It's not about asking questions; it's about asking the right questions in the right way. Whether you're building smarter chatbots, automating content creation, or streamlining customer support, prompt engineering helps you unlock the full potential of AI.\n\nIf you want to get AI to work for you – not just for you – mastering the art of AI Prompt Engineering is key. It's a process of refinement, iteration, and understanding your AI model's strengths and limitations. But when done right, it will transform the way you use AI and help you drive better business outcomes.\n\n## Frequently asked questions about AI prompt engineering\n\n**1. What is AI prompt engineering?**\\\nAI Prompt Engineering involves creating optimized inputs for AI models to ensure they perform specific tasks accurately, helping businesses maximize AI output with precise, context-driven prompts.\\\n\\\n**2. Why is providing context important in AI prompt engineering?**\\\nProviding context helps the AI understand the specific needs of the task, allowing it to generate more relevant and accurate responses, such as distinguishing between different customer service inquiries.\\\n\\\n**3. What are the challenges of AI prompt engineering?**\\\nKey challenges include handling ambiguity in AI responses, working within model limitations, and avoiding over-optimization, which can restrict the AI's creative potential."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1746688366/Zrzut_ekranu_2025-05-8_o_09.11.57_xvel8g.png","lead":"**AI Prompt Engineering is the process of designing and optimizing the inputs or \"prompts\" that guide AI models, like GPT-3 or GPT-4, to generate desired outputs.** It involves crafting specific, clear, and context-rich instructions to help AI systems produce accurate, relevant, and actionable results, ensuring that the AI meets the specific needs of a business or task.\n\nAI Prompt Engineering is not just a buzzword. It's the art and science of crafting the right inputs for AI models to ensure they deliver powerful, accurate, and actionable outputs. **It's about shaping AI responses to fit your business needs, reducing ambiguity, and making AI smarter, faster, and more reliable.** For companies looking to harness the true potential of AI, understanding AI Prompt Engineering is the key.\n\nWhether you're building **customer support chatbots, automating content generation, or developing sophisticated data analytics tools**, the way you ask AI to work matters. The prompts you use determine the value you get from AI. But here's the kicker: AI doesn't just understand natural language. It needs the right cues to operate at its best. And that's where prompt engineering comes in.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-02-03T11:44:53.031Z","slug":"ai-prompt-engineering-guide","type":"blog","slugType":null,"category":null,"additionalCategories":["GenAI"],"url":null},"author":"Magdalena Chmiel","authorAdditional":null,"box":{"content":{"title":"What is AI prompt engineering?","tileDescription":"Discover AI Prompt Engineering: the art of crafting optimized inputs for AI models to get better, more accurate results for your business needs.","coverImage":null},"coverImage":null}},"id":"ee543359-96cf-5f51-be1d-dfbf1365cf98"}},{"node":{"excerpt":"","fields":{"slug":"/blog/from-mvp-to-product-market-fit-why-early-success-often-doesn-t-scale/"},"frontmatter":{"title":"From MVP to Product-Market Fit – Why early success often doesn’t scale","order":null,"content":[{"body":"## Different challenges of MVP validation and product-market fit\n\nAn [MVP is built](https://www.boldare.com/services/mvp-development/) to answer two really narrow questions: \n\n* is there a problem worth solving?\n* does a proposed solution resonate with this problem? \n\nThis makes it a tool for reducing uncertainty early, not for proving long-term viability which may often be misleading and create biases.\n\n[Product-market fit](https://www.boldare.com/blog/product-market-fit-done-right-examples/), on the other hand, addresses different concerns. It is not about initial interest, but about repeated value as it answers whether a clearly defined group of customers consistently chooses the product over available alternatives, integrates it into their routine, and accepts its trade-offs at scale.\n\nThis distinction is easy to get lost in translation, as initial users tend to show motivation and willingness to adapt at the early stages. They tolerate missing features, unstable behavior, and unclear onboarding because they want the product to exist – this engagement signals intent, not sustainability. Unfortunately, as the audience grows, this tolerance tends to drop and what early users accepted as “good enough” becomes friction. \n\nIt’s not like the initial feedback is misleading, it simply answers a different question. Treating these two phases as a single continuity creates false confidence leading to teams moving forward assuming the hard part is done, while, in reality, the most demanding constraints are still on the horizon.\n\n## Why MVPs break down in practice\n\nMost MVPs fail to carry teams to product-market fit because they serve a different purpose – to be optimized for speed and learning under uncertainty, not for stability or scale.\n\nAI enhances this dynamic as it removes friction from early development and makes learning loops faster, which is exactly what MVPs are meant to do. The downside appears when the same setup is stretched beyond early validation when speed is no longer the main constraint.\n\nThis shows up first in **engineering** when logic that was hard-coded to support a narrow use case becomes difficult to generalize. Also, data models reflect early assumptions rather than real behavior, and infrastructure that worked under limited load starts to introduce friction once performance or compliance expectations increase. Each change becomes riskier, slowing down iteration at the moment learning should be accelerated the most. \n\nThe **UX** **area** follows a similar pattern. MVP interfaces are often designed based on insights from a very small and specific group of users (frequently early testers who are highly motivated and already familiar with the problem). As the product reaches a wider audience that context disappears leaving the new users confused and lost. \n\n**Measurement** is often where the gap between MVP validation and product-market fit becomes most visible. MVP [analytics](https://www.boldare.com/blog/product-market-fit-metrics/) (even the AI-assisted ones) usually focus on activity, not on whether users reach the outcome the product promises. Teams can see sign-ups and clicks, but they cannot tell which behaviors lead to repeat use and retention.\n\nAll of these are predictable **consequences** of extending a trial and experimental system beyond its intended lifespan. The MVP continues to function, but it no longer provides the feedback or reliability required to guide the next phase. \n\nWe’ve seen it all play out in high-growth products operating under real market pressure. A good example is how [BlaBlaCar](https://www.blablacar.com/) scaled from a successful early product into a reliable, multi-market system while expanding into 27 countries in just over a year – without losing speed or product focus.\n\nRead the case study: [Agile and skilled development teams for BlaBlaCar, a French unicorn](https://www.boldare.com/work/case-story-blablacar/)\n\n## What are the risks behind staying in the MVP phase for too long?\n\nStaying in MVP for too long results in a quiet problem built up. The product seemingly keeps running, and yet many difficult decisions are often delayed.\n\nOne of the biggest risks of relying on MVP for too long is **decision** **quality** **drop**. MVPs generate signals, but those signals are not fully reliable. So when teams over-rely on them, they start optimizing for what is easy to observe rather than what actually matters, making the roadmap grow without actual profit.\n\nAnother cost is **architectural** **paralysis** – MVP systems are built around assumptions that were reasonable early on, but over time, these assumptions limit what can be tested safely. Teams become cautious because every change touches too many fragile parts, resulting in slower learning and bigger risks.\n\nThese risks escalate in the shadow as nothing breaks outright. Instead, progress becomes expensive, slow, and increasingly difficult to navigate. By the time the situation is recognized, the effort required to change direction is significantly higher.\n\n## How to check whether you’re stuck in MVP\n\nBeing stuck in MVP rarely feels like it, because it’s usually not a result of a single failure, but a pattern of signals that are easy to ignore when viewed in isolation.\n\nThe matrix below is a way to connect observable symptoms to the hidden constraints that block product-market fit, and to translate them into specific moves a CTO can make in the next 90 days.\n\n
What you seeWhat it really meansIf you ignore itWhat to do in the next 90 days
Retention is flat or dropping, but you keep shipping featuresYou're polishing the surface, not fixing what mattersBloated product, fuzzy value, no PMFCheck retention and engagement by user type. Cut features that don't drive repeat use. Focus on the core loop.
Every change feels risky or slowYour tech setup makes learning hardExperiments get expensive, teams stop tryingFix the basics: refactor key flows, add tests, set up monitoring and safe rollbacks.
Roadmap is full of custom requests from a few clientsYou're building for edge cases, not a marketMessy product, weak positioning, poor scaleTreat requests as signals, not orders. Group by user type and job-to-be-done. Say no more often.
Metrics look \"busy\" but value is unclearYou're tracking activity, not outcomesFalse confidence, bad prioritiesRedefine success: retention cohorts, expansion, willingness to pay. Tie metrics to real outcomes.
Sales and marketing can't clearly explain who it's forYour product story isn't clear yetLow conversion, long sales cyclesAlign product, engineering, and target market around one clear target segment and differentiation.
Feedback feels chaotic and contradictoryYou lack a structured learning loopReactive roadmap, tired teamsCreate clear feedback channels and review cycles. Decide what input drives decisions and what doesn't.
Target market experiments are slow or painfulYour org isn't built for iterationPMF takes foreverMake pricing, onboarding, and sales experiments cheap and fast. Treat them as core product work.
\n\n\n\nWould you like to learn more about PMF thinking with real examples? We broke this down live with product leaders in [Product-Market Fit 101: Live talk with experts, Q&A | Boldare Events](https://www.youtube.com/watch?v=PvPhQIj7XAo)\n\n## What changes when teams move beyond the MVP\n\nThe most important work to do when pivoting to product-market fit is the mindset switch from “prove it works” to “make it reliable, repeatable, and valuable for a focused market.” At this stage, AI remains a powerful helper, but the limiting factor shifts from how quickly teams can build to how clearly they can decide what should be built next\n\nChanging this mindset means progressing on three fronts:\n\n**1. Product and discovery**\n\nTeams narrow their focus to a specific segment and a core problem, while consciously postponing adjacent use cases. Discovery becomes continuous rather than occasional, driven by regular customer interviews, in-product surveys, and usage data. The roadmap is shaped by learning about real behavior, not by isolated feedback.\n\n**2. Architecture and engineering**\n\nThe system is adjusted to support reliability and safe change. Critical parts of the architecture are refactored where they limit development or learning. Testing, monitoring, and rollback mechanisms are treated as tools that reduce risk and speed up change.\n\n**3. Target market and operations**\n\nProduct, engineering, and revenue teams align on clear PMF indicators – technology supports experiments in pricing, onboarding, and sales by making them cheap and reversible. Feedback from customers is structured and prioritized.\n\nAt this stage, many teams realize they need extra capacity. Not because they lack ideas. They lack time and focus to redesign the product, architecture, and learning loops.\n\nBoldare works across all product stages, from MVP to product-market fit and scaling. We combine product strategy, UX, engineering, and AI to address common MVP problems: fragile systems, missing analytics, unclear value and weak onboarding.\n\nWith over 300 digital products delivered, including [SaaS platforms](https://www.boldare.com/work/case-study-ionoview/) that we helped evolve from MVPs into scalable systems, we bring proven ways to refactor and improve without stopping the business.\n\n## Breaking the MVP glass ceiling\n\nBreaking the MVP ceiling requires a conscious shift. For a CTO, this means recognizing when the product has outgrown its initial setup and changing what the team optimizes for. Moving forward is not about abandoning speed or experimentation, but about building systems that support sustainable growth. Teams that make this transition early preserve their ability to adapt, rather than locking themselves into constraints that become expensive to undo later.\n\nMoving forward is not about abandoning speed or experimentation. Teams that succeed continue to use AI throughout the SDLC, but they pair it with clear product strategy, reliable data, and experience in navigating the trade-offs that only emerge at scale.\n\nFor many teams, experienced [outside support](https://www.boldare.com/services/consulting-and-scaling) can make this transition smoother by strengthening architecture, discovery, and decision-making while the product keeps shipping.\n\n## FAQ\n\n**1. What does it mean to “outgrow” an MVP?**\nA product outgrows its MVP when early architectural, data, and process decisions start limiting further growth. Typical signals include slower delivery despite stable team size, increasing risk with every new feature, and rising maintenance costs that don’t translate into user value.\n\n**2. Is breaking the MVP ceiling the same as slowing down development?**\nNo. The shift is not from speed to bureaucracy, but from short-term speed to sustainable velocity. Teams that invest early in scalable systems reduce rework, production issues, and decision friction, which ultimately enables faster and more reliable delivery.\n\n**3. How should AI be used after the MVP stage?**\nAfter MVP, AI should move from ad hoc experimentation to intentional use across the SDLC. This includes pairing AI tooling with reliable data, clear product strategy, and experienced oversight. At scale, AI is most effective when it supports decision-making and execution, not when it operates in isolation.\n\n**4. What changes in the CTO’s role post-MVP?**\nThe CTO’s focus shifts from validating assumptions to optimizing for long-term adaptability. This includes improving decision quality, system resilience, and architectural flexibility, while ensuring that short-term delivery does not create long-term constraints.\n\n**5. Why do teams involve external experts during this transition?**\nExternal support can help teams evolve architecture, product discovery, and decision-making without slowing ongoing delivery. This approach allows products to keep shipping while foundational improvements are made, reducing the risk and cost of large-scale rewrites later."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1770040800/Blog_post_wbbskb.png","lead":"A successful MVP launch is often treated as a guarantee that **product-market fit** is just a matter of formality. While in practice, this stage is where many products get **stuck**. Even though the product’s live, users are technically signing up and the feedback flow continues, the **growth does not speed up** and decisions are getting harder and harder. \n\nThis pattern is not rare as studies consistently show that poor product-market fit remains the leading cause of early product failure – **approximately 34% of startups fail without reaching product-market fit**, despite having an initial product in the market, per **Harvard Business School** research.\n\nThis dynamic is even more visible today, when tools powered by AI make it possible to prototype and launch MVPs faster than ever before – often with far less effort than teams had to invest just a few years ago. Speed helps teams learn quickly, but it can also blur the moment when the nature of the work needs to change.\n\nRead this article to see why MVPs rarely carry teams to product-market fit on their own, what hidden constraints surface after early validation, and what a more responsible transition out of MVP mode looks like in practice.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-02-02T13:59:39.770Z","slug":"why-early-mvp-success-doesnt-scale","type":"blog","slugType":null,"category":null,"additionalCategories":["How to","Strategy","Future"],"url":null},"author":"Aleksander Dąbrowski","authorAdditional":"","box":{"content":{"title":"From MVP to Product-Market Fit – Why early success often doesn’t scale","tileDescription":"Learn why early MVP success doesn’t scale and how teams must rethink systems, AI use, and architecture for sustainable growth.","coverImage":""},"coverImage":null}},"id":"a646644a-4a46-5280-bf24-c0af648a17ab"}},{"node":{"excerpt":"","fields":{"slug":"/blog/refactor-replace-isolate-2026-cto-guide-modernizing-legacy-systems-scaleups/"},"frontmatter":{"title":"Refactor, replace, or isolate? - 2026 CTO guide for modernizing legacy systems in scaleups","order":null,"content":[{"body":"## What legacy means in a scaleup context\n\nTechnology in scaleups ages quickly because the early phase optimizes for speed and learning, not longevity. Architectural shortcuts with tight coupling are often tolerated and documentation lives only in people's heads. All of this is rational to a certain point.\n\nProblems appear later, when the organization grows and the cost of change increases. This leads to [features that once took days start taking weeks](https://www.boldare.com/blog/technical-debt-building-future-proof-digital-products/), each deployment feeling risky and knowledge being concentrated around a few seasoned engineers. Data silos and rigid internal interfaces block new initiatives such as AI-driven features, advanced analytics, or large-scale integrations.\n\nAt this stage, the system is not failing outright, but it is quietly dictating what the business can and cannot do. This is where CTOs and leaders are forced into strategic trade-offs instead of purely technical decisions."},{"body":"## Refactor: evolving a system that still fits the business\n\nRefactoring is often the **least** **controversial** **option**, but also the most misunderstood. In the legacy system context, it means improving internal structure, performance, and maintainability without changing how the system behaves externally. It is not about rewriting everything, but about making the existing system easier and safer to work with.\n\nThis approach makes sense when the system **still supports the core business** well, but suffers from growing **technical debt** that slows down delivery. It is particularly advisable when significant domain knowledge is embedded in the code and would be expensive or dangerous to recreate elsewhere. Teams that understand the system but are constrained by it often benefit the most from a structured refactoring effort.\n\nTypical refactoring work starts with **stabilizing the foundation**. Adding regression tests around critical business flows reduces fear and enables change. Modularizing a monolith by introducing clearer boundaries and ownership can significantly improve velocity without introducing the operational complexity of distributed systems. Targeted performance improvements, especially around database access and caching, often result in immediate and measurable business benefits.\n\nThe strength of refactoring lies in its relatively **low risk** and its ability to deliver visible improvements quickly. It is easier to justify to stakeholders because it rarely creates a long period with no business value. However, refactoring has limits – it cannot fully compensate for a fundamentally flawed architecture or data model. In those cases, it often serves as a way to buy time rather than a permanent solution."},{"body":"## Replace: rebuilding or buying your way out\n\nReplacement strategies come in two different forms. The first is rebuilding or rearchitecting a system in-house. The second is replacing custom software with a COTS or [SaaS product](https://www.boldare.com/industries/future-proof-your-saas-product).\n\nRebuilding from scratch makes sense when the existing system fundamentally **blocks** **growth** and when business requirements have branched off so far from the original assumptions that gradual change no longer works. Examples include systems that do not support modern deployment models or make it impossible to implement required features such as multi-region availability or advanced data processing.\n\nThe most probable hazards when fully rebuilding the system are long timelines, feature freezes, duplicated work, and the loss of undocumented business logic. These risks can be mitigated by breaking the rebuild into smaller, independently deployable components and by validating assumptions through pilots before expanding scope, but keep in mind, they never disappear entirely.\n\nReplacing custom systems with SaaS or COTS products is a different way. This approach works best when the domain is not a source of competitive advantage and when the external alternative is mature and well-supported. Areas such as billing, CRM, HR, or internal analytics often fall into this category. The benefits are faster time to value and reduced operational burden, while the trade-offs include vendor lock-in, integration complexity, and limited customization.\n\nIn both cases, the replacement decision should be made under strategic focus, not by frustration alone."},{"body":"## Isolate: progressive modernization without stopping the business\n\nIsolation strategies, often implemented using the **strangler** **fig** **pattern**, are designed for situations where the system is too critical to risk a big-bang change, but too constrained to just leave. Instead of modifying or replacing the legacy core directly, a new layer is introduced around it.\n\nIn practice, this means **plugging an API gateway or proxy** in front of the existing system so that all traffic flows through a controlled entry point. Then, new functionality is implemented alongside the legacy code, and specific routes or use cases are gradually redirected to the new components. Over time, as confidence grows, more traffic is shifted until the legacy parts can finally be retired.\n\nThis approach is particularly effective for **revenue-critical systems** with zero downtime tolerance or for architectures where internal refactoring would be extremely risky. It also aligns well with evolutionary architectures, allowing teams to move toward services and modern platforms without committing to an all-or-nothing transition.\n\nOf course, there are challenges – integration layers can become complex and bring in new failure modes. Data synchronization between old and new systems requires careful design (especially when dual writes or backfills are involved). The process is also time-consuming by nature and demands discipline to avoid creating a new layer of chaos.\n\nThe reward in this scenario is **control** as each step delivers measurable improvements while keeping the business running."},{"body":"## Legacy systems decision matrix for scaleups\n\nLegacy modernization is not about finding the \"best\" approach but choosing **the least wrong option** for a specific domain under real business constraints.\n\nA decision matrix helps make those trade-offs explicit. Instead of debating refactor versus rewrite, it forces a domain-by-domain assessment based on factors that actually matter in a scaleup: business criticality, tolerance for downtime, delivery pressure, technical debt, and available skills.\n\nThe matrix should not be applied to the platform as a whole since different parts of the system carry different levels of revenue risk and strategic importance. Used correctly, the matrix shifts the conversation from architectural preferences to **business** **outcomes** making the decisions **easier to explain and defend.**\n\nThe table below is a practical tool for evaluating which strategy fits each domain and why:\n\n
Dimension Refactor Replace – Rebuild Replace – SaaS / COTS Isolate / Strangler Fig
Business criticality High – core revenue, customer-facing Medium to high, but current system actively blocks growth Low to medium, non-differentiating domains Very high – revenue-critical, downtime intolerant
Fit with current business model Still good, but slowing execution Poor – assumptions no longer match reality Good enough, industry-standard workflows Poor internally, but externally stable
Technical debt level Medium to high, but manageable Very high, systemic, architectural Irrelevant – debt outsourced Very high, unsafe to touch directly
Tolerance for downtime Low Medium to high (planned cutovers) Medium (migration windows) Very low
Time pressure High – improvements needed now Lower – strategic bet Medium – driven by vendor rollout Medium to high – gradual relief
Delivery pressure (roadmap) Must keep shipping continuously Often causes feature freeze Minimal impact if well-integrated Continuous delivery required
Domain differentiation High – embedded IP and business logic High – core competitive advantage Low – commodity functionality High – but implementation is fragile
Team familiarity with system High – team knows the code Often low or fragmented Low – shifts to integration skills Mixed – legacy knowledge + new stack
Required skill set Strong refactoring and testing discipline Architecture, distributed systems, migration Vendor management, integration Architecture, platform, data consistency
Upfront cost Low to medium High Medium (licenses + integration) Medium to high
Ongoing cost Gradually decreasing Medium to high Predictable subscription costs Medium – dual systems for a while
Risk profile Low to medium High Medium Medium but controllable
Typical horizon Short to mid-term Mid to long-term Short to mid-term Mid to long-term
What you optimize for Velocity and stability Long-term scalability and flexibility Speed and focus Risk reduction and continuity
\n\nFor a **monolith-specific view** on scaling and modernization challenges, see our guide on [scaling and modernizing monolithic apps](/blog/scaling-and-modernizing-monolithic-apps)."},{"body":"## Summary\n\nDeciding whether to refactor, replace, or isolate is rarely the hardest part. The real challenge is turning that decision into a plan that improves the system while the business keeps moving. Most scaleups cannot afford long pauses in delivery, and few can take on modernization work without clear links to revenue and team capacity.\n\nWhat usually helps is starting with a focused technical and business assessment rather than a rewrite plan. That means looking at systems by domain and **understanding** how each one affects delivery speed and operational risk. With that clarity, modernization becomes a string of manageable steps instead of a single high-stakes bet.\n\n[Boldare works with scaleups ](https://www.boldare.com/services/consulting-and-scaling)on this kind of groundwork. The goal is not to enforce a target architecture, but to help CTOs and product leaders map technical debt to business impact, choose where refactoring, isolation, or replacement makes sense, and build a modernization roadmap that runs alongside active product development.\n\nIf you are facing these trade-offs now, the next useful step is often a short, structured assessment that connects architecture decisions to delivery, risk, and cost. From there, it becomes much easier to modernize consciously without slowing the company down."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1770120553/Legacy%20systems.png","lead":"Legacy systems in scaleups are rarely the result of negligence or poor engineering. In most cases, they are the side effect of a product's early success as during the **MVP phase**, systems are built quickly to **validate** **assumptions** and **keep** **the** **momentum**. Those decisions are often correct at the time, but as the product moves into the next stage of the SDLC, they become a burden.\n\nWhen the company reaches meaningful scale, **reliability** and **operational** **efficiency** start to matter more than delivery speed alone. At this stage, many teams discover that their systems encode assumptions that are **no longer valid** due to evolution of various areas such as customer segment, pricing models or compliance requirements.\n\nThis is typically the moment when engineering leaders feel **stuck** – modernization is clearly necessary, but the path forward is not. This article looks at how scaleups can evaluate modernization trade-offs in practice and decide which option suits their situation best.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-02-02T13:44:29.470Z","slug":"refactor-replace-isolate-2026-cto-guide-modernizing-legacy-systems-scaleups","type":"blog","slugType":null,"category":null,"additionalCategories":["Tech","Strategy"],"url":null},"author":"Aleksander Dąbrowski","authorAdditional":null,"box":{"content":{"title":"Refactor, replace, or isolate? - 2026 CTO guide for modernizing legacy systems in scaleups","tileDescription":"A practical framework for CTOs and engineering leaders in scaleups to evaluate modernization strategies: refactoring, replacing, or isolating legacy systems.","coverImage":null},"coverImage":null}},"id":"201f3db5-6251-52e1-949a-8ab18bee724d"}},{"node":{"excerpt":"","fields":{"slug":"/blog/claude-code-experts-why-does-ai-fail-in-java-teams/"},"frontmatter":{"title":"Claude Code Experts – Why Does AI Fail in Java Teams?","order":null,"content":[{"body":"## Event Details\n\n**Date:** Thursday, February 26, 2026 **Time:** 4:00 PM CET **Duration:** 25 minutes **Format:** Online webinar **Organizer:** Boldare\n\nFor engineers, developers, and product teams, understanding the challenges and best practices of AI in complex environments is critical to success. This event focuses on the practical application of AI within large backend systems, specifically those built with Java.\n\n## Why Should You Attend?\n\nWhile AI is often showcased in demos as the solution to a wide range of challenges, its implementation in real-world, large-scale backend systems can be a tricky and nuanced endeavor. Many teams struggle with [integrating AI into their development workflows](https://www.boldare.com/blog/ai-outsourcing-partner-benefits), facing issues with predictability, control, and team adoption.\n\nBoldare's Piotr Majchrzak, co-CEO, and Maciej Król, Senior Software Engineer, will share their insights from years of experience in the industry, providing valuable lessons for Java teams considering AI in their development processes.\n\n## Key Takeaways\n\n### Ensuring Predictability and Control of AI in Large Systems\n\nLearn how to [integrate AI into enterprise backend systems](https://www.boldare.com/blog/claude-code-enterprise-backend-use-cases-benefits) without losing control over the process or compromising system stability. The session will explore practical approaches for maintaining predictability while introducing AI into your workflow.\n\n### Practical Use Cases in Daily Engineering Work\n\nDiscover how AI can assist with crucial engineering tasks like refactoring, testing, and code reviews. These real-world examples will show how AI can complement and optimize everyday tasks within the development process. Learn about [mature approaches to introducing AI in Java systems](https://www.boldare.com/blog/introducing-ai-in-mature-java-systems-layered-approach) that have proven successful in production environments.\n\n### Safe Implementation of AI in Production\n\nImplementing AI in production environments without jeopardizing quality or performance is a top concern for many teams. Piotr and Maciej will guide you through safe implementation strategies to ensure AI doesn't disrupt your product's reliability.\n\n### Scaling AI Across an Organization\n\nOnce AI has been successfully integrated into a project, scaling it within the organization presents its own set of challenges. Learn how to go from pilot projects to full adoption, establishing best practices and standards that can support AI growth within your teams. Discover how [automation and AI can accelerate software development](https://www.boldare.com/blog/ai-automation-software-development-boldare) across your entire organization.\n\n### Dealing with Skepticism and Resistance\n\nIntroducing AI into a development team can be met with resistance, especially from engineers who may feel uncertain about its impact on their work. Piotr and Maciej will share strategies for managing skepticism, encouraging adoption, and effectively leading teams through the transition to AI-enhanced development practices.\n\n## About the Speakers\n\n**Piotr Majchrzak**, Co-CEO at Boldare, has extensive experience in leading the adoption of innovative technologies in product development. He brings a wealth of knowledge on the strategic integration of AI into software engineering practices, particularly for complex systems.\n\n**Maciej Król**, Senior Software Engineer at Boldare, has over 8 years of experience working with Java-based systems. Maciej is a seasoned AI practitioner who bridges the gap between traditional software development and emerging AI-driven technologies.\n\n## About the APBC Tech Series\n\nThe APBC Tech Series is designed to share real, hands-on experience in building, scaling, and delivering digital products. This includes exploring the ways AI helps teams work smarter and faster. The series is aimed at anyone involved in the creation of digital products—whether you're a designer, developer, or part of a product team.\n\nPowered by Boldare, a company with over 20 years of experience in digital product creation, the series offers actionable insights into how AI can revolutionize the way we work and build software. It's a must-attend event for anyone looking to stay ahead of the curve in tech innovation.\n\n## Who Should Join?\n\nThis session is perfect for:\n\n- Java backend developers and engineers - Engineering managers and tech leads - CTOs and technical decision-makers - Product managers working with backend teams - Anyone interested in practical AI implementation in enterprise systems\n\nIf you're interested in learning how AI can improve your backend systems, particularly if you're part of a Java development team, this session will provide you with practical knowledge from industry experts who have successfully implemented AI in large-scale environments.\n\n## Join the Community\n\nBe part of the Agile Product Builders Community and stay connected with experts and practitioners from the world of AI and digital product development. Sign up for updates and get access to future webinars, events, and exclusive content.\n\n[Join the Agile Product Builders Community](https://www.boldare.com/community)\n\n---\n\n**Ready to take your Java team's AI implementation to the next level?** Register now for this free webinar and gain the insights you need to successfully integrate AI into your backend development workflows."}],"job":null,"photo":null,"slug":null,"cover":"__Image link__","lead":"As AI continues to make waves across industries, its integration into the software development lifecycle has become inevitable. It's no longer a question of if but how artificial intelligence can play a role in backend systems, especially when it comes to large-scale enterprise applications. Join us on February 26, 2026 for an insightful 25-minute session where Boldare's experts share their experience on implementing AI in Java teams.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-01-30T10:00:00.000Z","slug":"claude-code-experts-why-does-ai-fail-in-java-teams","type":"blog","slugType":null,"category":null,"additionalCategories":["Tech","Ideas"],"url":null},"author":"Magdalena Chmiel","authorAdditional":null,"box":{"content":{"title":"Why Does AI Fail in Java Teams?","tileDescription":"Join our free webinar on Feb 26 to learn practical strategies for implementing AI in Java backend systems. Discover real-world use cases, safe implementation approaches, and how to overcome team resistance.","coverImage":null},"coverImage":null}},"id":"d3978297-b4f1-5194-b69e-df4e7c412997"}},{"node":{"excerpt":"","fields":{"slug":"/blog/how-to-choose-software-partner-backend-projects-java-dotnet-2026/"},"frontmatter":{"title":"2026 guide: How to choose a software partner for enterprise backend projects (Java and .NET)","order":null,"content":[{"body":"## How to define project requirements for a backend project?\n\nBefore selecting the right technology partner for your backend project, it's crucial to take the time to clearly define your project's requirements. While it may seem like a straightforward step, many organizations make the mistake of rushing into partner selection without fully understanding what the project requires. This often leads to misaligned expectations, missed opportunities, or unnecessary complications later in the project.\n\nHere are some fundamental questions you should ask yourself, your team, and any potential partners to ensure a solid foundation for the project:\n\n**What are the core business goals of the project?**\n\nEvery backend system is built to solve specific business problems. For example, when working with clients like BlaBlaCar, [the focus was on scaling their SaaS platform](https://www.boldare.com/industries/future-proof-your-saas-product), which involved addressing the complex business logic around user interaction and data flow to ensure scalability across regions. Whether the goal is to automate internal workflows, develop a customer-facing service, or power an enterprise platform, the backend must be designed to meet those business objectives. Understanding these goals from the outset ensures that your backend solution is aligned with business needs and can scale as the organization grows. Without clear business objectives, there's a risk of building a technically sound system that doesn't actually support the company's strategic direction.\n\n**How complex is the business logic that needs to be implemented?**\n\nThe business logic is at the heart of any backend system. It defines how data flows, how different processes interact, and how requests are handled. If the logic is complex, the system architecture will need to support that complexity without becoming a bottleneck. Be sure to assess the complexity of the workflows, approval systems, decision-making processes, and any integrations with other systems. Understanding this complexity helps in selecting the right architecture, technology stack, and approach for building the system.\n\n**Are there integration requirements with existing systems?**\n\nFew backend systems operate in isolation. Whether it's interacting with legacy systems, third-party APIs, or other internal services, understanding the integration requirements is key to the project's success. Ask yourself what existing systems or services need to be integrated into the new backend and how the system will need to interact with those external components. This will inform your choice of technology and ensure seamless integration across platforms.\n\n**What is the expected scale of the system?**\n\nScalability is one of the most critical factors in backend development. A system that works well for a small number of users might fail under heavy load. Therefore, it's essential to understand the expected scale, not just in terms of user count but also data volume and transaction rates. Will your system be handling thousands, millions, or potentially more? Understanding the scale early on will guide decisions around infrastructure, data storage, and performance optimization, ensuring that the system can grow with your business needs.\n\n**How much flexibility will the system need in terms of future changes or scaling?**\n\nThe technology and business landscapes are constantly evolving. One of the biggest challenges is designing a backend system that can adapt to future changes, whether it's new features, shifting business requirements, or changes in technology. For instance, your backend may need to integrate with emerging technologies like AI or machine learning down the road. Your partner should help you plan for this flexibility, designing an architecture that accommodates future growth and changes without requiring costly overhauls.\n\nA clear understanding of these factors will not only guide the choice of technology but also help you choose a partner who can deliver a solution that meets your current and future needs. By clearly defining these requirements upfront, you set the stage for a smoother project execution and ensure that the chosen partner is aligned with your business objectives and technical requirements.\n\nIf you're looking for guidance on selecting the right development partner more broadly, check out our article on [choosing software development companies in Europe](https://www.boldare.com/blog/7-trusted-software-development-companies-in-europe/).\n\n## How to evaluate the experience of your potential backend software partner?\n\nWhen selecting a backend development partner, experience is the key differentiator. Backend systems are often complex, and only those with a proven track record in handling large-scale, intricate systems can successfully manage the various challenges and edge cases that arise.\n\nTo ensure your partner has the necessary expertise, look for experience in the following areas:\n\n**Large-scale systems:** Your partner should have experience building systems capable of handling millions of users or high transactional loads. In the Sonnen Digital Transformation project, the backend needed to ensure smooth performance during energy data collection, monitoring, and storage across multiple platforms. They need to understand how to optimize backend performance, ensure uptime, and guarantee high availability, especially under heavy traffic conditions.\n\nAsk for case studies or examples of systems they've built that had to scale and maintain performance under pressure.\n\n**Microservices and scalability:** As your organization grows, so too will the demands on your backend system. Your partner should be skilled in designing scalable microservices architectures that allow for flexibility, rapid growth, and the ability to handle spikes in demand. This includes knowledge of how to break down large systems into smaller, independently deployable services and ensure they can scale horizontally.\n\nFor a deeper understanding of microservices architecture, read our comprehensive guide on [microservices as an alternative to monolithic architecture](https://www.boldare.com/blog/microservices-architecture-definition-benefits/).\n\n**Best practices in Java/.NET:** The right partner will have extensive experience working with Java or .NET, depending on your technology preference. They should be able to demonstrate how they've utilized these technologies to solve complex backend challenges, following best practices in areas like security, system design, and data management. A reliable partner will have a portfolio of successful projects and be able to provide examples of how they addressed real-world issues in these specific technologies.\n\nExperience in these areas not only demonstrates technical competence but also reflects the ability to foresee potential obstacles and find effective solutions. A partner with this experience will ensure that your backend is built on a solid foundation, capable of handling both current and future business needs.\n\n## How to verify the approach to quality and security of your backend software partner?\n\nWhen building backend systems, especially those incorporating advanced technologies like AI, quality and security must be foundational elements, not afterthoughts. The reliability and safety of the system are essential to ensuring that it performs well under load and remains secure in the face of potential threats.\n\nHere's what to look for when evaluating your partner's approach to quality and security:\n\n**Security:** Security is a non-negotiable aspect of any backend system, especially when dealing with sensitive data or integrating third-party services. Your partner should have a clear and proven methodology for securing APIs, ensuring data privacy, and protecting sensitive information in accordance with industry standards (such as GDPR, SOC 2, etc.). Ask how they have managed security in past projects—particularly when integrating external systems or incorporating AI components. They should be able to explain how they ensure data protection, prevent unauthorized access, and secure communication channels between systems.\n\n**Testing and monitoring:** A robust testing strategy is crucial for maintaining system integrity. Your partner should employ rigorous testing practices, such as unit testing, integration testing, and performance testing, to ensure that the backend system functions as expected and handles any edge cases. In addition to testing, proactive monitoring should be in place to track system performance, identify potential issues early, and ensure the backend operates smoothly in real-time. For high-traffic systems or those requiring high availability, continuous monitoring ensures that any deviations in performance are quickly detected and addressed.\n\n**Risk management:** Every backend system has risks—whether technical, security-related, or operational. Your partner should have a solid risk management strategy in place that includes identifying potential risks early in the development process and devising strategies to mitigate them. This becomes particularly important when integrating AI, as AI models can introduce new uncertainties and challenges. Your partner should be able to demonstrate how they approach the testing, monitoring, and continual adjustment of AI models to ensure they perform consistently over time. How will they ensure that the AI models remain effective and secure post-deployment, and how will they handle any unforeseen risks?\n\nIncorporating a proactive approach to security, testing, and risk management ensures that your backend system is not only functional but also secure, reliable, and able to scale over time. A partner who values these practices will provide you with a stable foundation for future growth and minimize the likelihood of costly issues down the road.\n\n## How to check if your backend partner is experienced in managing large teams?\n\nIn backend development, particularly in complex, large-scale projects, effective team management is just as important as technical expertise. Coordinating teams across various departments – such as product, design, and operations – ensures that all stakeholders are aligned, and the project is executed smoothly. Your partner should have proven experience in managing distributed teams, leveraging agile methodologies, and providing robust DevOps support to facilitate seamless collaboration across all stages of the development lifecycle.\n\nHere's what to consider when evaluating your potential partner's team management capabilities:\n\n**Managing large, cross-functional teams:** A successful backend project often requires a multi-disciplinary approach, with teams of developers, engineers, designers, and business stakeholders working together. Your partner should have a track record of managing such diverse teams, ensuring effective communication and alignment between different departments. Ask how they've managed complex, multi-team projects in the past and how they handled collaboration across different functions.\n\n**Agile and DevOps:** A partner who understands Agile methodologies and DevOps practices will ensure that your backend development is flexible, iterative, and efficient. Agile methodologies allow for quick adjustments and responsiveness to feedback, while DevOps practices enable smooth deployment and continuous integration. Your partner should be comfortable with regular releases, ongoing iterations, and maintaining quick feedback loops. This is especially critical in backend systems, where complexity often requires rapid changes and flexibility.\n\n**Collaboration and communication:** Managing large teams isn't just about technical leadership; it's also about ensuring that communication remains fluid and transparent throughout the project. Ask your potential partner about their strategies for team coordination, how they handle feedback, and how they ensure all teams are aligned with the project's overall goals. A partner who has experience in managing large, collaborative teams will be able to efficiently address challenges and ensure the project progresses without significant delays.\n\nA partner with strong experience in team management, Agile, and DevOps practices will help guide your backend project to success, ensuring that all team members work in harmony and that challenges are tackled proactively. This experience is essential to ensure your project is completed on time, within budget, and meets your business objectives.\n\n## How to check if your backend partner ensures scalability and performance?\n\nScalability and performance are foundational to the success of any backend system. As your business grows, so will the demands on your backend – whether it's an increasing number of users, larger volumes of data, or more complex transactions. Your partner must have a proven track record of designing and optimizing systems that can scale effectively while maintaining high performance.\n\nHere's what to consider when evaluating your partner's approach to scalability and performance optimization:\n\n**Scaling backend systems:** A scalable system is one that can grow seamlessly as the business expands. Your partner should have experience working with cloud infrastructure (e.g., AWS, Azure, GCP) and be familiar with horizontal scaling to manage increasing loads. In the Prisma project, for example, we utilized AWS to deploy the backend, ensuring that the system could scale horizontally and handle growing traffic without impacting performance. We implemented Event Sourcing and domain-driven design to ensure the backend could efficiently manage complex data flows while maintaining high availability.\n\n**Load testing and performance optimization:** Backend performance tuning is a skill that requires deep technical expertise. Your partner should have a solid understanding of how to optimize system performance across the stack, from database indexing to API efficiency, and caching strategies. They should be able to demonstrate how they've addressed performance bottlenecks in past projects, ensuring that response times remain low – even under high load. Performance optimization is a continuous effort, especially for backend systems that need to handle large amounts of data or complex transactions.\n\nFor more insights on building scalable systems, explore our article on [how to build applications you won't have to rewrite using the MACH framework](https://www.boldare.com/blog/mach-framework/).\n\n## How to verify if your backend partner will ensure long-term support?\n\nThe work doesn't stop once your backend system is deployed. Long-term success depends on your partner's ability to provide ongoing support, maintenance, and updates to ensure the system remains secure, up-to-date, and efficient over time. Choosing a partner who understands the importance of long-term support is critical for maintaining the system's performance and adapting to future needs.\n\nHere's what to look for in a partner's approach to post-launch support:\n\n**Ongoing maintenance:** Backend systems require constant monitoring and maintenance to ensure they remain secure and perform optimally. Ask how your potential partner plans to provide ongoing support after the system is live. This includes regular security patches, performance monitoring, and updates to keep the system in peak condition. A proactive partner will ensure that any potential issues are identified and resolved before they impact users or business operations.\n\n**Feature updates:** As your business grows, your backend system may need new features or enhancements. Your partner should have a clear strategy for handling long-term feature updates and improvements. Ask them about their approach to adding new features, prioritizing technical debt, and ensuring that the system remains adaptable to evolving business needs. A partner who offers a structured roadmap for future development ensures that your backend system will continue to evolve in alignment with your organization's goals.\n\nBy choosing a partner who is committed to long-term support and scalability, you ensure that your backend system remains robust, adaptable, and prepared for future growth. A solid partnership can help prevent future disruptions, enabling your organization to scale with confidence and optimize backend performance as you expand.\n\n## How to check if your backend partner is AI-Native in the development process and follows best engineering practices?\n\nIncorporating AI into backend development can provide significant benefits, such as improving performance, automating repetitive tasks, and enhancing decision-making processes. However, not all AI implementations are the same, and it's important to ensure that your backend partner doesn't just use AI, but integrates it in a way that adheres to best engineering practices.\n\nTo assess whether your partner is truly AI-native in their development approach and follows industry-leading engineering standards, consider the following:\n\n**Proven AI expertise:** A partner that is AI-native will have substantial experience embedding AI into backend systems across multiple projects. Ask for concrete examples of AI-powered solutions they've implemented. Inquire about the specific tools, frameworks, and methodologies they've used and whether those align with industry best practices. This will show their depth of knowledge in applying AI to solve real business problems in a scalable, reliable way.\n\nTo understand more about AI readiness in software development, read our insights on [whether AI is ready for real software development](https://www.boldare.com/blog/is-ai-ready-for-real-software-development-insights-from-michal-czmiel/).\n\n**AI model management:** Proper management of AI models is essential for long-term success. Ask your partner how they handle the lifecycle of AI models: from training, validation, deployment, to continuous monitoring and updates. They should be able to explain how they ensure AI models stay accurate, efficient, and adaptable to new data and business requirements. A partner experienced with AI will have a structured process for managing model drift and ensuring models are optimized continuously.\n\n**Data quality and security:** AI's effectiveness is rooted in the quality of the data it trains on. Your backend partner should have strict protocols for collecting, cleaning, and managing data. They should demonstrate how they ensure data used for AI is secure, relevant, and unbiased. Make sure they follow data privacy regulations (e.g., GDPR) and implement strong security measures to protect sensitive information.\n\n**Seamless AI integration:** AI should not disrupt your existing systems, but rather integrate smoothly with them. Your partner should be able to demonstrate how they've deployed AI in past projects without negatively impacting system performance or stability. Ask about their experience with integrating AI into large-scale backend systems and whether they use proven architectural patterns (like microservices) to ensure scalability and flexibility.\n\nBy ensuring that your backend partner is AI-native – meaning AI is fully integrated into their development process, with a strong focus on engineering best practices – you can be confident that AI will be a valuable, secure, and reliable component of your backend system. This approach ensures not only effective AI deployment but also long-term stability and scalability for your business.\n\n## Why Boldare is the trusted partner for your complex backend project?\n\nAt Boldare, we've helped companies like Sonnen, Prisma, and BlaBlaCar tackle complex backend challenges by delivering innovative, scalable, and secure solutions. With over 20 years of experience in real software delivery, our exceptional backend engineers understand how to design and implement systems that perform under pressure, scale with growth, and evolve with the business. From managing large-scale integrations to optimizing performance and implementing AI technologies, our expertise in enterprise backend systems makes us a trusted partner for even the most complex projects.\n\n[To dive deeper into implementing AI in backend systems](boldare.com/services/claude-code-experts), we invite you to join our [**Claude Code Experts: Why Does AI Fail in Java Teams – and How to Make It Production-Ready?** webinar.](https://events.zoom.us/ev/ApvY7AJlYKSHiNxHg1tihnC3Ot-XRpG0mXeOqbJWR3mlES4YbVqY~AjtKSN5k7D0q0ReCWYbQaFrYcHPPpuOdVcBYLS50592Enm-YYOnSJQXCozmkjGFYgGF4OZq6QieuElwCupO7V1ostQ)\n\n[**Sign up for the webinar here** ](https://events.zoom.us/ev/ApvY7AJlYKSHiNxHg1tihnC3Ot-XRpG0mXeOqbJWR3mlES4YbVqY~AjtKSN5k7D0q0ReCWYbQaFrYcHPPpuOdVcBYLS50592Enm-YYOnSJQXCozmkjGFYgGF4OZq6QieuElwCupO7V1ostQ)– all registrants will receive a recording of the session. Don't miss the opportunity to learn from our extensive experience in enterprise backend projects!\n\n\n\n\n\n**FAQ:**\n\n**1. What should I look for when evaluating a potential backend software partner for my project?**\n\n* Look for experience in managing large, complex backend systems, expertise in relevant technologies (Java, .NET), proven track record in scalability and performance optimization, as well as their approach to security, quality, and long-term support.\n\n**2. How do I ensure my backend partner is experienced with AI and how it can be integrated into my system?**\n\n* Ask for examples of past AI projects, how they integrate AI into existing backend systems, and their methodology for managing AI models. Ensure they follow best practices and maintain a strong focus on security, data quality, and system stability.\n\n**3. What are the key challenges in integrating AI into an enterprise backend, and how can they be overcome?**\n\n* The challenges include maintaining performance, ensuring system security, and integrating AI models with existing architectures. Overcoming these requires a clear AI strategy, proper testing and monitoring, and working with experienced teams who understand both AI and backend systems.\n\n**4. How do I know if my partner’s approach to scalability and performance is suitable for my project’s long-term needs?**\n\n* Verify their experience in scaling backend systems, especially in cloud environments like AWS or Azure. Check if they have expertise in handling high transactional loads, optimizing performance, and ensuring systems remain stable as they scale.\n\n**5. What steps should I take to ensure my backend partner can handle future updates and long-term support?**\n\n* Confirm their approach to maintenance, updates, and security patches. Ask about their post-launch support process and how they ensure the system remains adaptable and scalable to meet evolving business requirements."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1754311516/Group_26086304_vavulx.png","lead":"The rise of AI-assisted tools makes the process of choosing a partner even more challenging than before. You may ask – why? The truth is that while AI reduces both cost and effort, it's still crucial to select a partner who not only experiments with AI but has experience implementing it effectively in production. \n\nThis challenge extends to backend development, where complexity increases not just due to the technology, but also because of the long-term demands of system maintenance. **For CTOs, the key question is how to build a system that is scalable, flexible, and capable of evolving as the organization grows.** \n\nEvery backend solution brings challenges that can impact the future development of the organization – from integration with other systems, to data and security management, and post-deployment support. With AI playing an increasing role,**[ the real question is how to leverage its potential in the backend without destabilizing the system. ](https://www.boldare.com/blog/introducing-ai-in-mature-java-systems-layered-approach/)**\n\nTherefore, choosing a technology partner who not only understands these challenges but can solve them in practice is crucial for the success of your project and organization. \n\nThis is why we've created the **2026 guide: how to choose a software partner for backend projects (Java and .NET)**. In this guide, we provide structured insights on how to evaluate potential partners for backend projects, drawing on the common problems and challenges we encounter in our work with clients. This compendium will help you navigate the partner selection process.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-01-29T11:35:17.957Z","slug":"how-to-choose-software-partner-backend-projects-java-dotnet-2026","type":"blog","slugType":null,"category":null,"additionalCategories":["How to"],"url":null},"author":"Magdalena Chmiel","authorAdditional":null,"box":{"content":{"title":"2026 guide: How to choose a software partner for enterprise backend projects (Java and .NET)","tileDescription":"Guide for CTOs on choosing the right partner for enterprise backend projects in Java and .NET, with insights on AI integration and scalability.","coverImage":null},"coverImage":null}},"id":"510884a2-1baf-5ab6-abfd-fb3aefb71a56"}},{"node":{"excerpt":"","fields":{"slug":"/blog/how-claude-code-works-in-enterprise-backend-systems-3-use-cases-and-benefits/"},"frontmatter":{"title":"How Claude Code works in enterprise backend systems – 3 Use cases and benefits","order":null,"content":[{"body":"## What is Claude Code and how does it work in enterprise backend systems?\n\nClaude Code is a tool that runs in the terminal or IDE and operates directly on a project’s **repository**. Instead of responding to isolated prompts with disconnected code snippets, it can understand the entire **repository** **structure**, **file** **dependencies**, and **project-specific rules**. Developers can issue high-level instructions in natural language and expect Claude Code to analyze the codebase, make **coordinated changes across multiple files**, and propose results that fit into existing workflows.\n\nIn practice, Claude Code integrates naturally with tools already used by backend teams. It can be used from **VS Code** or the **command** **line**, connected to **GitHub** for pull requests and reviews, and embedded into **[CI/CD pipelines](https://www.boldare.com/blog/continuous-delivery/)** to support automated feedback and checks. In enterprise setups, it is typically accessed through team or enterprise plans, sharing identity and access management with Claude chat, simplifying onboarding and reducing operational friction.\n\nThe main difference between Claude Code and a generic large language model (LLM) is **agency** and **context** since Claude Code operates on real repositories rather than pasted fragments of code. It supports multi-step workflows such as refactoring a module, updating tests, and validating changes against existing conventions. It’s not designed for isolated code generation but for backend realities such as integration work, refactoring, and test automation.\n\n## Use case 1: Using Claude Code for API development and maintenance in enterprise backends\n\n**API** **development** is one of the most common tasks in enterprise backend teams and also one of the easiest places for complexity to get overwhelming. A single endpoint can touch domain logic, validation, authentication, logging, documentation, and often multiple downstream consumers. In such systems, even small API changes can spread across services and repositories, especially when teams are working on **legacy** **codebases** that have evolved over many years.\n\nClaude Code fits perfectly into this workflow because it operates with **full awareness of the codebase** and its conventions. Instead of generating generic endpoint templates, it can analyze existing controllers, services, and domain models, then introduce new endpoints or refactor existing ones in a way that supports long-term **API architecture optimization**. \n\nMany teams write down their standards like **architectural** **rules**, **naming** **conventions**, **error** **handling**, **logging** **formats**, and testing expectations in a **CLAUDE.md** file stored alongside the repository. Guided by these rules, Claude Code starts behaving like a teammate that understands how the system is meant to be maintained.\n\n**Documentation** is another pain point in backend systems. OpenAPI specifications, README files, and change logs tend to drift away from reality as code evolves. One of the strongest Claude Code’s benefits is that it can **regenerate or update API documentation** directly from the implementation, helping teams keep contracts accurate without turning documentation into a separate manual task.\n\nThis makes **API development and maintenance** a strong entry point for AI adoption. The scope is bounded, changes are easy to validate, and the impact is immediately visible. \n\n## Use case 2: Debugging and incident analysis in large backend systems with Claude Code\n\n**Debugging** very often starts with **fragmented** **logs**, partial signals from monitoring tools, and an issue that only appears under specific load or data conditions making the bugs hard to replicate. Engineers spend a significant amount of time not fixing problems, but **reconstructing** **context** to understand what actually went wrong.\n\nClaude Code can minimize this struggle by acting as a **context-aware analysis assistant** during incident response. When provided with logs, stack traces, or error reports, it can correlate them with the surrounding **codebase**, **configuration**, and **recent** **changes**. Instead of treating logs as isolated text, it finds where failures originate, which components are involved, and how data flows through the system at the moment something breaks.\n\nAnother valuable aspect is **test generation during incident resolution**. After identifying a likely cause, Claude Code can generate tests that reproduce the failure scenario based on the observed behavior. This allows teams to validate fixes quickly and, just as importantly, lock in protection against the same type of incident recurring in the future.\n\nClaude Code does not replace monitoring or alerting tools, but it **shortens the path from alert to understanding**. For enterprise backend teams dealing with large systems and limited observability, this can significantly reduce the time to resolution while preserving human ownership of decisions and fixes.\n\n## Use case 3: Reducing technical debt and refactoring legacy backend code with Claude Code \n\n**Technical debt** in enterprise backend systems can accumulate through years of changing requirements, shifting team structures, and necessary trade-offs made under delivery pressure. Over time, codebases become hard to navigate, duplication spreads, and architectural boundaries blur. Many teams are aware that refactoring is necessary, but in practice it is often postponed because of its scope and risk.\n\nClaude Code is particularly effective in this area because it can **reason across large parts of a codebase** at once. Instead of applying local, file-by-file changes, it can analyze patterns that repeat across modules, services, or repositories. This makes it suitable for refactoring tasks that would otherwise require significant manual coordination, such as standardizing code style, merging duplicated logic, or reorganizing modules around clearer responsibilities.\n\nClaude Code can also help **enforce architectural decisions** by identifying concerns that should be extracted into shared components or services. When teams decide to introduce new boundaries, deprecate old abstractions, or align implementations with updated architectural guidelines, the assistant can apply those decisions consistently across the system.\n\nWhat makes this use case particularly valuable is its **long-term impact**. While the immediate benefits come from reduced manual effort, the larger gain is improved system clarity and maintainability. Over time, teams that use Claude Code for refactoring create codebases that are easier to evolve and onboard into.\n\n## Benefits of implementing Claude Code in enterprise backend teams\n\nThe proper Claude Code adoption in the backend system can be really impactful as its benefits spread across **delivery speed**, **system quality**, and **organizational leverage**.\n\nFrom the productivity perspective, it’s not more outputs that make a difference but the **faster** **flow** – backend teams spend less time navigating unfamiliar code, rewriting boilerplate or coordinating multi-file changes manually. Cognitive load of everyday tasks like API maintenance, debugging or refactoring can be absorbed by the AI assistant significantly accelerating the process. This **shortens pull request cycles** and allows smaller teams to maintain systems that would otherwise require significantly more engineering capacity.\n\nDue to Claude Code' reinforcement of **consistency across the codebase**, the quality and reliability improve along with the speed. This allows the tests to be generated more often, refactors applied more systematically leading to more consistent architectural decisions. \n\nIn enterprise reality, **governance** is often the deciding factor. Claude Code can be introduced with clear guardrails around access, auditing, and data handling, allowing organizations to meet security and compliance requirements without slowing teams down.\n\nWith all these reliefs, time saved on routine backend work opens the capacity for **higher-value initiatives**. Teams can spend less effort on maintenance firefighting and more on product evolution, improving time to market and ROI.\n\nMost importantly, Claude Code changes what is possible – large refactors and legacy modernization efforts, once seen as too risky or time-consuming, become doable. Backend systems shift from being a constraint on growth to an asset that can evolve alongside the business.\n\n## From AI experiments to production-ready adoption in backend systems\n\nThe real challenge of implementing AI coding assistants is no longer access to tools but knowing how to apply them in **complex** **backend** **systems** without increasing risk or technical debt. As the examples in this article show, such adoption can be a real game-changer when applied consciously.\n\nFor engineering leaders, this is not a question of replacing developers or automating decisions. It is about **augmenting** **teams** so they can handle complexity more effectively, modernize systems, and keep backend platforms evolving alongside the business.\n\nFrom our perspective as a delivery and consulting partner, the value of Claude Code is most visible in complex backend environments where **speed** **and** **reliability** must go hand in hand. Its impact is about integrating it into existing development workflows, architectures, and organizational constraints.\n\nIf you are considering introducing AI into your backend organization but are unsure where to start or how it applies to your specific system, our **[Claude Code experts](https://www.boldare.com/services/claude-code-experts)** can help assess your current setup, identify realistic use cases, and define a safe path to **production-ready adoption**.\n\nInterested in a deeper discussion focused specifically on Java backend teams? We will cover common AI failure scenarios and adoption patterns in our upcoming webinar: **[Claude Code Experts: Why does AI fail in Java teams?](https://events.zoom.us/ev/ApvY7AJlYKSHiNxHg1tihnC3Ot-XRpG0mXeOqbJWR3mlES4YbVqY~AjtKSN5k7D0q0ReCWYbQaFrYcHPPpuOdVcBYLS50592Enm-YYOnSJQXCozmkjGFYgGF4OZq6QieuElwCupO7V1ostQ)**.\n\n## **FAQ**\n\n\n\n**1. What is Claude Code and how is it different from a generic AI coding assistant?**\n\nClaude Code is an AI coding assistant that operates directly on a real code repository rather than isolated prompts or pasted snippets. It understands project structure, file dependencies, and repository-specific rules, allowing it to make coordinated changes across multiple files. Unlike generic AI models, it is designed for multi-step backend workflows such as refactoring, test generation, and API maintenance in enterprise systems.\n\n**2. Can Claude Code be safely used in enterprise backend systems?**\\\nYes. Claude Code can be introduced in enterprise environments with clear governance controls, including access management, auditing, and integration with existing CI/CD pipelines. It operates within established development workflows and does not bypass human review, making it suitable for organizations with security, compliance, and reliability requirements.\n\n**3. What backend tasks benefit most from using Claude Code?**\n\nClaude Code is most effective in tasks that require repository-wide context, such as API development and maintenance, debugging and incident analysis, and refactoring legacy code. These tasks often span multiple modules and files, making them difficult for generic AI tools that lack full system awareness.\n\n**4. How does Claude Code support debugging and incident response?**\n\nDuring debugging, Claude Code can analyze logs, stack traces, and error reports in the context of the surrounding codebase and recent changes. It helps engineers trace failures to their source and can generate tests that reproduce incidents, enabling faster fixes and reducing the risk of the same issue recurring.\n\n**5. Does using Claude Code reduce technical debt or increase it?**\n\nWhen used with clear architectural rules and guidelines, Claude Code can help reduce technical debt. It enables consistent refactoring across large codebases, supports standardization, and applies architectural decisions systematically. The tool does not replace engineering judgment but augments teams in maintaining long-term system quality."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1769525595/blog_z1mqxm.png","lead":"AI coding assistants have dominated the market in the blink of an eye, evolving from experiments used on side projects into outright instruments supporting **production-grade software development**. For lots of companies, this isn’t the reality, as many backend teams end up frustrated due to **shallow** **suggestions**, low-quality outputs, and a growing sense that AI is more distraction than help.\n\nThis tension becomes even more visible at the leadership level. Engineering managers and platform leads are asked to introduce AI, yet are left without a clear path on how to roll it out in enterprise backend systems in a way that delivers real value and fits existing architectures and workflows.\n\nRead this article to learn how **[Claude Code](https://claude.com/product/claude-code)** can be applied realistically in **enterprise backend environments** and how it differs from more generic approaches. We will walk through specific backend use cases that help teams move past shallow AI output toward **measurable impact in real-world systems.**","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-01-27T14:55:29.330Z","slug":"claude-code-enterprise-backend-use-cases-benefits","type":"blog","slugType":null,"category":null,"additionalCategories":["GenAI","Digital Product","Ideas"],"url":null},"author":"Aleksander Dąbrowski","authorAdditional":"","box":{"content":{"title":"How Claude Code works in enterprise backend systems – 3 Use cases and benefits","tileDescription":"Learn how Claude Code works in enterprise backend systems, with real use cases for APIs, debugging, refactoring, and legacy modernization.","coverImage":""},"coverImage":null}},"id":"131fcbbb-e039-579b-a369-9cfb869a7a86"}},{"node":{"excerpt":"","fields":{"slug":"/blog/claude-code-vs-copilot-choosing-the-right-tool/"},"frontmatter":{"title":"Claude Code vs GitHub Copilot: Choosing the right tool for enterprise backend systems","order":null,"content":[{"body":"## GitHub Copilot explained\n\nGitHub Copilot is an AI assistant that lives inside the developer's IDE, suggesting the code as you type and helping moving faster through common tasks. It works best when the **problem** **is** **well** **defined** and the solution follows known patterns, such as writing boilerplate, adding tests or making minor changes in pull requests. In these scenarios, Copilot is an efficient accelerator for routine engineering work.\n\nWhat makes Copilot especially useful in enterprise environments is not just speed alone, but the control that comes with it. Because it is part of GitHub's ecosystem, it fits naturally into existing workflows and tooling. Organizations can manage access centrally, apply usage and content policies, track activity through audit logs, and align with compliance requirements. This makes Copilot easier to introduce in larger teams where security and legal need clarity.\n\nIn short, Copilot acts as a **productivity boost** for individual developers. It shortens feedback loops and reduces the cognitive load of repetitive tasks, but it operates primarily at the level of **files**, **functions**, and **diffs**. As systems get larger and more complex, its understanding of the bigger picture becomes limited.\n\n## Claude Code as a thinking partner\n\nClaude Code approaches the work from a different angle – instead of focusing on small suggestions, it is designed to understand and reason larger and broader parts of the system. It can read **whole** **repositories**, acknowledge the project's structure and follow changes across multiple files and commits. It proves to be the most useful in tasks that require understanding more than typing.\n\nTeams choose Claude Code when working with **legacy** **systems** or poorly documented databases – it can help answer questions like where a certain business rule is implemented or how data flows through a service. Therefore, it acts more like a thinking partner than an autocomplete bot.\n\nClaude Code's enterprise features are improving, but it does not yet come with the same ready-made governance controls as Copilot. This means companies need to be more intentional about how and where it is used. The tool can deliver high leverage insights, but only when paired with clear processes and verification rather than blind trust.\n\n## Codebase size and system context\n\nThe key difference between the two tools appears as the systems grow – while Copliot handles local and small changes very well, it starts to struggle with understanding depending on how many parts of the system interact with each other.\n\nClaude Code, on the other hand, is better suited for that kind of complexity. It can follow flows across services, explain dependencies, and support changes that touch many files at once. For large backend systems, this kind of system level understanding is often more valuable than faster typing.\n\n## Legacy systems and Java - the reality of enterprise patterns\n\nThe frequent reason for enterprise backend systems (especially the Java ones) to fail is the amount of layers the system has grown over time and domain specific conventions that are only partially documented (if at all). Spring, Hibernate, event driven flows, custom security layers, and configuration heavy setups create an environment where understanding **context** **is vital**.\n\n**GitHub Copilot** performs well when working with standard, well known patterns. It is fast and accurate when generating controllers, repositories, configuration snippets, or test scaffolding. The problem appears when a system differs from textbook usage – in such cases, Copilot often produces **framework-correct code** that subtly ignores team conventions or historical constraints. This can lead to **slow erosion of the architecture** due to the boilerplate build-up, causing high maintenance costs.\n\n**Claude Code** does better on this ground. Instead of relying on generic patterns, it recognizes how a system actually works. By inspecting existing implementations and git history, it can explain why certain decisions were made, how custom abstractions are meant to be used, and where refactoring is safe.\n\nThis reveals the harsh truth about backend systems – they heavily rely on relationships between components and historical context. Choosing a tool that understands those connections is key for better decisions and less firefighting.\n\nIf you want to explore why AI tools often fail in Java teams and where Claude Code actually makes a difference, we’ll be covering this in an upcoming webinar: **[Claude Code Experts: Why does AI fail in Java teams?](https://events.zoom.us/ev/ApvY7AJlYKSHiNxHg1tihnC3Ot-XRpG0mXeOqbJWR3mlES4YbVqY~AqccGKQvHPtXd7EBefi06Jf6Hey1du1AbL47Z7Gb_vKrhM41aK5xHGAjUg)**\n\n## Security, compliance, and governance\n\nFor enterprise teams, one of the most important factors to consider before choosing the AI tool is whether the tool can be rolled out across the organization without triggering panic in security, legal, or compliance teams. At this point, the differences between Claude Code and GitHub Copilot are significant.\n\n**GitHub Copilot's** clear advantage is that it is enterprise-ready – due to its integration with GitHub, it allows organizations to manage access directly, enforce usage policies, apply content and IP restrictions, and maintain audit trails. Features like role based access, identity provider integration, and data residency support make Copilot easier to approve at a bigger scale.\n\n**Claude Code** approaches governance differently, and does not come with the same policy first mindset. The tool assumes a high level of responsibility and authority of the users, giving them more freedom in using the tool. This means that any policies have to be designed on the process level rather than enforced through settings.\n\nNeither of the approaches are right or wrong, they just reflect very different perspectives about how enterprises manage risk. So, picking the tool when it comes to the security area depends on the enterprise's nature, the approach and trust they have in their teams.\n\n## Decision matrix - choosing the right tool for the job\n\nConsidering all the aspects brought up previously, the question is no longer whether GitHub Copilot or Claude Code is better, but where each of them fits in the engineering process. In enterprise backend systems, different tasks require different kinds of support, and forcing a single tool to cover everything usually creates more friction than value.\n\n**Copilot** works best when **speed**, **consistency**, and **governance** are the priority. It is well suited for writing code faster, onboarding junior developers, and operating in compliance heavy environments where centralized controls and auditability matter. In these cases, it accelerates execution without changing how teams think about the system.\n\n**Claude Code** is more effective when the goal is **understanding rather than output**. Supporting senior engineers, analyzing large undocumented codebases, or reasoning about architectural decisions requires deep context and system level insight. This is where Claude Code provides leverage that suggestion driven tools cannot.\n\nThe matrix below reflects the differences:\n\n
Goal Tool Why
Write code faster Copilot Reactive, suggestion-driven, fast small changes
Understand systems Claude Code Deep context, git history, architecture
Onboard juniors Copilot Templates, standard patterns, safe defaults
Support seniors Claude Code Thinking partner, architecture, design decisions
Compliance Copilot Ready controls, audit trails, policies
Large undocumented legacy Claude Code Repo analysis, git blame, pattern extraction
\n\n## Summary\n\nFrom our experience, the biggest challenge for enterprise teams is not making the decision about the tool, but designing the process around it and finding ways to efficiently use it in their systems.\n\nThis is where most scaleups get stuck – they adopt AI to move faster, but without adjusting how decisions are made and validated. Over time, that gap becomes visible in the places that matter most: refactors that feel risky, legacy systems no one wants to touch, and architectural decisions that rely on assumptions instead of understanding.\n\nAt Boldare, we work with AI from the perspective of system ownership, not tool excitement. We use [**Claude** **Code**](https://www.boldare.com/services/claude-code-experts) where deep understanding, architectural reasoning, and legacy analysis are required, and we design processes around it that keep humans firmly in control of decisions. \n\n**Because at enterprise scale, the goal is not to write more code. It is to understand your system well enough to change it safely.**\n\n## FAQ\n\n**1. Is GitHub Copilot or Claude Code better for enterprise backend systems?**\n\nNeither tool is universally better. GitHub Copilot succeeds at accelerating well defined, low risk work like boilerplate, tests, and pull request changes, especially in compliance heavy environments. Claude Code is more effective when teams need to understand complex systems, legacy code, or architectural dependencies. In most enterprise setups, they complement each other rather than compete.\n\n**2. Can Claude Code replace GitHub Copilot in a Java team?**\n\nNot realistically. Claude Code is not designed to replace day to day coding acceleration inside the IDE. Its strength lies in system level reasoning, refactoring support, and legacy analysis. Java teams that try to use it as a Copilot replacement usually miss out on its real value and create unnecessary friction.\n\n**3. Why do AI tools often fail in legacy Java systems?**\n\nAI tools fail when they are applied without accounting for historical context, custom patterns, and undocumented decisions. Legacy Java systems rely heavily on relationships between components rather than isolated code snippets. Tools that focus only on local context tend to reinforce anti patterns instead of helping teams understand and evolve the system safely.\n\n**4. Is Claude Code safe to use in enterprise environments?**\n\nClaude Code can be used safely in enterprise environments, but it requires more intentional process design than GitHub Copilot. Governance, access rules, and verification practices need to be defined at the organizational level rather than relying solely on built in policy controls. The risk is not the tool itself, but over trusting its outputs without validation.\n\n**5. Should enterprise teams standardize on a single AI coding tool?**\n\nIn most cases, no. Forcing one tool to handle all types of work usually reduces overall effectiveness. Enterprise teams get better results by matching tools to specific tasks, using Copilot for execution speed and governance friendly workflows, and Claude Code for deep understanding, refactoring, and architectural reasoning."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1769517178/claude_code_vs_copilot_y7ldsq.png","lead":"If you work with an **enterprise** **backend**,  you probably are caught up in the constant war between AI tools. Every month there’s a new one promising enhanced productivity, and somehow you’re expected to standardize on a solution that will work for everyone. This can get especially difficult when you’re operating in systems shaped by years of trade offs, **legacy** **code**, and context that no generic AI tool actually understands.\n\n[GitHub Copilot](https://github.com/features/copilot) and [Claude Code](https://claude.com/product/claude-code) are often compared as if they **solved the same problem**, which they don’t. They are usually used side by side, but for very different kinds of work and at very different points in the delivery process. In this article, we break down what actually separates them and why that distinction matters for enterprise backend teams.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-01-27T12:43:27.351Z","slug":"claude-code-vs-copilot-choosing-the-right-tool","type":"blog","slugType":null,"category":null,"additionalCategories":["GenAI","Tech"],"url":null},"author":"Aleksander Dąbrowski","authorAdditional":null,"box":{"content":{"title":"Claude Code vs GitHub Copilot: Choosing the right tool for enterprise backend systems","tileDescription":"Discover the key differences between Claude Code and GitHub Copilot for enterprise backend systems. Learn which AI tool fits your team's needs best.","coverImage":null},"coverImage":null}},"id":"9aefc206-eaf1-5586-9431-41fbb2fd42cc"}},{"node":{"excerpt":"","fields":{"slug":"/blog/introducing-ai-in-mature-java-systems-a-layered-approach-for-scaling-engineering-practises/"},"frontmatter":{"title":"Introducing AI in mature Java systems – a layered approach for scaling engineering practises","order":null,"content":[{"body":"## A layered way to think about AI\n\nIn mature Java systems, AI adoption is not a clear-cut process. The introduction scale depends on the team’s needs and readiness – this works best when implemented in layers, because not every area carries the same risk or cost of change.\n\nThe three levels below explain how deeply AI interacts with existing engineering practices and how much responsibility it takes on within the system. Each step increases the impact but comes with bigger responsibility. You don’t need to climb up to the third level to benefit – for many cases, reaching the first level makes a significant difference alone. The point of the tiers below is to help you choose where to start and what fits your situation most, not to push you toward the deepest integration.\n\n## Level 1: AI as the engineer’s assistant\n\n**Best for teams that:**\n\n* work with a mature Java codebase and want faster delivery without touching production\n* struggle with onboarding and understanding legacy modules\n* lose time on repetitive scaffolding and test updates\n\nOn this level, AI exists entirely in the developer’s workflow – nothing enters or runs in the production directly. AI can be embedded in the IDE and take care of repetitive Java scaffolding and suggest test updates, so developers can focus on business logic.\n\nIn practice, this supports **everyday** **coding** and **refactoring** **tasks** common in **Spring** **based** systems, where even small domain changes require touching multiple layers of the application. A single change often spreads through controllers, data objects, mappings, validations, and tests. AI can generate this repetitive structure (for example, when extending a **JPA** **entity**) in a way that matches how the project is already organized, giving developers insights that would otherwise be discovered only after a wave of errors or failed tests\n\nAI can also help with **legacy** **code** **comprehension** since older Java modules often lack up-to-date documentation. In such a case, AI can summarize class responsibilities, explain method flows, and **generate** **JavaDoc** based on the findings. This is especially valuable during revisiting rarely changed parts of the system or navigating **large Spring monoliths**, where proper onboarding matters more than writing new parts of code.\n\nBecause AI operates entirely within the developer workflow, teams can experiment freely without risking production stability, while gaining speed and confidence.\n\n## Level 2: AI inside the quality and review pipeline\n\n**Best for teams that:**\n\n* handle a high volume of pull requests and long review cycles\n* experience regressions despite strong engineering practices\n* want more predictable quality without slowing down delivery\n\nThe second level is where AI starts **influencing** **what** **actually** **gets** **deployed**, not by writing production code directly, but by assisting with **quality related decisions**. This is the stage where mature Java systems typically slow down the most due to the number of reviews, regression analysis, and test maintenance.\n\nIn long-lived Java systems, pull requests tend to grow larger over time and even a small feature can touch many interconnected areas. Reviews become longer and more tiring, shifting the focus to spotting risks instead of design improvements. AI can support review practices by highlighting changes in historically fragile packages, shared libraries, or modules with a high regression rate, acting like a **second** **pair** **of eyes** relieving the developer from the mundanity.\n\nAt this level, AI can also assist with **contextual** **code** **review**, detecting potential bugs or security issues resulting from the system evolution. Unlike static analysis, AI can **correlate** changes with previous incidents, recurring regressions, or known weak spots in the codebase.\n\nWith changing APIs or domain logic, tests often require manual updates that stall the development. In this area, AI can suggest updates to existing tests or generate missing cases based on what changed. This directly addresses test maintenance debt, which in mature Java systems often slows teams down more than feature development itself.\n\nBy reducing mechanical review and test costs, AI allows engineers to focus on design decisions, edge cases, and business impact.\n\n## **Level 3: AI as a controlled part of the Java system**\n\n**Best for teams that:**\n\n* want to start using AI in production to support real product or operational use cases\n* are ready to validate AI through controlled experiments, observability, and clear fallback paths\n* operate a mature Java platform where changes must be introduced carefully, not experimentally\n\nOn the third level, AI becomes a **part** **of** **the** **product** – this is also the phase where caution and discipline matter the most. Embedding AI directly into core business logic introduces risks that are hard to predict.\n\nHowever, this doesn’t mean that AI should never run in production, it means that it should be **isolated**:\n\nThe safest and most scalable way to approach such deep integration is to treat AI as an external component or adapter. Instead of embedding it into the domain core, AI is accessed through clearly defined safe boundaries like a separate module, a service, or an adapter layer integrated via frameworks such as **[Spring AI](https://spring.io/projects/spring-ai)** or **SDKs** like **AWS**.\n\nSuch a setup allows teams to **control** **failure** **modes** – AI responses can be validated, monitored, and turned off without rolling back the entire system. Caution is key here, as you need to detect when AI behaves unexpectedly, how often it fails, and what impact it can possibly have on users or downstream processes.\n\nThe key challenge on this level is understanding what should happen when AI is wrong, and designing the system accordingly. This is often where teams benefit most from structured guidance rather than figuring it out through trial and error.\n\n## What are the real risks of using AI in mature Java systems?\n\nAny discussion about implementing AI wouldn’t be complete without acknowledging the **risks** behind it. Rest assured, it’s not like AI is the hidden danger here, it’s the adoption without proper boundaries.\n\nThe most obvious risk in the process is the **data** **exposure** – since mature systems contain sensitive information, they cannot be a part of the flow with the external model (e.g business logic or customer data). \n\nAnother ones are **hallucination** and **false** **confidence** – the generated code may look correct and fit existing patterns but still introduce subtle bugs that can build up over time. In systems with complex domain logic like Java, these mistakes are rarely spottable at first glance and resurface under production load, resulting in high maintenance costs.\n\n**Example**: While updating a **Spring** **based** **REST** endpoint backed by **JPA** **entities**, AI may suggest simplifying a validation rule or mapper. The change compiles and passes tests, but **removes** a domain constraint added years earlier to handle a specific edge case, leading to subtle data inconsistencies under real production traffic.\n\nThe risks discussed above are also the reason **senior** **developers** tend to be skeptical **about** AI. If you want to understand that skepticism and learn how to adopt tools like Claude Code without breaking trust or quality, the article below is a good next step.\n\n\n\n## How to think about AI integration\n\nThe most important thing to remember is that AI adoption in Java systems is not about replacing what already works but about **removing** **friction** where it affects the day to day work the most. A tiered approach helps teams start with low risk improvements, learn how AI behaves in their context, and increase scope when the organization is ready for it.\n\nIt’s worth reiterating that deeper integration increases both impact and responsibility as mistakes become harder to isolate and reverse. When there is uncertainty about where to stop or how far to go, pushing forward blindly often creates more risk than value.\n\nFor CTOs and engineering leaders, the hardest part of AI adoption is rarely choosing tools but deciding where AI fits into existing engineering practices without increasing delivery or operational hazards.\n\nThat’s exactly what our **[Claude Code Experts](https://events.zoom.us/ev/ApvY7AJlYKSHiNxHg1tihnC3Ot-XRpG0mXeOqbJWR3mlES4YbVqY~AjtKSN5k7D0q0ReCWYbQaFrYcHPPpuOdVcBYLS50592Enm-YYOnSJQXCozmkjGFYgGF4OZq6QieuElwCupO7V1ostQ)** focus on – helping Java teams adopt AI in a way that respects legacy systems, senior expertise, and real production constraints.\n\nAnd if you want to go deeper into why AI often fails in Java teams, and what actually works – join our upcoming live session: **[Claude Code Experts: Why does AI fail in Java teams?](https://events.zoom.us/ev/ApvY7AJlYKSHiNxHg1tihnC3Ot-XRpG0mXeOqbJWR3mlES4YbVqY~AjtKSN5k7D0q0ReCWYbQaFrYcHPPpuOdVcBYLS50592Enm-YYOnSJQXCozmkjGFYgGF4OZq6QieuElwCupO7V1ostQ)**\n\n## FAQ\n\n**1. What does “introducing AI in a layered way” mean for Java systems?**\n\nA layered approach means adopting AI gradually, based on risk and system impact. Instead of integrating AI directly into production logic from the start, teams begin with low risk use cases such as developer assistance, then move toward quality assurance and review automation, and only later consider controlled production use. This approach allows teams to gain value early while minimizing architectural and operational risk.\n\n**2. Can AI be used in mature Java systems without affecting production stability?**\n\nYes. At the first level of adoption, AI operates entirely within the developer workflow, for example inside the IDE. In this setup, AI supports tasks like code scaffolding, refactoring, test updates, and codebase comprehension. Because no AI generated output is executed in production, this level improves delivery speed without introducing runtime risk.\n\n**3. How does AI improve code quality and reviews in long lived Java codebases?**\n\nAI can assist code reviews and testing by analyzing changes in context, rather than relying only on static rules. It can highlight modifications in historically fragile modules, suggest test updates when APIs or domain logic change, and surface potential regression risks based on past incidents. This helps reduce review fatigue and makes quality assurance more predictable in complex Java systems.\n\n**4. Is it safe to use AI generated code in enterprise Java environments?**\n\nAI generated code should be treated as a suggestion, not as an authoritative source. While AI is effective at following existing patterns, it may miss domain specific constraints or historical edge cases. Safe usage requires human review, clear boundaries, and an understanding of which parts of the system are suitable for AI assistance. This is especially important in systems with complex business logic and long operational history.\n\n**5. When does it make sense to use AI directly in production systems?**\n\nAI can be used in production when it is introduced as a controlled and isolated component rather than embedded into the domain core. Common approaches include using AI behind a service boundary, adapter, or external module with validation, monitoring, and fallback mechanisms. Teams should only proceed to this level when they can clearly define failure scenarios and limit the impact of incorrect AI behavior."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1769433176/Blog_post_sohucv.png","lead":"Teams responsible for long-lived Java systems are usually under constant pressure – product teams push for faster delivery, while the engineers fight to keep stability. At the same time, investors expect velocity to increase linearly, even though every additional change affects more dependencies than it used to. This pressure is not a sign of failure, but an outcome of a Java system that accumulated domain knowledge, dependencies, and delivery expectations over time.\n\nThis is where AI becomes interesting for scaleup companies. Not as a way to miraculously rebuild the system but to reduce the cognitive load that oftentimes builds up in it. What slows teams down is rarely a lack of tools, but the growing effort required to think about impact, dependencies, and side effects before a single line of code is changed.\n\nIf you’re under the pressure of scaling delivery on a mature Java platform, this article offers a breakdown how AI can be introduced into real engineering practices.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-01-26T12:26:33.444Z","slug":"introducing-ai-in-mature-java-systems-layered-approach","type":"blog","slugType":null,"category":null,"additionalCategories":["GenAI","Strategy","Tech"],"url":null},"author":"Aleksander Dąbrowski","authorAdditional":"","box":{"content":{"title":"Introducing AI in mature Java systems – a layered approach for scaling engineering practises","tileDescription":"Discover how to introduce AI into mature Java systems to reduce cognitive load, manage dependencies, and scale engineering practices without risking stability.","coverImage":""},"coverImage":null}},"id":"564bec34-814f-518e-8f45-54f03655ed23"}},{"node":{"excerpt":"","fields":{"slug":"/blog/the-10xdevs-certificate-strengthens-boldare-s-team/"},"frontmatter":{"title":"The 10xDevs certificate strengthens Boldare’s team","order":null,"content":[{"body":"## What’s the certification about\n\nThree areas from the program stand out as especially important for the products we build at Boldare:\n\n* using AI as support for **architectural planning** and **technical decision-making** **–** the 10xDevs program emphasizes using AI to reason about system design, trade-offs, and constraints. For products, where early architectural choices tend to degrade, support of this kind helps teams make clearer decisions.\n* **creating strong context for AI to work with** **–** learning how to translate product requirements, technical assumptions, and business constraints into clear, shared context reduces noise and increases reliability.\n* **evaluating AI models based on project and business needs** **–** the certification puts strong focus on choosing models with intention, balancing quality, cost, performance, and risk. This matters in real delivery setups, where AI decisions have a direct impact on infrastructure costs, security posture, and long-term sustainability\n\nIf you are curious what happens when AI is introduced without this level of structure, we explore that perspective in more detail in the article below, where we break down how responsible AI adoption actually looks like.\\\n\\\n\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1768912113/certificate_x2izo9.png)\n\n## **Why we are excited about it**\n\nThis certification raises the bar for engineers at Boldare, sharpening architectural understanding, speeds up onboarding to complex systems, and encourages more thoughtful use of AI-automation. \n\nIt's another step that helps us stay a reliable partner as products grow and systems become more complex."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1768912683/blog_vmtod6.png","lead":"We are excited to share that one for our Tech Leads earned the **[10xDevs certification](https://www.10xdevs.pl/)**. \n\nIt’s a program built for experienced engineers who already operate in complex environments and want to use AI in a way that actually brings value in products. For us, it’s not only about the title itself but what’s behind it **–** a deep, hands-on understanding on how to use AI responsibly inside real software projects.\n\nRead this article to understand why this accomplishment genuinely matters to us and our partners.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-01-20T12:24:45.174Z","slug":"boldare-tech-lead-achieves-10xdevs-certification-boosting-team-expertise","type":"blog","slugType":null,"category":null,"additionalCategories":["GenAI","People"],"url":null},"author":"Aleksander Dąbrowski","authorAdditional":"","box":{"content":{"title":"The 10xDevs certificate strengthens Boldare’s team","tileDescription":"Discover how Boldare’s Tech Lead earned the 10xDevs certification, enhancing our team’s expertise in leveraging AI to drive value in software projects.","coverImage":""},"coverImage":null}},"id":"85dd00cc-f971-515f-858a-d70a6b6a5e43"}},{"node":{"excerpt":"","fields":{"slug":"/blog/why-your-devs-say-ai-is-useless-an-expert-take-on-adopting-claude-code-in-senior-software-teams/"},"frontmatter":{"title":"Why your devs say “AI is useless” – an expert take on adopting Claude Code in senior software teams","order":null,"content":[{"body":"## Where AI adoption starts to break down\n\nIn many cases, AI enters the organization as a regular tool rather than a change in how the work is done. Licenses are purchased and teams are simply encouraged to experiment and find the best course of action. All this under the assumption that they’ll work out productivity and fluency naturally - the same way they might with a new library or framework.\n\nWhat actually happens is far messier:\n\nDevelopers test AI tools in isolation, often without shared expectations or guidance. Some find limited value, others face incorrect or shallow suggestions, and a few go deeper by building their own workflows or experimenting with alternative tools. Over time, usage becomes fragmented and the organization struggles to form a solid conclusion **whether the implemented AI tool is helping at all.**\n\nFrom leadership’s point of view, this may seem confusing but from the developer's point of view, it feels like AI was dropped into a system that was never adapted to support it in the first place.\n\n## Why senior teams lose faith in AI\n\nFor example, in large backend systems, generic AI suggestions often feel shallow and out of touch, especially when they contradict architectural constraints or domain rules. So, it’s not like highly experienced developers are “anti-AI” – they just hate nonsense, and they are the quickest to spot when a tool generates more cognitive load than value.  \n\nThis is why AI enthusiasm often drops in mature teams. The issue is not dislike, but simply the standards – if AI outputs consistently fail to meet the benchmarks required in production environments, it is reasonable for teams to reject it.\n\n## Risks behind misguided AI adoption \n\nIntroducing Claude Code or other AI-augmentation tools requires a clear framework in order for the implementation to be successful. Without a specific plan, the adoption may cause some very serious issues to arise:\n\n* **Architecture erosion** – in large, long-lived systems, architecture’s consistency is non-negotiable. Careless use of AI (without proper **context building)** can lead to generating and implementing patterns that may appear correct at first, but be in violation with design decisions, leading to slow degradation of the code and finally higher maintenance costs;\n* **Degraded code reviews** – AI-generated code often looks genuine and valid, even when it’s actually incomplete. If teams are not properly trained to evaluate AI outputs critically, review quality and deep understanding of the code drops;\n* **Data leaks and compliance bypasses** – some code fragments shouldn’t be shared with external models. Without determining ground security rules, developers may unintentionally expose sensitive data.\n\nWhile AI implementation seems like an obvious and carefree thing to do, the risks mentioned above are no-joke. If there is uncertainty about how to start, you might want to consider the way you want to approach it.\n\n## Two paths to AI adoption\n\nFor organizations operating at scale, there are two paths to choose from when implementing AI:\n\nThe first is to continue letting teams experiment on their own. This approach highly enforces autonomy and allows learning through trial and error but comes with a major drawback – high **uncertainty**. As described earlier, this often leads to fragmented use, inconsistent outcomes and other serious risks that could potentially harm the entire infrastructure.\n\nThe second path is to look at AI integration as change on the system-level, not as a new tool to implement. This includes precisely identifying where AI can create value, where it shouldn't be used at all and how teams are expected to evaluate outputs. This approach makes AI embedded directly into SDLC process and architecture’s constraints.\n\nWhile many companies try to experiment with tools like Claude Code, many of them don’t understand how to integrate them safely into the production environment.  In such a scenario, delegating this change to experienced specialists is often a more responsible choice, as costs of architectural mistakes or security incidents are simply too high.\n\n## What responsible AI adoption actually looks like\n\n**Reality check** is key to introducing AI rationally – that is, identifying where AI can create value right now, without the risk. This includes focusing on repetitive activities like **[test case generation](https://www.boldare.com/blog/this-weeks-ai-bite-how-to-generate-test-cases-based-on-jira-tickets-guide-by-sylwia-rapacz/)**, code reviews, documentation drafts or backlog analysis. In practice, this often means defining which parts of the codebase are open to AI-assisted changes and which should remain human-owned, such as core domain logic or security-critical components.\n\nNext thing is determining what AI should be allowed to do, how the outputs are reviewed and where human judgement is mandatory. This minimizes the chaos of experimentation by individuals and allows predictable and secure usage.\n\nOnce the foundations and ground rules are taken care of, teams can move towards more advanced practices such as **agentic coding**.\n\nIn **spec-driven development**, AI can support early **problem decomposition** by helping teams turn requirements into structured specifications and identify edge cases before any code is even written. Above that, it can help with designing workflows that allow for controlled **self-improving iteration** – instead of prompting one-off, AI goes through structured feedback loops where outputs are evaluated and improved over multiple cycles.\n\nWhen approached this way, AI stops being unpredictable and becomes part of the engineering system itself. Teams gain a controlled way to benefit from the tool without compromising security and architecture while keeping the engineering standards.\n\n## Making AI work before it becomes a problem\n\nBeing **AI-native** does not mean automating everything, it’s the ability to reasonably implement AI into development systems in ways that respect architecture and process. Done this way, AI becomes a **reliable** **support** for experienced teams, instead of a source of frustration.\n\nOrganizations that approach the adoption with this mindset tend to move faster in the long perspective. If AI already feels like a problem in your organization, it is often a signal that the adoption model needs rethinking, not that the technology itself has failed.\n\n**Thinking about AI adoption in your system? Let’s make it work with our Claude Code experts before it becomes a problem.**"}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1768898959/Blog_post_uv8hlm.png","lead":"After hundreds of collaborations with mature software teams operating at scale we noticed a disturbing trend. When they first approach us, we keep hearing the same things about their attempts to implement AI: *“the output is low quality,”* *“the context is missing,”* and *“the tools do not fit real systems”.* Sometimes the conclusion is brutal: **AI is useless**.\n\nIf you are responsible for making AI adoption work, read this article to understand why AI reluctance is oftentimes valid, what usually goes wrong when AI enters mature teams, and how organizations can move toward meaningful **[Claude Code](https://claude.com/product/claude-code)** integration without trial and error.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-01-20T08:45:18.644Z","slug":"why-devs-say-ai-is-useless-claude-code-adoption-senior-software-teams","type":"blog","slugType":null,"category":null,"additionalCategories":["GenAI","How to","Tech"],"url":null},"author":"Aleksander Dąbrowski","authorAdditional":"","box":{"content":{"title":"Why your devs say “AI is useless” – an expert take on adopting Claude Code in senior software teams","tileDescription":"Learn why senior developers say AI is useless, what goes wrong in real teams, and how to successfully adopt Claude Code without costly trial and error.","coverImage":""},"coverImage":null}},"id":"46c4fe2a-69ae-521f-afa0-7e0eb0ca8df0"}},{"node":{"excerpt":"","fields":{"slug":"/blog/services-over-saas-why-a-partnership-first-model-works-better-for-growing-products/"},"frontmatter":{"title":"Services over SaaS – why a partnership-first model works better for growing products","order":null,"content":[{"body":"## The cost of betting everything on a single product\n\n> ***“SaaS is a dream everybody has - but to be honest.. I’ve never had”*** \n>\n> \\- as Boldare’s co-CEO, Anna Zarudzka, recently opened up on the **[Messy Growth](https://www.youtube.com/watch?v=mhufkm1PCQY)** podcast. \n\nSaaS is undeniably a powerful business model, as it allows the company to scale rapidly and secure strong and predictable revenue. But there’s one major drawback – it forces the organization to commit to one market, product and direction. When it thrives, it pays off big, but if not, the whole company pays the price. \n\nA service-based model distributes the risk differently – instead of focusing on one idea, we’ve helped shape hundreds of products at different stages of maturity, within various industries and technologies. This exposure gave us a broader understanding of how businesses behave under pressure and what challenges repeat themselves across the contexts. \n\n## A bridge to real life problems\n\nServices allow us to keep the teams close to reality, in comparison to SaaS, where teams focus on solving one problem or refining one product in one environment. In services, context changes constantly.\n\nA good example of this was our cooperation with **[The Elephant’s Trunk](https://www.boldare.com/work/elephants-trunk-unusual-ecommerce-mvp/)** – an Irish startup building an unconventional e-commerce platform. Their goal wasn’t just to sell children’s books, but to validate a business idea focused on inclusion, diversity and personalization. The challenge was clear from the start: test whether parents would actually buy personalized books with their children as main characters.\n\nInstead of immediate scaling, we focused on a MVP to validate the demand under real market conditions. Time pressure and budget constraints accompanied our every decision, forcing prioritization. It was a pure learning experience grounded in reality.\n\n## Paid and faster learning\n\nOne of the biggest advantages of a services model is how efficiently it sharpens the judgement – working on real products for real users forces decisions to be made under real-life conditions.\n\nEach collaboration becomes a chance for the teams to take part in a condensed learning loop. They observe how users behave, how organizations make decisions, where processes break and which trade-offs actually make a difference. These lessons result in better time management, communication and a stronger sense of what is worth building now vs. later.\n\nThis was especially visible in our collaboration with **[POLCO](https://www.boldare.com/work/case-study-polco/)**, a US-based civic engagement platform designed to bring clear information and data to public politics discussions. Instead of committing to a fully formed product idea, we iterated in cycles, releasing early versions to the users and watching how behavior evolved over time which resulted in invalidation of some assumptions. \n\nThis kind of hands-on learning makes teams work faster, not because of rush, but less time-waste. For partners, this means less dead ends and a much clearer path with earlier validations.\n\n## More than just delivery\n\nThere’s a persistent stereotype that service companies' main goal is “body leasing” – renting out people and walking away once the sprint is done. That’s a model we intentionally rejected. \n\nFor us, services mean building cross-functional teams that **share** responsibility with the client, stay invested in outcomes, and think in terms of systems, not tickets. \n\nThat’s why we choose to work with limited numbers of clients at a time – we want to stay close to the product, understand its context and contribute beyond main deliverables. \n\nIn both **the** **Elephant’s Trunk** and **POLCO** partnerships (and many others you can explore in our **[case studies](https://www.boldare.com/work/)**), this approach meant challenging the assumptions, redefining priorities and sometimes saying “stop” when we realized that a different approach was needed. \n\nIt also meant helping partners build independence and grow internal capabilities so they could take ownership over time. Partnership, in our view, is not about the dependency and locking-in the product but about leaving teams stronger than we found them.\n\n## 20 years worth of lessons\n\nWorking in a services-first model taught us to focus less on assumptions and trends and more on what reliably works across different products and stages of development. We started recognizing patterns earlier, especially with shifting priorities or increasing complexity, which is usually the moment when good partnership matters most.\n\nFor our partners, this translates into working with a team that understands not only how to deliver services well, but also where the limits are. \n\nThat’s why many long-term partnerships move toward the **[Service-as-a-Software](https://www.boldare.com/blog/service-as-a-software-an-executive-guide/)** model – an approach that’s becoming increasingly popular as companies look for ways to to scale without increasing headcount. With the current AI revolution, it’s now possible to utilize proven service processes and decision logic into systems to deliver consistent outcomes. Instead of growing by headcount, companies can scale outcomes, quality and consistency. Such a shift works best when it’s based on years of real delivery. \n\n**Twenty years of services teach you what no single product ever could.**"}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1768828131/Blog_post_secizt.png","lead":"In the tech world, services are often treated as a stepping stone, a temporary setup on the way to building “the real thing”, usually understood as a SaaS product. No wonder why – success stories of **[Slack](https://slack.com/)**, **[HubSpot](https://www.hubspot.com/)** or **[Atlassian](https://www.atlassian.com/)** proved how effective this model can be. At Boldare, we’ve never seen it this way. Not because we think SaaS is wrong, but because it’s not the only way to build a strong and resilient company.\n\nFor over **2 decades** on the **global market**, we’ve worked alongside hundreds of companies, rebuilding and scaling digital products, while learning deeply from real challenges and constraints. Over that time, we delivered **300+ digital products** and formed **long-term partnerships** that shaped how we think about value, risk and responsibility.\n\nRead this article to learn what this experience taught us about what it really means to be a partner, not just a vendor executing a backlog.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-01-19T13:45:47.709Z","slug":"why-a-partnership-first-model-works-better-for-growing-products","type":"blog","slugType":null,"category":null,"additionalCategories":["Future","Strategy"],"url":null},"author":"Aleksander Dąbrowski","authorAdditional":"","box":{"content":{"title":"Services Over SaaS - A Partnership-First Growth Model","tileDescription":"Services are not a stepping stone to SaaS. Learn why a partnership-first model builds stronger, more resilient digital products.","coverImage":""},"coverImage":null}},"id":"b4ae5c1c-b7db-55b2-8b1e-23fab65a2a26"}},{"node":{"excerpt":"","fields":{"slug":"/blog/you-are-a-beginner-again-and-again-the-mindset-behind-building-and-scaling-a-10m-ai-driven-digital-company/"},"frontmatter":{"title":"“You are a beginner again and again” – The mindset behind building and scaling a $10M+ AI-driven digital company","order":null,"content":[{"body":"## From craftsmanship to product thinking\n\nBefore Boldare, Anna worked in TV production during the industry’s quiet crisis – budgets were shrinking, formats were getting cheaper, and “good enough” was replacing the thing she valued most – true **craftsmanship**. For someone believing that quality was non-negotiable, this shift created an increasing discomfort.\n\nInstead of resisting the industry’s logic, Anna chose a different path. In 2010 she founded **Chilid** – a design-driven company focused on rapid product development and front-end. Around that time, **XSolve** founded by **Piotr Majchrzak**, was building agile software teams designed for long-term collaboration and support for businesses. For years the companies functioned in synergy and collaborated together on various projects, addressing different client needs and supporting each other, but as the market changed, and clients were looking for more comprehensive services, the separation became less and less necessary.\n\n**[Boldare was created as a natural response](https://www.boldare.com/blog/the-story-of-boldare/)** to that shift, taking care of the entire process, from design, through software development to support and advisory.\n\n## No status, no hierarchy\n\nEach career switch pushed Anna back to the beginner’s position with new rules, skills and people, and zero certainty the next step would work. Each reset also meant losing status or authority.\n\n> **“People think about power. I think about coexistence.”** \n>\n> *said Anna Zarudzka*\n\nThis point of view directly echoes in how Boldare operates today:\n\nInstead of traditional hierarchy, the company works under a flat structure based on distributed authority through **[holacracy](https://www.boldare.com/blog/holacracy-in-nutshell/)**. Teams self-organize around real problems, and change is considered the natural environment rather than an obstacle.  \n\nFor clients, this means working with a **flexible** and **responsive** partner without slowing the product down due to rigid decision-making processes.\n\n## Intentional growth\n\nThroughout 20 years, Boldare delivered **hundreds** of digital products for **300+ clients** – each with its own unique nature and features, tailored to the user’s needs.\n\nAt one point, the company grew rapidly and scaled its teams and delivery in response to increasing expectations from the international clients. This made some challenges emerge – leadership became less clear, the quality was harder to protect and the organization got distracted from its core, craftsmanship values. \n\nInstead of pushing the growth at all costs, the company consciously decided to slow down and focused on maintaining quality and standards the founders always cared for the most. Growth in this case wasn’t about the size but the maturity and staying true to the vision the company was initially built on.\n\n## Uncertainty as the operating environment\n\n> **“Jumping into something new, surviving chaos many times… that’s basically running a company.”**\n>\n> *– Anna Zarudzka*\n\nAfter many years of starting over in new roles and industries, Anna learned that uncertainty isn’t just a phase on the way to stability, it’s the environment where successful companies learn to work in every day. Markets change, tools come and go, plans fail and what worked yesterday might not be valid today.\n\nBoldare doesn’t fight this reality, it accepts and embraces chaos as the constant and natural state of things, using the lessons learned from uneasy experiences to improve how the work’s done. \n\nHowever, accepting chaos doesn’t mean thinking blindly, it’s about constantly looking for ways to learn and operate faster.\n\n## AI-native by practice\n\nBoldare didn’t start using AI because it was a trend. The company treated it as every new tool – as **a way to solve real problems and increase the value for clients**. As soon as AI became practically usable, it was implemented across the entire product process: from discovery to design, development and delivery. This fundamentally changed how Boldare’s cross-functional teams operate on a daily basis:\n\n* developers use AI-assisted tools like GitHub Copilot, CursorAI to **speed up** prototyping, **reduce** repetitive tasks, and **focus** on core business needs;\n* designers use AI-powered flows to **test** and validate ideas more **quickly**, then turn those ideas into **production-ready** implementations;\n* product teams use AI to **explore** hypotheses earlier, **sharpen** problem understanding, and **test** assumptions before investments.\n\nThis is what **[AI-augmented development](https://www.boldare.com/blog/ai-augmented-development-at-boldare/)** looks like in practice. Teams recognize when AI adds value and when it doesn’t and wisely apply it when needed. The outputs are always reviewed, questioned, and validated by experienced practitioners.\n\nWhat’s more, beyond internal use, the company also supports clients through **[AI consulting](https://www.boldare.com/services/ai-software-development-consulting/)**, helping them identify where AI makes sense for their business whether in automation, personalization or improving user experience. The focus is always on **clear goals** and **measurable outcomes**, not experiments for the sake of it.\n\nAdditionally, through initiatives like the **[Around the Product Development TECH](https://www.youtube.com/playlist?list=PLdvko3YEuQr8VwER1UTvCpi-GBfoAt3fn)** podcast, Boldare creates space for conversations about building products, using AI responsibly, and making better decisions for your business.\n\nBy treating AI as a natural extension of team capabilities, Boldare integrates innovation directly into how products are built and improved, keeping the primary focus on real business value.\n\n## A partner built for change\n\nBuilding digital products today means constantly facing uncertainty. Decisions are often made having only partial information and priorities change mid-process. \n\nThis is exactly the environment Boldare was built to thrive in – it doesn’t rely on fixed solutions but on staying open, flexible and adaptable. For clients, this means lower risk, better decisions and future-proof products that evolve together with their business. \n\n**That’s why Boldare is not a partner for perfect conditions, it’s a partner for real ones.** \n\n**If you want to hear more about this mindset straight from the source, listen to the full conversation on the Messy Growth podcast:**\n\n"}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1768384060/Blog_post_hiwcuw.png","lead":"She studied jazz, painted, and produced TV – and now co-runs a **$10M+** digital company operating for nearly **20 years** on the global market. **Anna Zarudzka**, Boldare’s co-CEO, recently appeared on the **[Messy Growth](https://www.youtube.com/@messygrowthhustlex)** podcast, where she reflected on the career she shaped by constant resets, rather than linear growth.\n\nRead this article to explore how that mindset became the foundation of **Boldare’s DNA** – a partner trusted by international clients like **[BlaBlaCar](https://www.boldare.com/work/case-story-blablacar/)** or **BOSCH** to build and scale products.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-01-14T11:28:18.174Z","slug":"the-mindset-behind-building-and-scaling-a-10m-ai-driven-digital-company","type":"blog","slugType":null,"category":null,"additionalCategories":["Tech","Ideas","Digital Product"],"url":null},"author":"Aleksander Dąbrowski","authorAdditional":"","box":{"content":{"title":"“You are a beginner again and again” – The mindset behind building and scaling a $10M+ AI-driven digital company","tileDescription":"How a beginner mindset helped Boldare scale to $10M+. Anna Zarudzka on constant resets, non-linear growth, and building AI-driven products.","coverImage":"https://res.cloudinary.com/de4rvmslk/image/upload/v1768384060/Blog_post_hiwcuw.png"},"coverImage":null}},"id":"25d3f966-9de8-5ce2-bd68-ce9b2190effb"}},{"node":{"excerpt":"","fields":{"slug":"/blog/is-cursor-ide-safe-for-enterprise/"},"frontmatter":{"title":"Is Cursor IDE safe and suitable for enterprise development teams?","order":null,"content":[{"body":"## 1. Does Cursor send source code outside the organization?\n\n**Short answer:** Yes, selected code context is transmitted to external AI services.\n\nWhen developers interact with [Cursor's AI features](https://www.boldare.com/blog/what-is-cursor/), the IDE sends code fragments, file context, and sometimes project-level structure to cloud-hosted LLMs for inference.\n\nFrom an enterprise perspective, this has three main implications. First, code may be processed outside company infrastructure, which affects data locality requirements. Second, AI model providers effectively become indirect subprocessors of company data. Third, formal approval from IT and security teams is usually required before allowing such tools in production environments.\n\nCursor does not operate fully offline for AI features. Therefore, teams working with highly sensitive or regulated codebases must evaluate whether external processing is acceptable under internal security policies.\n\n## 2. How does Cursor affect intellectual property (IP) and confidentiality?\n\n**Short answer:** IP protection depends on vendor terms and enterprise agreements, not only on technical controls.\n\nFor enterprise adoption, three questions are critical. First, whether submitted code is stored or logged by the provider. Second, whether the code is used to train future models. Third, who owns the generated output.\n\nIn most enterprise procurement processes, acceptable answers must be contractually guaranteed, not assumed. Without clear data-processing and IP clauses, legal teams may block usage for proprietary products.\n\nA common intermediate approach is allowing Cursor for internal tools, prototypes, or non-core systems before it is approved for core product development.\n\n## 3. Is Cursor compliant with enterprise security and regulatory frameworks?\n\n**Short answer:** Compliance must be verified per organization and industry.\n\nFor regulated industries such as finance, healthcare, or B2B SaaS handling customer data, typical checks include GDPR compliance and availability of data processing agreements, recognized security certifications such as SOC 2, transparency around subprocessors, and defined incident response procedures.\n\nEnterprise risk evaluations usually focus on whether personal data could appear in code, whether operational controls are audited, whether legal responsibility for data handling is clearly defined, and whether the vendor can support customer audits.\n\nWithout formal compliance validation, Cursor is often limited to experimental or sandbox environments rather than production systems.\n\n## 4. How does Cursor change engineering workflows and code quality?\n\n**Short answer:** Cursor increases speed but requires stricter review discipline.\n\nCursor can generate full functions and classes, perform multi-file refactors, and propose architectural changes. This shifts how teams work.\n\nOn the positive side, teams benefit from faster prototyping, reduced boilerplate coding, and quicker onboarding of new developers. On the risk side, developers may have only superficial understanding of generated code, architectural decisions may become inconsistent, and teams may become overly dependent on AI-generated logic.\n\nTo mitigate this, enterprises typically enforce mandatory code review regardless of AI usage, define architectural rules that AI-generated code must follow, and introduce internal guidelines for documenting or labeling AI-assisted changes. [Building applications with Cursor](https://www.boldare.com/blog/how-to-build-mobile-app-with-cursor-ide-no-code/) demonstrates both the efficiency gains and the importance of proper oversight.\n\nCursor does not remove engineering responsibility; it moves effort from writing code to validating and maintaining it.\n\n## 5. What does controlled enterprise adoption look like?\n\n**Short answer:** Successful adoption is staged and policy-driven.\n\nA typical rollout starts with a limited pilot involving a small group of developers working on non-critical projects, where productivity and quality metrics are monitored.\n\nThe second phase includes formal security and legal validation, vendor risk assessment, and approval from IT and compliance teams.\n\nThe final phase focuses on standardization, defining which project types are allowed, publishing internal usage guidelines, and training developers and technical leads.\n\nThis approach reduces operational and compliance risks while still allowing organizations to evaluate real productivity gains.\n\n## 6. Does Cursor provide measurable ROI for enterprises?\n\n**Short answer:** ROI is highest in early-stage development and maintenance-heavy codebases.\n\nCursor tends to improve productivity in feature scaffolding, refactoring legacy code, generating tests, and updating documentation. However, these gains may be partially offset by additional review time, governance overhead, and longer approval cycles.\n\nEnterprise ROI is strongest when teams work on frequently changing codebases, when technical debt is high, and when onboarding new developers is a significant cost factor. For highly standardized or safety-critical systems, ROI is often lower because strict validation reduces the speed benefits of AI-assisted coding.\n\nWith new capabilities like [Cursor Agents](https://cursor.com/agents), which enable more autonomous code generation and refactoring, the productivity potential increases further, but so does the need for robust validation processes.\n\n## Conclusion\n\nCursor IDE can be used in enterprise development environments, but only under controlled conditions. It introduces external data processing, vendor dependency, and governance challenges that must be addressed through security review, legal agreements, and internal usage policies.\n\nFor CTOs and engineering leaders, the key decision is not whether Cursor improves individual developer speed, but whether the organization can safely integrate AI-assisted coding into its software delivery lifecycle without compromising compliance, IP protection, and long-term code quality.\n\n## FAQ\n\n**1. Can Cursor be used for projects containing customer data?**\n\nOnly if data processing terms and infrastructure compliance are formally approved by legal and security teams. Otherwise, it should be restricted to non-sensitive projects.\n\n**2. Does Cursor replace the need for senior engineers?**\n\nNo. It increases output speed but also increases the importance of architectural oversight, validation, and code review.\n\n**3. Is Cursor better suited for startups than enterprises?**\n\nStartups benefit faster due to lower compliance barriers. Enterprises can still benefit, but only with structured governance and phased adoption."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1756732905/Frame-2_lhpaon.png","lead":"Cursor IDE can be used by enterprise development teams, but only if security, compliance, and governance requirements are formally reviewed and approved. Cursor operates as a local IDE connected to cloud-based large language models (LLMs). To generate suggestions, it sends contextual code snippets to external AI providers. This creates potential risks related to source-code exposure, regulatory compliance, and intellectual property (IP). From an enterprise perspective, Cursor should be treated like any other cloud-based developer productivity tool and evaluated through standard vendor risk assessment, security review, and controlled rollout.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-01-13T10:01:42.994Z","slug":"is-cursor-ide-safe-for-enterprise","type":"blog","slugType":null,"category":null,"additionalCategories":["GenAI","Tech"],"url":null},"author":"Magdalena Chmiel","authorAdditional":null,"box":{"content":{"title":"Is Cursor IDE safe and suitable for enterprise development teams?","tileDescription":"Learn how Cursor IDE fits in enterprise environments with proper security review, compliance validation, and controlled rollout. Risks and requirements.","coverImage":null},"coverImage":null}},"id":"b2d77806-d106-57e7-aa1f-073135fe4815"}},{"node":{"excerpt":"","fields":{"slug":"/blog/this-week-s-ai-bite-how-to-generate-test-cases-based-on-jira-tickets-a-guide-by-sylwia-rapacz/"},"frontmatter":{"title":"This week’s AI Bite: How to generate test cases based on Jira tickets – A guide by Sylwia Rapacz","order":null,"content":[{"body":"## A step toward the future in QA\n\nGenerating test cases based on Jira tickets is a process that can take up a lot of time and is naturally prone to human errors. Traditionally, this involves analyzing acceptance criteria, planning, and transforming that information into appropriate test scenarios, which are then manually written and entered into a selected tool. **[Claude Code](https://claude.com/product/claude-code)** simplifies this process by automating it through Python scripts and the Jira API. The tool directly connects to Jira, retrieves ticket details such as acceptance criteria, and generates a .csv file with test scenarios, including both ideal paths and error or edge cases. \n\n**Claude Code** generates tests in .csv format, which can be easily imported into test management tools (e.g., Testmo). Automating this process makes the creation of test scenarios 96% faster, reduces the errors arising from manual creation, and provides better coverage.\n\n## Benefits of automating test cases – Time saving and greater consistency\n\n\n\nAutomating the generation of test cases using Claude Code brings several benefits:\n\n* Faster TC creation: Instead of hours spent writing test cases, the entire process now takes just a few minutes. Of course, time is still required to analyze the generated scenarios and make any necessary adjustments.\n* Better coverage of negative scenarios: AI focuses on edge tests and errors that might be overlooked in a manual process. In particular, **Claude Code** highlights issues like missing data, form errors, or API problems, which are crucial in detecting rare but serious bugs.\n* More consistency in formats: Tests are created according to a unified template, eliminating discrepancies between team members.\n\nThanks to this, the QA team can focus on more strategic tasks, such as requirement analysis, test results interpretation, and solving more complex problems, rather than spending time creating test cases.\n\n## Claude Code in action – A practical example\n\n\n\nSylwia presents real-world examples of using **Claude Code** to generate test cases. For instance, based on a ticket about employee transfer, AI generated over 10 test scenarios in just 2 minutes, which in traditional conditions might have taken 1-2 hours.\n\n## Summary\n\n\n\nAutomating the generation of test cases with Claude Code can significantly improve the efficiency of QA teams. Instead of spending valuable hours manually writing tests, teams can now focus on more advanced tasks, while Claude Code takes care of creating test scenarios quickly and accurately. It’s a practical tool that brings real change to the QA process, increasing test efficiency, accuracy, and consistency.\n\nIt’s important to remember that the starting point is a good description of requirements, and it’s always advisable to review, analyze, and make any necessary adjustments to the AI-generated scenarios.\n\nIf you want to learn more about how **Claude Code** can support your QA team, follow our blog for more innovative solutions!\n\nWe recommend these posts, which remain relevant despite widespread changes:\n\n\n\n****"}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1767880003/Group_1000005074-2_bis213.png","lead":"**Weekly AI Bites is a series that gives you direct access to what’s happening in our day-to-day AI work.** Every post comes straight from our team’s meetings and Slack, sharing insights, tests, and experiences we’re actively applying to real projects.What models are we testing, what challenges are we tackling, and what’s really working in products? You’ll find all of this in our bites. **Want to know what’s buzzing in AI? Check out Boldare’s channels every Monday for the latest weekly AI bite.**In the latest post, Sylwia Rapacz, a **QA Engineer** at Boldare, shares her experiences and tips on automating the generation of test cases (TCs) based on Jira tickets. With the help of Claude Code, Sylwia demonstrates how to use artificial intelligence to quickly and accurately create test scenarios, significantly improving the efficiency of QA teams.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2026-01-08T13:38:45.275Z","slug":"this-weeks-ai-bite-how-to-generate-test-cases-based-on-jira-tickets-guide-by-sylwia-rapacz","type":"blog","slugType":null,"category":null,"additionalCategories":["GenAI","News","Ideas","Tech"],"url":null},"author":"Sylwia Rapacz","authorAdditional":"","box":{"content":{"title":"This week’s AI Bite: How to generate test cases based on Jira tickets – A guide by Sylwia Rapacz","tileDescription":"Discover how Sylwia Rapacz, QA Engineer at Boldare, automates the generation of test cases from Jira tickets using Claude Code. Learn how AI boosts QA efficiency, reduces errors, and saves time in this insightful guide.","coverImage":""},"coverImage":null}},"id":"de21fe47-83c8-504a-b937-f0457d759107"}},{"node":{"excerpt":"","fields":{"slug":"/blog/boldare-joins-forces-with-szlachetna-paczka-making-a-difference-together/"},"frontmatter":{"title":"Boldare joins forces with Szlachetna Paczka – Making a difference together","order":null,"content":[{"body":"As part of this initiative, we prepared packages that were delivered to those most in need. It was a time of reflection and unity for us, where each of us could feel how important it is to share what we have with others.\n\n**[Szlachetna Paczka](https://www.szlachetnapaczka.pl)** is more than just packages – it’s a sense of community, support, and hope for those who need it most. We are proud to have been part of this wonderful endeavor, which changes the lives of many people for the better.\n\n**Helping is in our nature, and collective actions like Szlachetna Paczka show that together, we can truly make a difference.**\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1767181261/IMG_0715_vps8lp.jpg)\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1767181253/IMG_3523_a1mqa5.jpg)"}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1767181241/IMG_0791_1_m9tjtf.png","lead":"This year, we proudly joined **[Szlachetna Paczka](https://www.szlachetnapaczka.pl)**, an initiative that brings people together for a noble cause – helping those in need. Once again, the **Boldare** team has proven that helping others is our shared mission, and the positive energy flowing from such actions inspires us to create a better future.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-12-31T11:38:01.848Z","slug":"boldare-joins-forces-with-szlachetna-paczka-making-a-difference-together","type":"blog","slugType":null,"category":null,"additionalCategories":["People","News"],"url":null},"author":"Roksana Kaczmarska","authorAdditional":"","box":{"content":{"title":"Boldare joins forces with Szlachetna Paczka – Making a difference together","tileDescription":"Boldare proudly supports Szlachetna Paczka, an initiative that brings hope and help to those in need. Learn how we came together to make a real difference in the lives of others.","coverImage":""},"coverImage":null}},"id":"e3a92c29-9e4f-5079-bcfd-af10a21a2bca"}},{"node":{"excerpt":"","fields":{"slug":"/blog/how-to-build-mobile-app-with-cursor-ide-no-code/"},"frontmatter":{"title":"How to build a full mobile app using Cursor IDE without knowing how to code?","order":null,"content":[{"body":"## How Cursor can help build a mobile app\n\n\n\n1. ### Code generation:\n\nCursor can generate code snippets based on simple prompts. Whether you need a basic app interface or complex backend systems, Cursor can automate most coding tasks like user authentication, API calls, and database setups.\n\n1. ### Frontend development:\n\nFor mobile apps, Cursor uses React Native to help create user interfaces (UI) that work across platforms like iOS and Android. You can generate components, manage user interactions, and handle UI styling with minimal manual coding.\n\n1. ### Backend development:\n\nCursor can set up backend systems with services like Supabase or Firebase, managing databases and APIs. You can create an authentication system, connect databases, and manage app data without writing the backend code from scratch.\n\n1. ### Debugging and optimization:\n\nAs you work on your app, Cursor can help identify errors and suggest optimizations. It also assists with real-time feedback, allowing for quick fixes and improvements.\n\n1. ### Iterative development:\n\nBuilding with Cursor is an iterative process. You can make quick changes and test features, refining your app with each iteration. This allows for rapid prototyping and testing of new ideas."},{"body":"## Challenges you might face:\n\n\n\n1. ### Complexity:\n\nFor more advanced features like real-time data syncing or custom integrations, Cursor may fall short. While it can handle many standard tasks, more intricate components might require manual coding or troubleshooting.\n\n1. ### Learning curve:\n\nDespite the AI assistance, some understanding of basic programming concepts – like data structures, logic flow, and API usage – will be helpful. A lack of these skills can make it harder to communicate with the AI or troubleshoot issues.\n\n1. ### Code quality:\n\nThe code Cursor generates might not always be perfectly structured or optimized. For long-term maintenance, you might need to refine the generated code to ensure it meets best practices in terms of readability and scalability.\n\n1. ### Customization:\n\nIf you need highly specific features or custom designs, you might run into limitations with Cursor's pre-generated code. While it's great for standard app components, complex customizations may require you to dive deeper into coding."},{"body":"## Cursor development tips: FAQ\n\n\n\n1. **Question: How should I start working with Cursor?** \n\nAnswer: It's best to start by developing a small, simple app to get familiar with Cursor’s capabilities. Once you're comfortable, gradually increase the complexity of your project. \n\n1. **Question: How should I break down my project in Cursor?** \n\nAnswer: Break the development process into smaller tasks, focusing on one feature at a time. For example, start with user authentication before moving on to the UI or database setup. \n\n1. **Question: Do I need to know programming basics to use Cursor?** \n\nAnswer: While Cursor simplifies the process, understanding fundamentals like how APIs work and how databases are structured will help you make the most of the tool. \n\n1. **Question: Where can I get help if I run into issues?** \n\nAnswer: Take advantage of tutorials, documentation, and forums. Many users share their experiences, which can help you troubleshoot and speed up development. \n\n1. **Question: How should I handle initial setbacks in app development?** \n\nAnswer: Mobile app development is an iterative process. Don't be discouraged by initial challenges. Use Cursor to prototype your ideas and refine your app as you go.\n\n## Conclusion\n\n\n\nBuilding a mobile app using Cursor without coding knowledge is achievable, but it requires a mix of AI assistance, basic technical understanding, and continuous iteration. While Cursor handles the majority of the development work, having a foundational grasp of app concepts like authentication and data flow will help ensure your app functions as intended. With the right approach, anyone – regardless of their coding expertise – can build a functional and professional mobile app."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1765535322/MACH_najiee.png","lead":"To build a full mobile app using [Cursor](https://www.boldare.com/blog/what-is-cursor/) without knowing how to code, you can leverage Cursor's AI-driven features to generate frontend and backend code automatically. Cursor helps with tasks such as UI design using React Native, backend setup with tools like [Supabase](https://supabase.com/) for databases and authentication, and integrating APIs. \n\nWhile you don't need to write the code manually, a basic understanding of app development concepts – such as data flow, databases, and user authentication – will improve the app-building process. However, complete technical proficiency isn't required, as Cursor can guide you through the development of an app with minimal coding experience.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-12-30T08:36:51.424Z","slug":"how-to-build-mobile-app-with-cursor-ide-no-code","type":"blog","slugType":null,"category":null,"additionalCategories":["How to"],"url":null},"author":"Magdalena Chmiel","authorAdditional":null,"box":{"content":{"title":"How to Build a Full Mobile App Using Cursor IDE Without Knowing How to Code","tileDescription":"Learn how to leverage Cursor's AI-driven features to build a complete mobile app without coding expertise. Discover automated code generation, React Native UI development, and backend setup with Supabase.","coverImage":null},"coverImage":null}},"id":"19808cb9-37a7-5fcd-a628-a9dc0752ed29"}},{"node":{"excerpt":"","fields":{"slug":"/blog/boldare-achieves-prestigious-aws-certified-solutions-architect-associate-certification-1/"},"frontmatter":{"title":"Boldare achieves prestigious AWS Certified Solutions Architect. Associate certification","order":null,"content":[{"body":"## Certification benefits\n\nThe **AWS Certified Solutions Architect – Associate (SAA-C03)** certification is recognized globally as one of the most respected credentials in cloud computing. It validates our team’s ability to design AWS architectures following best practices, ensuring that solutions are resilient, cost-optimized, and can scale effectively as client needs grow.\n\nThis certification allows us to create systems that are not only secure but also built to perform at high levels over time. It gives our clients peace of mind, knowing that their cloud solutions are in the hands of a certified expert.\n\n## Why AWS certification matters to Boldare\n\nAWS is one of the leading cloud platforms used worldwide for building everything from MVPs to global digital products. Earning this certification shows that we’re not just technically competent but also aligned with AWS’s highest standards. With this expertise, we can now confidently design systems that are both cost-effective and high-performing – tailored specifically to meet our clients’ long-term business needs.\n\nThe significance of this certification is reflected in industry reports as well. According to Skillsoft's IT Skills and Salary Report, the AWS Certified Solutions Architect – Associate ranks among the [Top 10 IT Certifications of 2023](https://s3.us-east-1.amazonaws.com/skillsoft.com/prod/resources/Skillsoft-IT-Skills-and-Salary-Report-2023.pdf). Certified professionals are known to make more informed architectural decisions, gaining greater trust from technical teams and clients alike.\n\nAWS certifications like this one are part of our commitment to ongoing professional development. We believe that continuously upgrading our skills ensures that we stay ahead of the curve in delivering cutting-edge solutions.\n\n## What it means for future projects\n\nFor our clients, this certification directly translates into better, more reliable solutions. With this newly gained expertise, we’re now better equipped to design cloud architectures that are optimized for cost, security, and long-term scalability. This means fewer risks, better planning, and more confident decision-making for the projects we collaborate on.\n\nWe’re excited to continue working with AWS and bringing the latest best practices to every product we create. Our goal is to always ensure that your cloud solutions are not only fit for today but also adaptable to the challenges of tomorrow.\n\nTo learn more about **AWS certifications** and how they can elevate your cloud solutions, visit the [AWS Certification Overview](https://aws.amazon.com/certification/)."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1767018217/Group_1000005069_fbzbwz.png","lead":"We’re thrilled to announce that one of our DevOps at Boldare has earned the **AWS [Certified Solutions Architect – Associate](https://aws.amazon.com/certification/certified-solutions-architect-associate/)** certification. This is a significant achievement for our team, enabling us to strengthen our ability to deliver secure, scalable, and future-ready cloud solutions for our clients.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-12-30T07:57:43.351Z","slug":"boldare-achieves-aws-certified-solutions-architect-associate-certification","type":"blog","slugType":null,"category":null,"additionalCategories":["How to","Ideas","GenAI","Tech"],"url":null},"author":"Roksana Kaczmarska","authorAdditional":"","box":{"content":{"title":"Boldare achieves prestigious AWS Certified Solutions Architect – Associate certification","tileDescription":"Discover how Boldare has achieved the prestigious AWS Certified Solutions Architect – Associate certification, showcasing their expertise in designing secure, scalable, and cost-effective cloud solutions.","coverImage":""},"coverImage":null}},"id":"62acaeea-a127-5ddf-a498-1569966595a1"}},{"node":{"excerpt":"","fields":{"slug":"/blog/this-week-s-ai-bite-vibe-coding-ai-and-a-large-project-a-few-lessons-from-a-software-developer/"},"frontmatter":{"title":"This week’s AI Bite: Vibe coding, AI and a large project. A few lessons from a software developer","order":null,"content":[{"body":"## Tools and Process\n\nFor coding and planning, I used **Opus 4.5**, and for discussions and brainstorming, I relied on **Gemini 3.0** in the browser. My workflow was simple: first, I set up Cursor rules and the project context. Then, I figured out what I wanted to build, planned the implementation in Cursor (in plan mode), occasionally discussed ideas with Gemini 3.0, and finally, implemented everything with the AI agent.\n\nThe result? You can watch a video showcasing the final product.\n\n## Token Savings and Better Context in Large Projects\n\nThe game required large and fairly complex systems, even for basic mechanics and rendering. As a result, the codebase grew quickly, and at one point, the cost per prompt skyrocketed, with AI losing accuracy during the implementation.\n\nTo solve this, I created automated documentation tailored for the AI agent, and each new task was handled in a separate agent chat, with the documentation automatically included for context. This helped save tokens and improved the precision of the model.\n\n**Here’s the prompt I used:**\n\n> **We need to plan a project documentation architecture strictly optimized for AI to avoid wasting tokens on reading unnecessary files. Design a system (e.g., a context map or index files) that allows the agent to precisely pinpoint files for editing without loading the entire codebase. The documentation must be technical—written for a 'robot,' not a human. Also, add a main rule requiring documentation updates after major changes, such as creating new files or significant edits to core systems.**\n\nThe results were noticeable: better model accuracy and a significant reduction in token usage. You can see the folder structure and cost comparisons for the same task in the screenshots.\n\n## Clean Architecture and Clean Code Still Matter\n\nThere’s nothing new here, but it’s important to note: **if the code looks bad, it will behave badly**—maybe not immediately, but after a few prompts, it’ll show up.\n\nEven when AI follows rules like SOLID or DRY, it tends to dump everything into a single file and doesn’t always think about building flexible, scalable systems for the future.\n\nThe solution was simple: I had to explicitly remind AI to follow clean, scalable architecture and coding principles every time I created a plan. This could probably be automated with a rule during the planning phase, but I haven’t tested that yet.\n\nThe result? When a bug appeared, the agent could easily find it, fix it, and continue developing the application or game.\n\n## Opus 4.5: The Clear Winner (In My Opinion)\n\nThe quality difference between **Opus 4.5** and other models was huge. After working with Opus for a while, switching to another model became noticeable right away.\n\nWith a well-thought-out plan, **Opus practically nailed tasks in one shot**, without needing major fixes. However, switching to any other model caused the output quality to drop drastically—incorrect calculations, broken physics, and results that missed the mark entirely.\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1767014412/ssgry_p8n8ci.jpg)"}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1767014439/Group_1000005065-2_wnv8xq.png","lead":"**Weekly AI Bites** is a series that gives you a direct look into our day-to-day AI work. Every post shares insights, experiments, and experiences straight from our team’s meetings and Slack, highlighting what models we’re testing, which challenges we’re tackling, and what’s really working in real products. If you want to know what’s buzzing in AI, check Boldare’s channels every Monday for the latest bite.\n\nI wanted to share a personal experiment I’ve been itching to try for a while. I set out to build a 3D browser game, using AI as my primary tool for the entire process. The focus here isn’t on the game itself, but on what I learned throughout the journey—particularly the challenges and surprises that came from using **vibe coding**.\n\nThe goal? To test how far AI can take you when you have little understanding of a specific domain. I decided to dive into **3D game development in the browser**—an area where I’m a total novice and, to be honest, find quite technically challenging. Perfect for an experiment, right?\n\nIn this article, I’ll walk you through my approach, what worked, and where things got tricky, as well as the lessons I learned along the way.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-12-29T13:07:24.232Z","slug":"this-weeks-ai-bite-vibe-coding-ai-and-a-large-project","type":"blog","slugType":null,"category":null,"additionalCategories":["GenAI","Ideas","How to","Tech"],"url":null},"author":"Maksymilian Mogilski","authorAdditional":"Roksana Kaczmarska","box":{"content":{"title":"This week’s AI Bite: Vibe coding, AI, and a large project. A few lessons from a software developer","tileDescription":"Explore how AI and vibe coding helped build a 3D game in the browser. Learn key lessons from a developer's journey, including handling large projects, optimizing token usage, and ensuring clean architecture with AI.","coverImage":""},"coverImage":null}},"id":"84083bfd-b97c-5f8b-863e-e2dee42239ae"}},{"node":{"excerpt":"","fields":{"slug":"/blog/this-week-s-ai-bite-using-ai-in-api-migration-how-cursor-accelerated-our-work-at-boldare/"},"frontmatter":{"title":"This week’s AI Bite: Using AI in API migration – How Cursor accelerated our work at Boldare","order":null,"content":[{"body":"## Task overview\n\nOur goal was to replace the existing **API integration** with a new one, which required a major architectural overhaul. The old integration had multiple layers, while the new solution aimed to streamline the connection between the app and the API. The differences between the old and new APIs were significant, and since the app had already been under development for several months, the migration process posed a considerable challenge.\n\n## How did Cursor help us?\n\nTo facilitate the migration, we used **Cursor**, an **AI-powered tool** that, based on the API documentation, automatically generated a comprehensive Postman collection with the correct environment variables and simple scripts. This feature saved us a significant amount of time!\n\n## Mapping functionalities\n\nWe compared the functionalities of the old and new APIs to determine which ones needed to be migrated and what modifications were required.\n\n## Migration plan\n\nWe devised a detailed migration plan, ensuring it not only covered the API integration transfer but also enhanced the app’s architecture for greater scalability and **future growth**.\n\n## Our approach\n\nWe carried out the migration in stages, with each functionality being migrated step-by-step. The process included:\n\n* Generating the migration plan\n* Reviewing and refining the plan\n* Implementing the migration\n* Making minor adjustments during implementation\n\nThe use of pre-prepared templates and files made the entire process smoother and faster.\n\n## Current status\n\nThe old integration has been successfully removed, and the app is now operating with the new API.\n\n## Conclusion\n\nWithout AI, the migration would have taken much longer and been far more resource-intensive. With **Cursor’s support**, we saved hundreds of hours, and the process was completed smoothly and efficiently. The migration, which would have otherwise been drawn out for weeks or even months, was streamlined thanks to AI.\n\n> I truly believe that without AI, we would still be working on the migration for weeks, if not months\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1766154936/hhjb_pby18i.png)"}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1766154732/Group_1000005058_tvaizr.png","lead":"**Weekly AI Bites is a series that gives you direct access to what’s happening in our day-to-day AI work.** Every post comes straight from our team’s meetings and Slack, sharing insights, tests, and experiences we’re actively applying to real projects. \n\nWhat models are we testing, what challenges are we tackling, and what’s really working in products? You’ll find all of this in our bites. Want to know what’s buzzing in AI? **Check out Boldare’s channels every Monday for the latest weekly AI bite.**\n\nNow, let's jump into a recent experience where we migrated an API integration for one of our client projects...","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-12-19T14:27:24.320Z","slug":"this-weeks-ai-bite-using-ai-in-api-migration-how-cursor-accelerated-our-work-at-boldare","type":"blog","slugType":null,"category":null,"additionalCategories":["How to","Tech","GenAI","Ideas"],"url":null},"author":"Sebastian Musiał","authorAdditional":"","box":{"content":{"title":"This week’s AI Bite: Using AI in API migration – How Cursor accelerated our work at Boldare","tileDescription":"Discover how AI-powered Cursor streamlined our API migration process at Boldare, saving time and improving efficiency. Read about the tools, steps, and challenges we tackled in this week's AI Bite.","coverImage":""},"coverImage":null}},"id":"89fda7d5-a405-510f-9084-22fc3d37bd93"}},{"node":{"excerpt":"","fields":{"slug":"/blog/when-and-how-to-hire-a-fractional-cpo-practical-insights-by-melissa-stringer/"},"frontmatter":{"title":"When and how to hire a fractional CPO? Practical insights by Melissa Stringer","order":null,"content":[{"body":"Anna: Welcome to Around the Product Development in 25 minutes. I'm Anna, co-CEO at Boldare, where we help companies build and scale digital products. That's why we'll talk today about a very practical question for many founders and CEOs regarding digital products.\n\nAnd the question is: when does it really make sense to bring in external product leadership? And how do you make sure it accelerates growth instead of just adding costs or complexity?\n\nMy guest today is Melissa Stringer, a fractional Chief Product Officer with over 18 years of experience in FinTech and financial infrastructure, and an MBA from Cambridge, which adds a strong strategic lens to her product work.\n\nAnd the beautiful story is, I had the chance to work with today’s guest when she was the CPO on the client side. So I’ve seen her work and way of working up close. Melissa, welcome.\n\nIt’s really great to have you here.\\\n\\\n**Melissa**: Thank you so much. It’s really exciting to chat with you. I'm definitely a fan of this webinar series, and also loved working with you, Anna. So yeah, I’m very happy to be joining you.\n\n**Anna**: So we should start with the core question.\n\n## When is it the right time to hire an external CPO?\n\n\n\nAnd it would be, of course, everything is from your perspective, so it can be subjective. How do you know it’s the time to call in a fractional CPO? Is it before or after things start getting messy?\n\n**Melissa**: Well, I think in a lot of organizations, mess is par for the course. It happens in all stages of companies. But it’s most evident when the roadmap is more of a wish list and not really a strategy, but you maybe have some other pressures.\n\nSo it’s ahead of a round, like, you know, you’re trying to raise money, or maybe you’re aware that your competitors are doing certain things and you need to take some strategic action. It’s sort of when you need some change, some kind of catalyst for positive change in the organization. That’s when you would seek to hire somebody senior, fractional in product.\n\nIt can also be when you’ve done a merger or an acquisition, and every team has its own plan, but they’re not necessarily playing well together and not necessarily speaking from the same hymn sheet.\n\nSo there’s some kind of discord in the organization. It’s when you can kind of smell smoke, I would say, not when it’s on fire. But I mean, I definitely do get brought into situations that are 100% on fire, but it’s obviously nice to catch that before it happens.\n\n## How to avoid pitfalls when hiring a product leader?\n\n\n\n**Anna**: So the fire can be before hiring somebody from outside, but there’s a risk of doing things the wrong way then. So based on what you’ve seen, and I know that you’ve seen many places and options, what’s the fastest way a CEO can waste money on external product leadership?\n\n**Melissa**: I think it’s not trusting them to be part of the senior team, so they don’t give them access. You can’t speak to customers, you can’t have access to data, you’ve got no decision-making rights. You’re sort of locked into the existing informal and formal paths within the organization, which probably means there’s some kind of founder or C-level bottleneck, and no decisions can be made outside of that.\n\nAnd maybe the strategy has not been disseminated to the team, so people feel like they’re unable to take positive action. So in that scenario, if you already have that culture and you’re not willing to change it, bringing in external product leadership is probably just going to heighten your anxiety as that C-level person or founder.\n\nIf you're not willing to kind of change and adapt and be open and accept help.\n\n**Anna**: Yeah. So you hire someone and you have no idea how to distribute the authority. Yes. Oh, good. And from the perspective of a company or a CEO, hiring a fractional CPO comes with meaningful costs for most organizations, especially for companies in a growth phase or in transition.\n\n## What should change after hiring a fractional CPO?\n\nWhat specific indicators, or let’s call them performance signals, should convince a CEO that the investment is worth it? Like tangible outcomes, maybe, they can expect to see.\n\n**Melissa**: I think a main one is if they make any kind of positive change, whether it's operational efficiency, faster, better output, and if they can sustain that after you've gone, then it’s been very successful.\n\nOther things are, if the team can now articulate why they’re building something, I think that’s very positive. And if you’re actually shipping things iteratively towards validated outcomes, and not just because it’s a graveyard of wishes in the backlog.\n\nSo if it’s directional with strategic intent, I think that’s a win.\n\n## The first 48 hours as a CPO – how to get started?\n\n**Anna**: Okay, so let’s talk about your way. You enter a new environment, and when you join a team as a fractional CPO, what is your first 48 hours survival kit at the beginning?\n\n**Melissa**: The first thing I do is speak to people on the coal face. So it’s people who are doing the selling, probably customer service teams. I mean, a lot of the organizations I’ve worked for have been in financial services, which is highly regulated and often quite complex.\n\nThere’s a high degree of risk of misunderstanding, things going wrong. And also because often these companies scale quite quickly, there’s often disconnect between the teams internally.\n\nSo I think first doing that research piece to figure out what the pains are, try to understand what the ambition is for the organization, and where the disconnect is between what is said around the exec table and what’s understood or experienced by people on the coal face.\n\nThat’s what I try to understand first.\n\n**Anna**: And is openness the first thing you face? Or would you say no, it’s usually fine?\n\n**Melissa**: I mean, I’m quite skilled at building trust quickly, and I can empathize deeply with all of the different domains within a typical organization because I’ve had quite a varied career. So I can speak the language and the nomenclature of the different departments in an organization.\n\nSo I can speak to the compliance team and I’ll know what their concerns are, speak to customer service, speak to sales, and they’re frustrated that they don’t have the products that their customers want, or that we’re not being agile enough, and so forth. So I think it’s just about speaking their language, then listening for actionable threads, things where you could broker alignment and get people motivated around a culture of positive change.\n\n**Anna**: Good. And on the other hand, there is always some kind of ending to this engagement. What do you want the team to definitely know or be able to do by the time you leave?\n\n**Melissa**: I think having some kind of decision framework and being able to prioritize without me. That’s the first thing. And then being able to say no without it being political.\n\nSo improving the relationship between the different departments and the C-level, and kind of trying to smooth and eliminate politics, and making it permissible to say no.\n\nAnd also, I think helping them to understand who they’d need to hire next and why. I think that’s what the team should know when I leave.\n\n**Anna**: But do you usually end up training your own replacement?\n\n**Melissa**: I have, yeah, several times before. Often how it goes is that I’ve worked with somebody previously, either in-house or consulting, and I’ll be able to find that person to take over who wants to be in-house and do the work on a permanent basis.\n\nSo that’s how I try to ensure success when I leave a project, or I help them to secure quality candidates and help with the interview process and all of that.\n\n**Anna**: Is it a part of the work you actually enjoy or the opposite?\n\n**Melissa**: I really enjoy helping other product people with upward career mobility. I feel I’m quite strong at spotting hidden talents or people that may be underappreciated, who need a bit of coaching and then can blossom, having these really amazing careers.\n\nSo I definitely enjoy that, but I wouldn’t want to have a career in HR.\n\n**Anna**: Okay, good. And have you ever had a handover that went like funny or a bit chaotic?\n\n**Melissa**: Yeah. So I hope so. Yes. I was working for a consultancy firm, and we had a client in South America. It was a very large, high-stakes financial institution that had taken a massive amount of investment. We were working on some financial services products for them on a very aggressive timeline.\n\nIt was my job to be the consulting lead, so to speak—multidiscipline consulting lead—into this financial services organization. They had kind of mapped out for me the people I should meet internally.\n\nIt was maybe 20 individuals. But then on the very last day of delivering everything we’d done—the final presentation, packs, and handover documents—all of that, I get a DM to my mobile saying, “Well, you really need to meet this person.”\n\nI think it was internal politics and conflict. They hadn’t introduced us to this person, who was actually quite critical to the commercial investor side of the product and the organization.\n\nI think it was just that they were quite an intimidating and disagreeable character, and the people interfacing with us on this project didn’t want that person involved. So that was a bit of a nightmare, and I had to try and delicately moonwalk back a few paces, reframe, and try to get this person’s buy-in.\n\nSo yeah, there’s always this kind of trepidation in high-stakes consulting to make sure you’ve got everybody on board, aligned, and bought into the strategy and future thinking before you leave.\n\n**Anna**: Yeah, it was about the corporates, but I’m sure that everywhere there are some problems. So I want to ask about smaller companies or startups working closely with the founders.\n\nI’m sure it’s both challenging and entertaining. But from your perspective, what’s the founder behavior that makes your job harder? You know, maybe some kind of amusing behavior, but still, what can break the things you want to do?\n\n**Melissa**: Yeah, I mean, founders by nature are a little bit crazy, and they’re dreamers. That’s why I love working directly with founders. It’s extremely rewarding, and they usually have quirky personalities—high risk, high reward type people, which is really great.\n\nBut I think if you’re working on a technology project, sometimes there can be tension between stealth-mode obsessive founders who won’t let you talk to customers or are too afraid of opening up and stress-testing their ideas.\n\nSo they sort of want to build in the dark, but then expect an explosion of interest just because you built something in the dark. There’s always that kind of tension. You need to test concepts and validate and prove as you go.\n\nAlso, I’ve worked with founders who’ve just kept adding more and more and more stuff to the backlog, or just wanting one more feature. Another common refrain is that they have a limited number of clients, so each client is disproportionately powerful to the trajectory of the business.\n\nThey lose sight of the North Star, and think, “Well, this client needs us to do this by next Tuesday,” so everybody has to drop what they’re doing and make that happen. It’s about finding a balance between being reactive to what the founders want and helping them with emotional containment to stay focused on where the company’s going.\n\n**Anna**: We say sometimes—and we had this conversation before—that nobody is really ready for the product launch. I don’t know a person who is truly ready for that. It’s always difficult.\n\nBut from my perspective, people doing your job is sometimes the only way to really do it lean and build some kind of iteration, not the full vision.\n\nBecause it’s really difficult to have the full vision in your head and at the same time build something really small. So I think it’s one of the most critical elements. If you are not ready as a founder to think about your vision a little bit smaller, you should hire a fractional CPO.\n\nOtherwise, it’s really difficult. So yeah, I would say the same thing from the perspective of people building these products. Okay. The last topic I want to touch on, of course, would be AI. But I want to ask about AI-supported product development.\n\nNow it’s a standard part of the conversation, and whether we like it or not. From your experience, what does AI allow you to do differently today as a fractional CPO compared to, I don’t know, five years ago? Is it speed, decision-making, or different ways of working?\n\n## The role of AI in accelerating product development – changes over the last five years\n\n**Melissa**: I think it does all of those things. It’s accelerated delivery, definitely. For example, when we were working together, we’d have a hypothesis about something, and we’d be able to brainstorm it together, also battle-test some of those ideas with AI.\n\nThere’s the ability to automate routine parts of a product or an experience or a platform and create some sort of agentic workflows, which just wasn’t possible five years ago.\n\nThe adoption of AI tools has helped me accelerate my work dramatically. So quick, rapid prototyping, mocking up even brand experiences, having something that’s exciting and beautiful to show to customers immediately, without requiring necessarily heavy design resource.\n\nUntil you have more information—front-end development resources—you don’t need to mock these things up. It’s a bit more high-fidelity than what we used to do, stitching wireframes together in Figma, which used to be the client walkthrough.\n\nNow, you can mock up something that’s really high fidelity, beautiful, and bring people along for the journey. It helps to garner more trust in the direction and get more quality feedback.\n\nFor the engineers themselves, they’re so much faster and more confident in their work. You guys have some incredibly skilled and impressive AI developers, and I think they’d be fine with me saying they work alongside AI tools.\n\nSo it’s not necessarily only them coding stuff. They can battle-test their code as they build. They move much more quickly. The progress that can be made in short order is just incredible.\n\n**Anna**: I can’t even imagine going back then. So, yeah, when I look at the team’s work right now, I really believe that there’s more cooperation between product owners, founders, customers, and the team because everything can be really visible very quickly.\n\nWe can communicate faster as well. Okay, Mel, let’s make this practical for people listening who might be on the fence. If a company suspects they might need external product help, what’s the one question they should ask themselves first?\n\n**Melissa**: If they suspect they might need external help?\n\n**Melissa**: I guess, is this actually a product problem, or is it a decision-making problem?\n\nSorry. Yeah, so I think it’s: Is it a decision-making problem or a product problem? That’s the main question I’d ask them to validate.\n\nWhere do they want to spend their energy over the next six months? Do they want clarity and purposeful direction, or do they want to spend six months trying to figure out what that is, broker alignment, and gain trust?\n\nSometimes, just having an external senior perspective that can listen to everyone and bring a different point of view helps unlock organizations that are trapped in this paradigm where the founders keep everything in their minds, and they don’t disseminate enough strategy or decision-making throughout the organization.\n\n**Anna**: Yeah, yeah. It’s 27 past. Mel, thank you for this grounded conversation. I hope our listeners can use this as a reference point when they’re asking themselves: Is this something we actually need right now?\n\nAnd for everyone listening, if your product complexity is growing faster than your internal structure, or if you’re under pressure from the competition, I hope this discussion gives you a better lens for deciding whether fractional product leadership or someone from outside is the right option.\n\nThis was Around the Product Development in 25 minutes. Thanks for joining. I look forward to the next episode. And thank you, Melissa, again.\n\n**Melissa**: Thank you so much. I really appreciate it.\n\n**Anna**: Thank you. Bye.\n\n\n\n## FAQ: When and How Should a CEO Bring in a Fractional CPO to Accelerate Product Development?\n\n\n\n**1. When is it the right time to hire an external CPO?**\n\n* A fractional CPO should be brought in when the product roadmap is unclear or just a wish list, when you're facing pressure to raise money, or when teams struggle with alignment after a merger or acquisition. The goal is to bring in someone before things go “on fire” to help guide strategic action and foster positive change.\n\n\n\n**2. What are the common mistakes when hiring external product leadership?**\n\n* The fastest way to waste money is by not integrating the external CPO into the senior team, limiting their access to data, customers, or decision-making processes. If the team is not aligned with the strategy or culture, an external CPO may add more complexity rather than value.\n\n\n\n**3. What tangible outcomes should a CEO expect after hiring a fractional CPO?**\n\n* Positive indicators of success include improvements in operational efficiency, clearer product strategy, and more effective execution. The team should be able to articulate the \"why\" behind their work, ship products iteratively, and focus on outcomes with strategic intent.\n\n\n\n**4. How should a CPO spend their first 48 hours in a new environment?** \n\n* The first step is understanding the challenges at the \"coal face\" by speaking to frontline teams such as customer service and sales. Building trust quickly, listening to concerns, and identifying misalignments between the executive team and the broader organization are crucial to forming a foundation for improvement.\n\n\n\n**5. How has AI transformed product development for a fractional CPO?**\n\n* AI accelerates delivery and streamlines product development. It enables quick prototyping, automates routine tasks, and enhances the quality of product mockups without heavy design resources. It also allows engineers to work more efficiently, test their code, and make faster progress compared to just five years ago."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1767093694/grafika_w%C5%82as%CC%81ciwa_-_blog_vudaxw.png","lead":"**In this episode of *Around the Product Development in 25 Minutes*, Anna, co-CEO at Boldare, sits down with [Melissa Stringer,](https://melissastringer.ai/) a fractional Chief Product Officer with over 18 years of experience in FinTech. Together, they explore the pivotal role of fractional CPOs in driving growth and product strategy for scaling companies.**\n\n**Melissa shares her insights on when it's the right time to bring in external product leadership, the common pitfalls CEOs face, and how AI is transforming product development. From building trust within teams to streamlining decision-making, Melissa dives into how fractional CPOs can accelerate product development while avoiding common missteps. Check out the full transcript and listen to the episode.**\n\n\n\n","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-12-19T13:15:09.905Z","slug":"when-and-how-to-hire-a-fractional-cpo","type":"blog","slugType":null,"category":null,"additionalCategories":["Digital Product"],"url":null},"author":"Magdalena Chmiel","authorAdditional":"","box":{"content":{"title":"When & how to hire a fractional CPO for growth?","tileDescription":"Discover the key moments to bring in a fractional CPO, common pitfalls to avoid, and how AI is changing product development in this insightful interview with Melissa Stringer.","coverImage":""},"coverImage":null}},"id":"00f5a65f-01b6-5823-85ff-d43504b72837"}},{"node":{"excerpt":"","fields":{"slug":"/blog/anna-zarudzka-on-the-messy-growth-podcast-how-boldare-became-a-tech-leader/"},"frontmatter":{"title":"Anna Zarudzka on the Messy Growth podcast – How Boldare became a tech leader ","order":null,"content":[{"body":"One thing Anna said really stood out:\n\n> **“The moment you think you’re at the top – you’re already done.”**\n\nThis quote perfectly captures the philosophy that Anna, and the entire Boldare team, follow. The discussion delved into the importance of continuous growth, making bold decisions, and how a company can thrive in a world filled with uncertainty and change.\n\nDuring the conversation**, Anna explored Boldare’s approach to leadership**, particularly the concept of building a company without traditional hierarchy, something that Boldare practices through Holacracy. She explained how this model fosters greater autonomy and responsibility within the company, allowing teams to act more dynamically and efficiently.\n\nAnna also addressed why the idea of “being at the top” is a flawed goal. For her, true success lies not in reaching a final destination but in the continuous evolution of a company and its people. She shared her insights on running a services business by choice, not by default, and how Boldare’s decision to embrace this path has shaped the company’s unique culture and approach to client relationships.\n\nReflecting on her 17 years of experience in building Boldare, Anna offered valuable lessons about leadership. She explained that leadership is not just about making the right decisions but also about learning from mistakes, adapting to change, and guiding teams through uncertainty.\n\nThe conversation sheds new light on what true success and leadership mean. It’s clear that growth is not about reaching the top – it’s about continuous evolution, adaptability, and learning from every step of the journey.\n\n🎧 **Listen to the full episode here:** \n\n"}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1766059429/Group_1000005049_edarqp.png","lead":"We are really excited to announce that **Anna Zarudzka**, Co-CEO of Boldare, was featured in the latest episode of the *[Messy Growth](https://www.youtube.com/@messygrowthhustlex)* podcast! In this insightful conversation, Anna shared the story of how Boldare transformed from a small company into a technological leader – driven by courage, innovation, and perseverance.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-12-18T11:34:19.464Z","slug":"anna-zarudzka-messy-growth-podcast-boldare-tech-leader","type":"blog","slugType":null,"category":null,"additionalCategories":["Ideas","Strategy","Video","People"],"url":null},"author":"Roksana Kaczmarska","authorAdditional":"","box":{"content":{"title":"Anna Zarudzka on the Messy Growth podcast – How Boldare became a tech leader ","tileDescription":"Listen to Anna Zarudzka, Co-CEO of Boldare, share the inspiring story of how Boldare transformed from a small company into a tech leader. Discover leadership insights, Holacracy in practice, and the power of continuous growth.","coverImage":""},"coverImage":null}},"id":"a44d532b-e1fe-5d98-822e-c71393782e6c"}},{"node":{"excerpt":"","fields":{"slug":"/blog/what-is-cursor-ai-augmented-ide-transforming-developer-workflow/"},"frontmatter":{"title":"What is Cursor – AI‑Augmented IDE transforming developer workflow","order":null,"content":[{"body":"## **Introduction**\n\n\n\n[Cursor](https://cursor.com/agents) is an innovative AI-powered development environment that analyzes a project and its structure. By integrating with language models like GPT, Cursor understands code, dependencies, and project context, enabling automation of many programming processes such as refactoring, code analysis, and component generation. Developers can issue instructions in natural language, and AI handles the implementation of the changes.\n\n\n\n## **How does Cursor work?**\n\n\n\n### **2.1 Code Analysis**\n\nCursor operates by performing a full analysis of the project's structure, rather than just working on individual files. With this capability, AI understands the entire codebase and the impact of changes on other elements of the system. Developers don’t need to worry about incorrect dependencies because Cursor performs operations independently, ensuring code consistency.\n\n### 2.2 Natural Language as an Interface\n\nInstead of manually modifying code, Cursor allows developers to issue commands in natural language, such as “Change this function to comply with the new standard.” This makes using Cursor simpler and more intuitive.\n\n\n\n## **When should you use Cursor?**\n\n### **3.1 Large Projects and Complex Codebases**\n\nCursor is ideal for working on large projects, where manually editing multiple files and dependencies could be time-consuming and error-prone. By analyzing the entire codebase, Cursor can make consistent and efficient changes, saving time and reducing the risk of mistakes.\n\n### 3.2 Automating Repetitive Tasks\n\nMany repetitive tasks, such as code refactoring, changing coding standards, or removing unused imports, can be automated by Cursor. Instead of performing these tasks manually, developers can delegate them to AI, allowing them to focus on more complex issues\n\n### 3.3 Onboarding New Developers\n\nIn large projects, onboarding new developers can be challenging. With Cursor, new team members can quickly understand the code structure and project principles, speeding up the integration process.\n\n## Cursor’s Limitations\n\nWhile Cursor is a powerful tool, it doesn’t completely replace human creativity and experience. In more complex cases, where specific decisions about the code are required, the developer still needs to take control of the project. Additionally, for Cursor to work effectively, the project must be well-organized and adhere to consistent standards.\n\n## Cursor in the Context of AI in Software Development\n\nCursor fits into the growing trend of using artificial intelligence in the software development process. Automating tasks such as refactoring and code analysis speeds up development, reduces errors, and improves the quality of the final product. With Cursor, developers can focus on more strategic tasks, such as designing system architecture, while AI handles routine tasks.\n\n## Conclusion\n\nCursor is a modern tool that changes the way developers work, especially in large projects with multiple files and complex dependencies. By leveraging artificial intelligence, Cursor automates processes such as code refactoring, generating new code, and maintaining consistency across the project. While it doesn’t fully replace human creativity, it provides valuable support for development teams. If you want to learn more about how Cursor helps development teams, I encourage you to read the detailed interview [“How to AI‑Augment Your Dev Team with Cursor AI IDE” on the Boldare blog](https://www.boldare.com/blog/how-to-ai-augment-your-dev-team-with-cursor-ai-ide-interview-with-maksymilian-mogilski/)."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1736508212/MACH_2.png","lead":"**Cursor is an AI‑augmented integrated development environment (IDE) that understands code structure and project context, automating refactoring and accelerating the development process. Learn how it works and when it’s beneficial to use.**","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-12-16T11:25:52.000Z","slug":"what-is-cursor","type":"blog","slugType":null,"category":null,"additionalCategories":["How to"],"url":null},"author":"Roksana Kaczmarska","authorAdditional":"","box":{"content":{"title":"Cursor: AI-Augmented IDE transforming developer workflow","tileDescription":"Cursor is an AI-powered IDE that automates refactoring, understands code structure, and accelerates development. Discover how it enhances your coding workflow.","coverImage":""},"coverImage":null}},"id":"1e5bb6bc-585e-50d3-9d8a-70c5e4035d91"}},{"node":{"excerpt":"","fields":{"slug":"/blog/this-week-s-ai-bite-ai-and-design-automatic-generation-of-design-systems-from-existing-websites/"},"frontmatter":{"title":"This week’s AI Bite: AI and design – Automatic generation of design systems from existing websites","order":null,"content":[{"body":"## Context drives quality\n\nAI models deliver far better results when they are given meaningful context. One effective approach is to use an existing website as a reference point. By doing so, the model can infer a style guide directly from a real product, which leads to more consistent, natural-looking interfaces and cleaner, better-structured code.\n\n## Automating the workflow\n\nThe application works in a straightforward way. A user provides a URL, and the system retrieves the page’s HTML and CSS. It then performs a comprehensive analysis, examining both the DOM structure and the actual rendered styles using the `getComputedStyle` function. Screenshots are also generated to detect repeating visual patterns across the interface.\n\nFrom this data, the system extracts core visual attributes such as color palettes, typography, spacing, grid systems, and recurring UI components like buttons, cards, forms, and navigation elements. These findings are normalized and stored in a JSON format that describes components, their variants, and style properties, including states like hover or disabled. This dataset becomes the contextual input for AI models such as **Claude, Cursor, or Lovable,** enabling them to generate a ready-to-use Design System. The output can be applied directly to prototypes and UI projects, ensuring both visual consistency and high-quality code.\n\n## Practical use cases\n\n* This approach makes it possible to:\n* prototype quickly using an existing website’s visual style,\n* create visually consistent interfaces without manually rebuilding styles,\n* support designers and developers in speeding up UI work, especially in projects where brand consistency matters.\n\n## Summary\n\nBy combining automated style extraction with AI, it becomes possible to generate Design Systems that align closely with existing branding. This significantly accelerates the design process while improving the overall visual quality of prototypes. Feedback is welcome, and the project is available as open source:\n\n👉 \n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1765803375/1765212476130_lexe9g.jpg)"}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1765803144/1765212349438_gpebwd.png","lead":"**Weekly AI Bites** is a series that gives you a direct look into our day-to-day AI work. Every post shares insights, experiments, and experiences straight from our team’s meetings and Slack, highlighting what models we’re testing, which challenges we’re tackling, and what’s really working in real products. If you want to know what’s buzzing in AI, check Boldare’s channels every Monday for the latest bite.\n\nAI tools like Claude, Cursor, and Lovable are becoming increasingly capable of generating user interfaces. The main limitation appears when these models operate without proper context — the result is often generic UI that lacks visual coherence and a distinct identity.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-12-15T12:42:16.019Z","slug":"ai-and-design-automatic-design-system-generation","type":"blog","slugType":null,"category":null,"additionalCategories":["How to","GenAI","Tech"],"url":null},"author":"Maksymilian Mogilski","authorAdditional":"","box":{"content":{"title":"This week’s AI Bite: AI and design – Automatic generation of design systems from existing websites","tileDescription":"How AI can automatically generate design systems from existing websites. A practical AI Bite on context-driven UI, consistency, and faster product design.","coverImage":""},"coverImage":null}},"id":"e8399678-f082-5767-a81a-fc20d1ae5b9a"}},{"node":{"excerpt":"","fields":{"slug":"/blog/this-week-s-ai-bite-can-ai-build-an-application-from-scratch-our-front-end-developer-tests-the-capabilities-of-opus-4-5/"},"frontmatter":{"title":"This week’s AI Bite: Can AI build an application from scratch? Our front-end developer tests the capabilities of Opus 4.5","order":null,"content":[{"body":"## First steps: Code generation and UI setup\n\nFrom the start, **Opus 4.5 generated code with surprising confidence**. In just a few prompts, it created a **working HTML structure, layout, and transformation logic**. The result was **minimalistic yet functional**, ready for testing without unnecessary visual frills.\n\n## Challenges in game level configuration\n\nThe mini-game focused on **text transformations and encoding** (Base64, ROT13, Caesar Cipher, Atbash). These fine-grained operations posed a challenge for the LLM, revealing **mistakes**: some transformations were **incorrect**, the **order of operations failed** in some cases, and a few levels were **initially impossible to complete**. Importantly, this was intentional, the goal was to see how the model would **identify and fix its own errors**. Through **iterative testing**, Opus made mistakes, evaluated results, and corrected them, demonstrating **self-directed debugging**.\n\n## The model as user, tester, and developer\n\nThe experiment took a new dimension when Opus was asked to **\"click through\" the application in the browser**. The model **ran through the levels, verified operations, returned to the code, and fixed errors**. In effect, Opus created a **full iterative feedback loop**, acting simultaneously as a **tester** and a **developer addressing regressions**.\n\n## Multimodality in action: UI improvements\n\nAnother insight emerged: Opus handles **front-end tasks more effectively** when it can **see the rendered UI**. It suggested **improvements in spacing, alignment, and readability** based on how the interface actually appeared. This ability to **analyze visual output** allowed Opus to combine the roles of **front-end developer and UI designer** intuitively. By “seeing” its own work, it could **deliver responsive design** and **replicate the intended interface accurately**, a true **game-changer for front-end development**.\n\n## What this means for Opus 4.5\n\nEven though the experiment began as a small mini-game, it revealed a critical insight: **Opus 4.5 is not just a code generator**. It can **understand application behavior, run tests, identify errors, and fix them independently**. This positions the model as an **active participant in development**, rather than a passive assistant.\n\n## Summary: Small project, big insights\n\nThe experiment highlights the **potential of multimodal AI** in digital product development. Models like Opus 4.5 can **understand and interact with their environment, respond to errors iteratively**, and **collaborate with humans at every stage of development**. This doesn’t replace developers, it **augments their workflow**, enabling **faster, smarter, and more reliable product creation**.\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1765213888/1764599486322_p4ybkc.png)"}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1765213603/1764598767197_klocys.png","lead":"**Weekly AI Bites** is a series that gives you a direct look into our day-to-day AI work. Every post shares insights, experiments, and experiences straight from our team’s meetings and Slack, highlighting what models we’re testing, which challenges we’re tackling, and what’s really working in real products. If you want to know what’s buzzing in AI, check Boldare’s channels every Monday for the latest bite.\n\nAt Boldare, we regularly explore AI solutions that can improve product development. Recently, our front-end developer decided to test how far **Opus 4.5** could go by combining code generation, reasoning, and direct browser interaction. What seemed like a simple experiment turned into an exploration of the ways AI could fundamentally change how we build and test digital products.\n\n**The game and repository are publicly available:**\\\n**Demo:** [crypto-game-opus-4-5.netlify.app](https://crypto-game-opus-4-5.netlify.app/)\\\n**Repository:** [github.com/jankepinski/crypto-game](https://github.com/jankepinski/crypto-game)","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-12-08T17:02:15.403Z","slug":"this-weeks-ai-bite-can-ai-build-an-application-from-scratch-our-front-end-developer-tests-opus-4-5","type":"blog","slugType":null,"category":null,"additionalCategories":["GenAI","Digital Product","Tech"],"url":null},"author":"Jan Kępiński","authorAdditional":"","box":{"content":{"title":"Can AI build an application from scratch? Our front-end developer tests the capabilities of Opus 4.5","tileDescription":"Discover how our front-end developer tested Opus 4.5 to build a mini application from scratch. See how AI generates code, runs tests, and fixes errors independently.\n","coverImage":""},"coverImage":null}},"id":"06fecb34-7bac-581a-b82b-eb1d9897b0fa"}},{"node":{"excerpt":"","fields":{"slug":"/blog/ai-powered-digital-assistant-development-of-an-ai-driven-work-management-automation-solution/"},"frontmatter":{"title":"AI-powered digital assistant – development of an AI-driven work management automation solution","order":null,"content":[{"body":"**The client:** Due to NDA, client name cannot be disclosed\n\n**Country:** Germany\n\n**Form:** Startup with a B2C focus (and with a B2B perspective in the future)\n\n**AI-enhanced development benefits in the project:** We accelerated some processes by up to 50%\n\n## Vision and challenge: building an AI-powered assistant for work management\n\nThe client proposed creating an AI system automating job management, aiming to keep inboxes at zero. The vision was to build an advanced workplace assistant powered by a multi-agent AI system that integrates calendars, email inboxes, and the user's work context, with a unique memory system to manage vast volumes of emails. \n\nTo realize this, a scalable, stable, and secure infrastructure capable of analyzing emails and providing contextual memory for AI agents was needed. **We were invited to design and deliver the first critical layer of the product – the foundation upon which the entire system depends.**\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1768224794/Blog_xaylla.png)\n\n\n\n### The vision in four phases\n\n1. The heart of the app automates email management – each message is analyzed (author, content, intention, urgency) and automatically sorted into the correct folder\n2. This analysis is integrated with the calendar, meeting schedule, and other mailboxes, forming a cohesive, context-aware memory.\n3. Leveraging this data, AI agents assist with managing correspondence, organizing daily or weekly schedules, prioritizing tasks, finding critical information, and initiating micro-automations.\n4. Leveraging this data, AI agents assist with managing correspondence, organiziWhen appropriate, the system delegates simple tasks (e.g., confirming details or collecting materials) to the right person, providing pre-defined next steps and calendar suggestions.g daily or weekly schedules, prioritizing tasks, finding critical information, and initiating micro-automations.\n\nAccording to the overall vision, the system will unify all tools into one platform, not only speeding up work but also caring for the user’s well-being by minimizing distractions. \n\nIt will suggest breaks in the calendar, reschedule meetings, and handle tasks on behalf of the user. Additionally, it will remind users of important non-work-related matters, such as family time.\n\n## Solution\n\nWe focused on delivering the first and crucial phase of the project: automating email management. The system analyzes incoming emails, assessing attributes like sender, content, intention, and urgency, and automatically sorts them into the appropriate folders. Before this process begins, the user first provides insight into their work habits and priorities by answering onboarding questions via voice, helping the AI understand their unique workflow. Based on this input, the AI generates a customized list of suggested folders tailored to the user’s needs. \n\nFor example:\n\n`If the user works for Client A on Projects B and C and specifies that this is important to their workflow, the AI system will create a folder for Client A, with subfolders for Projects B and C.`\n\nThis approach ensures that each user’s inbox is organized according to their specific needs. The system continuously improves its ability to categorize emails accurately, adapting to the user's preferences over time. It’s designed to handle multiple mailboxes and large volumes of emails, ensuring that even users with extensive inboxes benefit from automated sorting. \n\nThis phase establishes a scalable and secure foundation, setting the stage for future integrations with calendars, work contexts, and other tools. By automating email management, we’ve laid the groundwork for a more efficient, intelligent assistant, with future phases enhancing task prioritization and workflow management.\n\n## How did we work? Agile Delivery of AI-powered solution\n\nWe worked using the Scrum methodology, which allowed us to quickly and flexibly respond to changes and efficiently implement feedback. **The project kicked off with workshops involving key stakeholders: developers, the Agile Project Lead (APL), the CEO, and the Product Owner (PO).** \n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1768224809/Blog_xp9n53.png)\n\n\n\nThe goal of these workshops was to identify the features that would have the greatest impact on automating users' work and to map out the essential processes to improve user efficiency and well-being. \n\nTo ensure steady progress, we adopted weekly sprints that allowed us to adapt to real-time needs and expectations. The project quickly attracted interest from a few organizations, which made it clear that to meet the enterprise security level, we needed a robust cloud infrastructure. After careful evaluation, we decided to migrate to GCP as our primary cloud provider.\n\nThis strategic move, while challenging, allowed us to harness modern cloud capabilities, ensuring that the infrastructure could scale efficiently to support the growing needs of the project. \n\n**Our team, consisting of 2 Fullstack and GenAI developers, an APL, and a designer on an as-needed basis, worked collaboratively in this iterative process.** This approach helped us consistently deliver high-quality solutions tailored to both business and technical requirements.\n\n## AI technologies and tech stack used in the project\n\n\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1768224818/Blog_kn0cse.png)\n\nIn our development workflows, AI has become a central component, and we have extensively incorporated this innovation into our project. By blending engineering expertise with advanced technologies, we ensure that our approach is both secure and thoughtful, allowing us to build in a more intelligent and effective manner. \n\nThe project was built using a contemporary tech stack that included **technologies such as Next.js, Vercel, Supabase, React, Node.js, AI SDKs, and memory management tools**. After migrating to a cloud platform, we integrated cloud services and PostgreSQL into the infrastructure. \n\nA pivotal choice was to begin with systems that facilitated rapid iteration, **such as Vercel, Next.js, Supabase, and pre-built templates**. These tools allowed us to leverage ready-made components and assign many standard modules, significantly reducing the project scope and speeding up development. AI played a key role throughout the development process. \n\nWe utilized advanced AI tools to enhance coding efficiency, automate code generation, and maintain high-quality standards. We also implemented AI-based code review suggestions, automated code generation templates, and continuous code-quality monitoring, which reduced manual, repetitive tasks and helped maintain code quality from the early stages. \n\nWe embraced automation across the project, implementing [full CI/CD pipelines ](https://www.boldare.com/blog/continuous-delivery/)to streamline the development and deployment process. Moreover, the entire infrastructure is managed through infrastructure-as-code tools, ensuring consistent and traceable deployments. \n\nTo build core functionalities, we leveraged pre-built components for user authentication and used memory management tools to quickly develop the system. By embedding AI-augmented development into our workflow, we ensured that our approach was fast, adaptive, and efficient, delivering a high-quality product that can scale.\n\n## Ensuring security and scalability in development\n\nIn ensuring scalability, security, and performance, our team relied on proven technologies and solutions that optimized the entire system. Security, which is a top priority for us when developing software, played a key role in every aspect of the project. \n\nWe exclusively used dependencies with security certificates and implemented AI in a way that ensures user data is not used for model training. In the future, we plan to fully anonymize data sent to language models. \n\nAdditionally, **we deployed cloud-based solutions to ensure data protection, storing all data in our own database. In the future, we also plan to host sensitive dependencies on-premises.** Scalability was achieved by using popular tools that allow us to delegate tasks, which would normally require complex coding, such as login panels or chat views. \n\nRegarding performance, the use of the latest technologies and Server-side Rendering significantly reduced the load on the user’s machine. The project also involved integration with leading AI/LLM providers, enabling smooth connections with external systems and legacy solutions, thereby ensuring flexibility and broad compatibility across diverse environments.\n\n## Conclusion: delivering an AI-driven digital assistant for smarter workflows\n\nWe’ve successfully completed the initial phase of the project, laying the groundwork for email management automation. By analyzing each incoming email – taking into account its sender, content, and urgency – we’ve effectively streamlined a crucial part of the user’s daily operations. \n\n**This phase was fully AI-enhanced, with experienced engineers ensuring that the system delivers both efficiency and precision. This milestone paves the way for the next phases as we look to expand the system’s capabilities to manage other aspects of work.** \n\nWe invite you to follow our channels for updates on the project's progress and to stay informed about the exciting developments ahead."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1764689875/AIpowered_assistant_kfra23.png","lead":"I’m sure you’ve heard this statistic – today, [people process as much as 74 GB of information daily](https://kids.frontiersin.org/articles/10.3389/frym.2017.00023?utm_source=chatgpt.com). How much is that really? To put it into perspective, **that’s like watching 16 full movies in a single day**. This information comes from TV, computers, phones, tablets, billboards, and various other screens, including those we interact with at work. **In fact, this number grows by around 5% each year.**\n\nFive centuries ago, during the transition from the Middle Ages to the Renaissance, **74 GB represented the total amount of information a well-educated person would absorb in their lifetime**. Can technology, the very cause of this information overload, also help us manage it – by sorting, prioritizing, and filtering only what matters?\n\nThe answer is yes, and this is where the story of this **AI-powered digital assistant begins.**\n\nThe assistant is a digital tool that optimizes and manages your work based on your mailbox and the context of your daily workflow. **Discover the full story behind the AI-powered digital assistant – conceived by the client and developed in collaboration with Boldare.**","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-12-01T15:30:42.074Z","slug":"ai-powered-digital-assistant-work-management-automation","type":"blog","slugType":null,"category":null,"additionalCategories":["GenAI"],"url":null},"author":"Magdalena Chmiel","authorAdditional":"","box":{"content":{"title":"AI assistant for email automation & workflow efficiency","tileDescription":"Discover how an AI-powered solution automates email management, streamlines workflows, and enhances user efficiency. Learn about the project's phases, agile development approach, and AI technologies driving innovation.","coverImage":""},"coverImage":null}},"id":"898a7a2f-c36c-5dd3-8ae7-a2da43cc0d88"}},{"node":{"excerpt":"","fields":{"slug":"/blog/top-10-ai-driven-software-companies-in-2026/"},"frontmatter":{"title":"Top 10 AI-Driven Software Companies in 2026","order":null,"content":[{"body":"## Boldare\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1759139538/65_smol6v.png)\n\n* Company size: 70 professionals\n* Founded: 2004\n* Website: [https://www.boldare.co](https://www.boldare.com/)m\n\nHuman-first, AI-augmented digital product builders\n\nFounded in 2004, Boldare is a Polish software consultancy delivering custom software development, product design, and AI-augmented engineering services for companies operating across Europe and global markets. The company supports organizations throughout the entire digital product lifecycle - from MVP development and product-market fit to scaling platforms and entering new markets.\n\nBoldare combines strong software engineering foundations with the practical adoption of AI technologies in software delivery. Its teams work within agile, cross-functional setups, aligning technical execution with business and product goals.\n\n**AI in Software Development & Daily Coding Practices**\n\nBoldare actively integrates AI into daily coding practices and delivery workflows. Engineering teams use AI-assisted coding, AI-supported debugging, automated code reviews, and intelligent code completion to improve efficiency and maintain consistent code quality.\n\nAI-powered development tools are applied across development, testing, and delivery processes to:\n\n* automate repetitive tasks\n* streamline workflows\n* enhance coding processes\n* improve code quality and maintainability\n* accelerate development cycles\n\nThe company approaches AI as a coding partner that supports developers rather than replaces them, reinforcing human-AI collaboration in software engineering and enabling teams to focus on higher-value problem solving.\n\n**Software Development Services & Technical Expertise**\n\nBoldare delivers bespoke software solutions tailored to specific business needs. Its service offering includes:\n\n* custom software development\n* AI-powered and AI-supported development\n* product design & UX/UI\n* legacy modernization and system migrations\n* architectural optimization\n* large-scale integrations\n* cloud-based and scalable infrastructure\n\nProjects are delivered using technologies such as React, Node.js, PHP, Java, and AWS, supporting both new product development and modernization of existing systems for midsize and large tech companies.\n\n**Trusted by Global Clients**\n\nBoldare works with international and European companies across a wide range of industries, supporting long-term digital product development and technology modernization initiatives. Client collaborations often emphasize transparent communication, close cooperation with internal teams, and consistent focus on product and technical goals.\n\nThe company’s client portfolio includes BlaBlaCar, Bosch, Decathlon, Sonnen, Prisma, and e.l.f. Cosmetics, representing sectors such as renewable energy, fintech, retail, SaaS, and public services.\n\n## InoXoft\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1754312772/47_idrgvs.png)\n\n* Company size: 100 - 249 professionals\n* Founded: 2014\n* Website: \n\nData-centric and AI-enabled software engineering for complex digital products\n\nInoXoft is a software development company specializing in building data-intensive and AI-enabled digital products for companies that operate in complex, regulated, or rapidly evolving environments. The company works with organizations that rely heavily on structured and unstructured data, advanced analytics, and intelligent system behavior to support business-critical processes.\n\nRather than positioning itself as a pure AI vendor, InoXoft approaches AI as an embedded capability within software systems - applied where automation, prediction, or intelligent data processing directly improves how products function in real-world scenarios. This perspective aligns closely with modern AI-driven software development, where intelligence is part of the architecture, not a separate layer.\n\nInoXoft’s engineering teams focus on integrating AI technologies into backend systems, data pipelines, and application logic, supporting use cases such as data aggregation, predictive features, and intelligent automation. AI is introduced alongside strong software engineering practices, ensuring that systems remain scalable, maintainable, and understandable as they grow.\n\nThe company’s work is closely tied to data engineering and system reliability. Many of its projects emphasize how data flows through platforms, how insights are generated, and how software reacts to changing inputs over time. This makes InoXoft particularly relevant for AI-driven products where accuracy, consistency, and long-term performance matter as much as speed.\n\nInoXoft collaborates with clients across industries such as fintech, healthcare, logistics, SaaS, and enterprise platforms - domains where AI-supported decision-making and intelligent data processing play an increasingly important role. Engagements often involve long-term development, system evolution, and close cooperation with internal client teams.\n\nBy combining software engineering expertise with a strong focus on AI-enabled data processing and intelligent system design, InoXoft positions itself as a technology partner for organizations building products where AI is not an experiment, but a functional part of everyday software operations.\n\n## Glorium Technologies\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1771320742/undefended_z4cawe.jpg)\n\n* Company size: 250 - 499 professionals\n* Founded: 2010\n* Website: \n\nAI-Driven Engineering and Product Innovation for Complex Enterprise Systems\n\nGlorium Technologies is a software engineering company that focuses on building enterprise-grade digital platforms and AI-driven solutions for organizations requiring advanced technical expertise and scalable digital ecosystems. With a strong foundation in full-stack engineering and strategic product thinking, Glorium helps companies transform business requirements into resilient, intelligent systems.\n\nRather than offering AI as an isolated capability, Glorium integrates AI technologies into core product functions, particularly in scenarios where machine learning, intelligent data processing, or predictive logic add measurable business value. The company’s engineering teams work closely with clients to identify opportunities where AI can support automation, elevate user experiences, or enrich data-centric functionality in ways that go beyond rule-based software.\n\nGlorium’s approach to AI is grounded in applied software engineering practices. Teams focus on building systems where AI capabilities - such as real-time analytics, intelligent data augmentation, or automated user interaction models - are cohesive elements of the overall architecture, not afterthoughts. This makes Glorium a strong partner for organizations moving from proof-of-concepts to production-ready intelligent systems.\n\nThe company’s capabilities span a range of complex engineering requirements including distributed systems, cloud-native platforms, custom integrations, and modular product design. In projects involving AI, Glorium often supports use cases such as smart data ingestion, user behavior insights, and algorithm-supported decision logic - all designed to enhance system adaptability and long-term maintainability.\n\nGlorium Technologies collaborates with clients across multiple industries including fintech, healthcare technology, logistics, and enterprise SaaS platforms. Engagements typically involve deep technical partnership, long-term development cycles, and shared ownership of evolving product roadmaps. The company’s delivery model prioritizes clear communication, incremental value delivery, and iterative refinement of both product features and underlying intelligent capabilities.\n\nBy combining robust software engineering expertise with practical AI integration, Glorium positions itself as a partner capable of delivering product solutions where AI enables meaningful improvement in performance, user value, and technical resilience.\n\n## ELEKS\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1771320818/ELEKS_tr_Blue_yl0avl.png)\n\n* Company size: 1000 & up professionals\n* Founded: 1991\n* Website: \n\nEngineering innovation and AI-enabled transformation for enterprise scale\n\nELEKS is a global technology company that partners with large enterprises and fast-growing organizations to deliver complex digital solutions enhanced by AI and intelligent automation. With decades of experience in bespoke software delivery, ELEKS supports clients in reimagining core product functions through data-driven insights, machine learning models, and AI-powered features that extend beyond traditional application development.\n\nRather than treating AI as a standalone add-on, ELEKS embeds AI technologies within its engineering and consulting workflows - identifying areas where artificial intelligence can materially impact outcomes such as operational efficiency, predictive analytics, and automated decision support. This includes integrating machine learning into analytics platforms, building custom models that interpret enterprise-scale data, and applying AI-based pattern recognition to enhance process automation.\n\nELEKS’s delivery model emphasizes strategic alignment between engineering execution and business value, helping clients not only adopt AI but also integrate it into their broader transformation agendas. Cross-disciplinary teams of engineers, data scientists, and product strategists collaborate with client stakeholders to ensure that AI initiatives are grounded in practical outcomes rather than exploratory experiments.\n\nThe company’s engineering expertise covers a wide range of technical domains - from advanced analytics and intelligent data processing to secure integrations and scalable microservices architecture - with AI acting as a catalyst for enabling richer insights and more adaptive systems. Project engagements often include designing AI-augmented systems that help organizations respond to changing business needs, personalize user experiences, or optimize internal operations at scale.\n\nELEKS works with a diverse portfolio of clients across industries such as finance, healthcare, retail, logistics, and manufacturing, often supporting multi-year transformation programs that combine deep domain knowledge with forward-looking technology adoption. In every engagement, the focus remains on delivering measurable business impact through technology excellence and AI-informed engineering decisions rather than on technology for its own sake.\n\nBy anchoring AI capabilities within a strong foundation of software engineering and data expertise, ELEKS helps clients build sustainable, intelligent platforms that support evolving market needs and long-term digital competitiveness.\n\n## EffectiveSoft\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1771320943/EffectiveSoft_Logo_n0xg7c.png)\n\n* Company size: 250 - 499 professionals\n* Founded: 2003\n* Website: \n\nEnd-to-end AI Development and Intelligent Systems for Business Impact\n\nEffectiveSoft is a software engineering firm that helps organizations build AI-powered systems and data-centric applications designed to solve real business challenges. With a foundation rooted in analytical computing and bespoke technology solutions, the company combines AI technologies with core engineering practices to deliver products where intelligence is a functional part of the user experience and business logic.\n\nFrom its inception, EffectiveSoft has positioned itself at the intersection of software craftsmanship and applied intelligence, tailoring solutions that mix traditional engineering with advanced AI methodologies. The company’s AI development practice emphasizes the integration of machine learning, natural language processing (NLP), and predictive modeling into custom software architectures - enabling systems that can interpret complex inputs, automate decision processes, or generate actionable insights from large data sets.\n\nRather than offering AI as a theoretical add-on, EffectiveSoft approaches AI as a practical component of product delivery. Its teams work directly with clients to identify where AI can drive meaningful improvement - for example, by automating data classification, enhancing search and recommendation capabilities, or supporting contextual analysis within operational systems. This focus on purposeful AI adoption helps ensure that solutions are not only technically sound but also aligned with measurable business outcomes.\n\nEffectiveSoft’s competencies extend across a wide range of industries including finance, healthcare, telecommunications, logistics, and retail, where AI-enabled applications are leveraged to support processes such as risk evaluation, customer behavior prediction, anomaly detection, and semantic data processing. Projects commonly combine custom data engineering, algorithm enhancement, and tailored user interfaces to deliver systems that users can trust and scale.\n\nIn its delivery methodology, EffectiveSoft emphasizes collaboration with client teams throughout the project lifecycle - from initial requirements gathering to iterative development, testing, and deployment. This collaborative engineering process ensures that AI-related features are deeply integrated into the product’s architecture and remain adaptable as business needs evolve.\n\nBy combining broad software engineering experience with focused AI development services, EffectiveSoft supports clients in pursuing practical, intelligent digital transformation - building systems where AI contributes directly to solving domain-specific challenges and improving operational effectiveness.\n\n## Scalo\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1771321054/EwImMhfXIAMn9oZ_amszuy.jpg)\n\n* Company size: 500 - 999 professionals\n* Founded: 2007\n* Website: \n\nData-first software engineering and intelligent analytics solutions\n\nScalo is a software engineering company that focuses on building data-driven digital solutions and intelligent analytics platforms for organizations facing complex data challenges. The company combines deep expertise in data strategy, cloud engineering, and AI-related data processing to help clients transform large volumes of raw information into actionable, automated solutions that support business decisions and operational efficiency.\n\nRather than positioning itself solely as an AI vendor, Scalo emphasizes practical data science and intelligent automation within software systems - using analytical models, algorithmic logic, and data integration pipelines to deliver value where structured insights matter most. Scalo’s approach aligns with the evolving landscape of AI-driven engineering, where data quality, processing scalability, and algorithmic responsiveness are as critical as the models themselves.\n\nIn its delivery practice, Scalo works closely with clients to understand data flows, system behaviors, and business outcomes - ensuring that analytics and intelligent components are seamlessly embedded into core applications. This includes designing systems that can handle real-time data streams, orchestrate complex data flows, and support insight generation at scale without disrupting existing operations.\n\nScalo’s teams bring together engineers, data architects, and product strategists who collaborate with client stakeholders to shape solutions that not only manage large datasets but also incorporate intelligence layers - such as predictive analytics, trend analysis, and automated decision triggers - where they align with business logic. This positions data and intelligence as integrated product features, not external tools.\n\nThe company’s expertise spans industries where data complexity and intelligent behavior are key differentiators, including retail, finance, logistics, B2B platforms, and enterprise systems. Scalo supports both greenfield product development and modernization of existing systems that require improved insight extraction, performance optimization, and intelligent automation.\n\nBy centering its work on data engineering, analytics-enhanced software, and intelligent process support, Scalo helps clients realize digital platforms where intelligence is a functional part of everyday operations - enabling richer insights, automated responses, and scalable growth in data-intensive environments.\n\n## Itexus\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1771321318/image-4096x1209_betj5v.png)\n\n* Company size: 100 - 249 professionals\n* Founded: 2013\n* Website: \n\nAI-Enabled Product Engineering and Intelligent Digital Solutions\n\nItexus is a software engineering partner focused on delivering AI-enabled digital products and customized technology solutions for clients operating in fast-moving markets. With a strong engineering culture and a product-oriented mindset, Itexus works across the lifecycle of software development - from concept validation and MVP delivery to scalable implementations and long-term technical evolution.\n\nThe company highlights its emphasis on practical AI adoption within product workflows, integrating machine learning, natural language processing, and predictive capabilities into applications where they deliver measurable benefits. Instead of positioning AI as a theoretical add-on, Itexus applies AI technologies to enhance user experiences, automate key processes, and support intelligent decision support features inside digital systems.\n\nItexus’s engineering teams work with clients to define clear product goals, identifying areas where AI-related components can improve operational performance or unlock previously inaccessible insights. This includes use cases such as customer behavior prediction, contextual search, real-time data enrichment, and algorithm-assisted classification - features that become integral to how the software performs in production.\n\nIn its delivery approach, Itexus combines engineering rigor with an understanding of business value. Data science, software engineering, and UX expertise collaborate closely to ensure that AI-powered functionalities are robust, reliable, and aligned with user needs. This human-centric engineering process helps clients navigate the complexity of creating intelligent systems compatible with existing technology ecosystems.\n\nThe company’s portfolio spans industries such as fintech, healthcare, logistics, marketing technology, and enterprise applications, reflecting a broad set of domains where intelligent software plays a strategic role. Across these engagements, Itexus supports both foundational software architecture and AI-informed enhancements that help systems adapt, learn, and scale as requirements evolve.\n\nBy integrating data insights, machine learning components, and adaptive features with core application logic, Itexus supports the creation of digital products that are not just modern but meaningfully intelligent - meeting client needs in environments where AI-driven capabilities are increasingly expected.\n\n## Apptension\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1771321405/logo-full_bo7kvl.svg)\n\n* Company size: 50 - 99 professionals\n* Founded: 2012\n* Website: \n\nProduct-led digital transformation with intelligent features and UX focus\n\nApptension is a software product studio that helps organisations create human-centric digital products powered by modern technologies, including AI-enabled components that enhance interaction, automation, and insight capabilities. With a focus on blending thoughtful design and resilient engineering, Apptension partners with clients to build web, mobile, and hybrid solutions where intelligence plays a practical role in user outcomes.\n\nRather than presenting AI as a separate module, Apptension incorporates AI-related enhancements into existing product workflows, particularly in areas where automation and pattern recognition significantly improve user experience and system responsiveness. This includes intelligent recommendations, personalised content flows, and adaptive interfaces that respond to user context and behaviour.\n\nThe company emphasises a product-driven engineering culture, where cross-disciplinary teams of designers, developers, and product strategists collaborate to align technical decisions with real user needs. In practice, this often involves identifying where AI-assisted workflows could reduce friction, augment user engagement, or introduce smart predictions that maintain product clarity rather than complexity.\n\nApptension works with clients across a range of industries - such as fintech, digital health, consumer platforms, and SaaS applications - supporting everything from early-stage product experimentation to scaling mature digital systems. Through these engagements, AI-related features are introduced in ways that complement design thinking and software architecture, ensuring that intelligent capabilities have practical relevance and measurable impact.\n\nThe company’s approach is rooted in rapid prototyping, iterative delivery, and frequent feedback loops, enabling teams to test AI-enhanced interactions early and refine solutions based on real usage data. This iterative mindset supports the adoption of AI technologies that are aligned with product goals, user behaviour, and business priorities rather than speculative feature sets.\n\nBy infusing AI-enabled enhancements into product experiences, Apptension helps clients deliver digital solutions that feel intuitive, responsive, and tailored - reflecting a view of AI not as experimental tech, but as an intelligent extension of product design and development.\n\n## Instinctools\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1771321639/Zrzut_ekranu_2026-02-17_o_10.47.07_uzk8ty.png)\n\n* Company size: 250 - 499 professionals\n* Founded: 2000\n* Website: [](https://www.apptension.com/) \n\nIntelligent engineering for custom, data-forward digital systems\n\nInstinctools is a software engineering firm focused on delivering custom digital platforms and intelligent solutions for businesses navigating complex technical challenges. The company blends deep technical expertise with a clear emphasis on data processing, smart automation, and emerging AI technologies - positioning its services within the evolving landscape of AI-driven software development.\n\nRather than presenting AI as a standalone offering, Instinctools embeds intelligent capabilities into core software systems where they make meaningful, measurable contributions. This includes leveraging data analytics, pattern recognition, and automation in enterprise applications to support operational efficiency and improve the way systems react to real-world inputs. AI-enabled elements are introduced as part of broader engineering frameworks designed for performance, scalability, and reliability.\n\nInstinctools works closely with client organisations to understand their business context, data dependencies, and technical constraints, enabling teams to define where intelligent features can drive tangible value. From enhancing backend systems with predictive features to integrating decision-support functions into workflows, Instinctools applies AI technologies in a targeted and practical way, rather than as experimental novelties.\n\nThe company brings together multidisciplinary teams - including software engineers, system architects, and domain specialists - who collaborate with stakeholders at every stage of development. This collaborative model ensures that AI-related capabilities are thoughtfully designed, technically viable, and aligned with long-term product goals rather than isolated engineering experiments.\n\nInstinctools’ expertise spans industries such as manufacturing, healthcare, finance, logistics, and enterprise solutions, where intelligent automation and data-derived insights offer competitive advantage. Many engagements focus on modernising legacy systems, implementing complex integrations, and building architectures that support both current operational needs and future technological evolution.\n\nBy integrating data-oriented intelligence and adaptive automation into custom software, Instinctools helps clients develop platforms that are not only technically robust but also responsive to dynamic business environments - reflecting a practical and sustainable approach to AI-driven digital transformation.\n\n## Alltegrio\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1771321480/rmoaiaajbv5sodlxqgpu_ba4nmn.avif)\n\n* Company size: 100 - 249 professionals\n* Founded: 2012\n* Website: [](https://www.apptension.com/) \n\nIntelligent, data-driven development with a focus on AI-powered software solutions\n\nAlltegrio is a technology consulting and software engineering company that collaborates with businesses to design, build, and scale AI-driven digital products and intelligent systems. With expertise spanning data science, machine learning, and custom software development, Alltegrio helps organizations integrate intelligent features that elevate user experience, automate complex processes, and derive actionable insights from data.\n\nAt its core, Alltegrio embeds AI technologies into bespoke software solutions, aligning advanced analytics and predictive capabilities with real business goals. The company’s approach emphasizes the practical application of machine learning models, natural language processing, and advanced data workflows as part of everyday product behavior - rather than as isolated experiments or proof-of-concepts.\n\nIn practice, Alltegrio collaborates closely with client teams to identify where AI can support automation, enrich product interactions, or enable smarter decision-making within digital platforms. This includes integrating machine learning components to enhance recommendation systems, support contextual search, automate content classification, or enable adaptive user interfaces that learn from behavior over time.\n\nAlltegrio’s delivery model blends cross-functional engineering expertise with strategic alignment to business outcomes. Teams work through iterative development cycles, using data-informed roadmaps and analytics feedback loops to refine intelligent features and ensure alignment with evolving product priorities. This approach supports not only the creation of AI-enhanced applications but also long-term product evolution in rapidly changing markets.\n\nThe company’s portfolio spans industries such as fintech, e-commerce, healthcare, and SaaS platforms, where AI-powered software capabilities are increasingly expected. Across these projects, Alltegrio configures data pipelines, builds machine learning-enabled modules, and implements scalable architectures that support both performance and flexibility.\n\nBy integrating AI technologies directly into software workflows and product logic, Alltegrio helps clients transition from traditional application development to data-driven, intelligent systems that support efficiency, user value, and competitive differentiation in digital markets.\n\n## FAQ: Top 10 AI-Driven Software Companies in Poland\n\n**1. What does “AI-driven software company” mean in this ranking?**\n\nIn this article, an AI-driven software company is defined as an organization that actively integrates AI technologies into software development or digital products. This includes embedding AI-enabled features into applications, using data-driven and intelligent system components, or applying AI-assisted engineering practices that support scalability, automation, and advanced analytics. The ranking focuses on practical AI adoption rather than experimental or research-only use cases.\n\n**2. Are all companies in the ranking building standalone AI products?**\n\nNo. The companies featured in this ranking apply AI in different ways. Some specialize in AI-enabled platforms and intelligent systems, while others integrate AI into core software architectures, data pipelines, or product functionality. The ranking includes firms that use AI to enhance real-world software solutions, not only those building dedicated AI or machine-learning products.\n\n**3. Why is Poland considered a strong hub for AI-driven software development?**\n\nPoland has become one of the leading technology hubs in Europe due to its strong software engineering talent pool, mature outsourcing market, and growing adoption of AI technologies in digital products. Many Polish software companies combine deep engineering expertise with practical AI integration, supporting clients across Europe and global markets in building scalable, data-driven, and intelligent systems."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1758012658/Frame-3_qfvti0.png","lead":"**Artificial Intelligence** has become a key driver of modern software development. Today, AI-assisted coding, AI-powered development tools, and human-AI collaboration are reshaping how digital products are designed, built, and scaled - improving code quality, accelerating delivery, and streamlining workflows across engineering teams.\n\nPoland has established itself as **one of Europe’s strongest technology hubs**, recognized for its highly skilled software engineers and a mature software development ecosystem. Companies serving the Polish market increasingly integrate AI into daily coding practices and delivery workflows, using AI technologies to boost productivity, automate repetitive tasks, and support more efficient software engineering processes.\n\nThis ranking presents the **Top 10 AI-Driven Software Companies Serving the Polish Market**, based on DesignRush’s Artificial Intelligence Company Rankings. It includes both Polish software companies and international engineering firms actively delivering projects within Poland and the broader Central and Eastern European region. The list highlights trusted software partners that successfully combine technical expertise, AI-enabled development practices, and proven delivery capabilities for clients across European and global markets.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-12-01T09:15:31.199Z","slug":"top-ai-driven-software-companies-2026","type":"blog","slugType":null,"category":null,"additionalCategories":["GenAI","Ideas","News","Tech"],"url":null},"author":"Roksana Kaczmarska","authorAdditional":"","box":{"content":{"title":"Top 10 AI-Driven Software Companies in 2026","tileDescription":"Discover the top AI-driven software companies shaping 2026. Explore innovative vendors delivering machine learning, data, and intelligent product solutions.","coverImage":""},"coverImage":null}},"id":"4cb3b9e5-00d1-57e9-a6be-aac919faee65"}},{"node":{"excerpt":"","fields":{"slug":"/blog/the-last-6-months-changed-how-i-work-as-a-product-designer-and-i-think-more-designers-need-to-hear-this/"},"frontmatter":{"title":"This week’s AI Bite: The Last 6 Months Changed How I Work as a Product Designer ","order":null,"content":[{"body":"## How It All Started: A Small “What If” That Changed My Direction\n\nOne day, during a project, someone asked: “Why don’t we try prototyping this directly in Cursor?\" \n\nHonestly, I laughed.\n\nCursor? \n\nMe? \n\nCoding?\n\nSounded like chaos.\n\n**But I tried it. And that moment – opening Cursor for “just a quick experiment” – was the turning point that pulled me into a completely new way of working.**\n\nOne that feels natural, faster, and honestly, more fun.\n\n## Vibe Coding: A Joke Term That Accidentally Describes the Future\n\nYes, “vibe coding” started as a meme on Twitter. But the reality behind it is actually quite serious.\n\nIt’s the idea that designers and developers now collaborate in a space where:\n\n* AI helps write the code\n\n prototypes become functional much earlier\n\n ideas feel real within hours\n\n the gap between design and development gets smaller\n\n and iteration becomes insanely fast.\n*\n\nThe first time you do it, it feels strange.\n\nThe second time, it feels obvious.\n\nYou realize: This isn’t just a trend – this is the next phase of product design.\n\n## **Cursor + Figma MCP: The Combo That Rewired Our Process at Boldare**\n\nThis is where everything clicked for me.\n\nAt Boldare, we started using Cursor + Figma MCP, and suddenly:\n\n* Figma screens became functional prototypes\n* flows were testable, not just viewable\n* clients could try the product before development even started and feedback arrived days – not weeks – earlier\n\n![Picture Cursor + Figma MCP ](https://res.cloudinary.com/de4rvmslk/image/upload/v1763977400/Insta_ozmarl.png \"Cursor + Figma MCP \")\n\nWe weren’t just designing anymore.\n\nWe were simulating the real experience.\n\nThe biggest benefit? Clients finally understand the product not through “pretty screens,” but through actual interactions.\n\nOnce you taste this workflow, going back feels impossible.\n\n## **Figma Make & Lovable: These Tools Are Not Just “For Fun” Anymore**\n\nAt first, I used Make and Lovable just to speed up small tasks – quick layouts, landing pages, experimenting with flows. But something changed.\n\n**[I’ve been following Anton Osika, CEO of Lovable, and almost every week he shares another insane story.](https://www.linkedin.com/posts/antonosika_17m-was-just-raised-by-a-startup-built-activity-7391217122661703680-SFVI?utm_source=share&utm_medium=member_desktop&rcm=ACoAAChLirYBNJmQ3JTFgcyiAm5NnAToWk5WSlE)**\n\nJust recently, a startup called Startdust, built entirely with Lovable, raised $1.7M.\n\n[A month before that, two Swedish founders built a startup with Lovable that reached €700k ARR in 9 months.](https://www.linkedin.com/posts/antonosika_two-swedish-founders-built-a-startup-with-activity-7380995329405464576-ACqX?utm_source=share&utm_medium=member_desktop&rcm=ACoAAChLirYBNJmQ3JTFgcyiAm5NnAToWk5WSlE)\n\n![Picture Figma Make & Lovable – not for fun anymore](https://res.cloudinary.com/de4rvmslk/image/upload/v1763977408/Insta_gahasa.png \"Figma Make & Lovable – not for fun anymore\")\n\nLet me repeat that:\n\n* No traditional dev team\n* No long development cycles\n* Built in Lovable\n* Raised money\n* Reached real revenue\n\n Became real businesses\n\n\n\nThis is when it hit me:\n\nLovable, [Figma Make](figma.com/make/), [Cursor](https://cursor.com/agents) – they’re not just tools for “vibe coding.” They’re tools that help you build real products. Potentially real companies.\n\nThis is life-changing for designers who are willing to explore these tools.\n\nBecause suddenly we’re not just visualizing ideas – we’re building them.\n\n## **Designers Are Becoming Hybrid Creators (And It’s Not Something to Fear)**\n\nThis is the part I want more designers to hear:\n\nWe’re no longer “just designers”. Our role is evolving into something broader – a blend of designer, prototyper, interaction builder, and even an occasional lightweight front-end problem solver. More importantly, we’re becoming a true bridge between the idea and the implementation.\n\nThis shift doesn’t take anything away from developers – it actually strengthens collaboration. When designers can prototype logic, interactions, and basic functionality, communication becomes clearer, handoff becomes smoother, misunderstandings fade away, and teams ultimately ship faster.\n\nThis shift isn’t scary – it’s empowering.\n\nYou have more control over the product and less waiting time.\n\n## **If You’re a Designer Reading This: Please Try New Tools**\n\nI’m not telling you to stop using Figma.\n\n\n\nI’m telling you:\n\n**Stop using only Figma.**\n\nBecause the designers who explore these new tools are the ones who will:\n\n* ship faster\n* communicate better\n* collaborate better with dev teams\n* understand product logic more deeply\n* and ultimately influence the final product much more\n*\n\nThe last 6 months taught me this clearly:\n\nThe future of product design belongs to designers who are not afraid to prototype, to explore, to experiment – and to step a little closer to the world of development.\n\n![Use new tools](https://res.cloudinary.com/de4rvmslk/image/upload/v1763977419/Insta_hadzx5.png \"If You’re a Designer Reading This: Please Try New Tools\")\n\nNot to replace anyone.\n\nBut to create better products, faster, with more clarity and more fun.\n\nIt’s not just vibe-coding.\n\nIt’s a new way of working.\n\nAnd it’s already changing careers.\n\n*"}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1763977320/Group_1000005033_wcy4ma.png","lead":"Six months ago, [I wrote an article about the tools I use as a Product Designer.](https://www.boldare.com/blog/tools-influencing-my-product-design-work-beyond-figma-and-ai/) Back then, everything felt stable: Figma for design, FigJam for workshops, Notion for structure, plus a few AI helpers to speed things up.\n\nNothing unusual.\n\nNothing groundbreaking.\n\nA comfortable routine.\n\nBut in the last 6 months, everything changed for me: my workflow, my mindset, even how I see the role of a product designer. And I want to talk about it, because I truly believe more designers should explore what’s happening right now.\n\n**If we keep working the same way we did in 2023, we’ll slowly fall behind.** \n\nThis isn't a drama.\n\nThis is simply where the industry is moving.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-11-24T09:32:22.245Z","slug":"the-last-6-months-changed-how-i-work-as-a-product-designer","type":"blog","slugType":null,"category":null,"additionalCategories":["GenAI"],"url":null},"author":"Gleb Sapronov","authorAdditional":"","box":{"content":{"title":"How the Last 6 Months Transformed My Work as a Product Designer","tileDescription":"Discover how new AI-powered tools like Cursor, Figma MCP, and Lovable reshaped my product design workflow – and why designers should embrace this shift.","coverImage":""},"coverImage":null}},"id":"32ff993c-b929-5783-8945-7f0b3793bea5"}},{"node":{"excerpt":"","fields":{"slug":"/blog/boldare-tracks-the-latest-ai-trends-insights-from-the-ai-driven-marketing-conference/"},"frontmatter":{"title":"Boldare tracks the latest AI trends: insights from the AI Driven Marketing conference","order":null,"content":[{"body":"## AI in marketing: a revolution at every turn\n\nAt the conference, we delved into the latest trends and applications of AI in marketing and sales. One of the key topics was the role of artificial intelligence in transforming SEO and content marketing processes. In the \"AI in SEO\" session, we discussed how AI-powered technologies influence search engine algorithms, allowing for better content optimization based on user queries. Presentations also focused on data analysis tools that enable the creation of effective marketing strategies, grounded in consumer behavior insights. We also participated in discussions on building modern marketing teams that must leverage AI tools to automate processes and improve campaign targeting.\n\n## AI security and practical applications\n\nSecurity in the context of AI was one of the key topics discussed at the conference, which particularly caught our attention. We explored the challenges associated with implementing AI in organizations and the need for proper data protection standards. In our work, we emphasize the wisdom and responsibility of using new technologies – both internally and in our advisory services to clients. \n\n\n\n![Graphic with the title ‘Top AI Tools Marketers Should Know in 2025’](https://res.cloudinary.com/de4rvmslk/image/upload/v1763976295/Marketingowe_toole_AI_przegla%CC%A8d_2_ykefvt.heic \"Top AI tools marketers should know in 2025\")\n\nWe were also inspired by the approach to measuring the effectiveness of marketing activities in the age of AI. Thanks to advanced analytical tools like GA4, companies can closely track how their actions influence brand visibility online and customer interactions. This approach enables faster responses and better real-time strategy adjustments, giving companies a competitive edge in the fast-paced world of marketing.\n\n![Graphic illustrating GA4 settings with the headline ‘How to Set Up GA4 to Track Traffic Coming from LLM Models?](https://res.cloudinary.com/de4rvmslk/image/upload/v1763976295/LLM_Jako_z%CC%81rod%C5%82o_GA4_2_hysnog.heic \"How to set up GA4 to track traffic coming from LLM models?\")\n\n\n\n## Innovations and industry insights\n\nThe event brought forth numerous fresh ideas that have inspired us to continue developing. One particularly interesting presentation was the discussion on shifting from the traditional sales funnel to a \"chaotic\" model that better reflects the new consumer habits. The use of tools like Performance Max was discussed as a way to better understand user behavior, especially as consumers increasingly combine multiple online channels when making purchasing decisions.\n\n## Conference speakers and industry guests\n\nAt the conference, we had the opportunity to meet experts from companies such as[ Krispol](https://krishome.pl/), [Blachy Pruszyński](https://pruszynski.com.pl/), [Medicover](https://www.medicover.pl/),[ Wakacje.pl,](https://www.wakacje.pl/)[ LINK4](https://www.link4.pl/), [L'Oréal](https://www.loreal.com/en/), [Runmageddon](https://www.runmageddon.pl/), and many others. It was a great chance to see how different organizations are implementing AI in their operations and the challenges they face. The event allowed us to establish valuable connections and exchange experiences with industry leaders who are working on innovative solutions in marketing and sales.\n\n## Turning insights into innovation: our post-conference reflections\n\nWhile many of the topics discussed were already familiar to us, the conference provided fresh perspectives on how to leverage AI even more effectively in our work. We left the event inspired and full of new ideas, which we are already implementing to deliver even more valuable and innovative solutions to our clients. We're grateful for the opportunity to attend and connect with industry leaders. We look forward to future events that will continue to shape our AI and marketing strategy."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1763978472/Zrzut_ekranu_2025-11-24_o_11.01.01_xss6rz.png","lead":"In October 2025, we had the pleasure of attending the AI Driven Marketing conference in Warsaw. This event, filled with inspiration and expert knowledge, provided us with an excellent opportunity to deepen our understanding of how artificial intelligence is transforming marketing. As a company committed to continuous growth and innovation, participating in such an event was crucial, especially since AI has become the cornerstone of our work – both in developing digital products for our clients and in our consulting services.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-11-24T08:19:37.937Z","slug":"Boldare-tracks-the-latest-AI-trends","type":"blog","slugType":null,"category":null,"additionalCategories":["News"],"url":null},"author":"Magdalena Chmiel","authorAdditional":"","box":{"content":{"title":"AI Driven Marketing 2025 – Boldare stays ahead of trends","tileDescription":"Boldare attended the AI Driven Marketing 2025 conference, gaining fresh perspectives that help us keep pace with emerging trends and apply AI-driven innovation for our clients.","coverImage":"https://res.cloudinary.com/de4rvmslk/image/upload/v1763978472/Zrzut_ekranu_2025-11-24_o_11.01.01_xss6rz.png"},"coverImage":null}},"id":"831003dc-6d9b-5bab-a665-ca358f6d16e2"}},{"node":{"excerpt":"","fields":{"slug":"/blog/this-week-s-claude-code-at-boldare-shared-workflows-automations-and-best-practices/"},"frontmatter":{"title":"This week’s AI Bite: Claude code at Boldare, shared workflows, automations, and best practices","order":null,"content":[{"body":"At Boldare, working with AI is not an add-on but a natural part of everyday processes. That’s why every new feature that improves collaboration across teams and speeds up delivery quickly becomes part of the company’s internal ecosystem. One of the latest developments is the ability to share custom workflows, prompts, automations, and best practices within Claude Code.\n\nInstead of storing these assets locally in individual projects, Boldare is creating a shared, central space that allows teams to build on what already works and continue improving it together.\n\n## New: The Boldare claude code marketplace\n\nTo support knowledge exchange and the creation of repeatable practices, Boldare has established a private, company-only repository accessible exclusively to Bolders. The marketplace acts as a catalogue of AI-supported workflows and automations that enhance daily work, streamline development processes, and structure collaboration between humans and AI.\n\nIt is a space where Bolders can add their own skills and workflows, install ready-to-use automations, test and iterate on existing solutions, and explore how AI can support everyday tasks. The marketplace will continue to grow alongside ongoing projects and the experience of the teams.\n\n## What’s available at launch\n\nThe marketplace already includes initial workflows and automations that support development processes, improve code quality, increase efficiency, and facilitate smoother AI-Human collaboration. Some of these tools also help automate cooperation within client environments, making day-to-day project work faster, more consistent, and easier to maintain. This is only the beginning — more elements will be added based on project needs.\n\n## Co-creation as a foundation\n\nThe marketplace was created not only to simplify daily work but also to reduce duplicated effort, streamline onboarding, and unify the company’s approach to AI adoption. It provides a space to share solutions that deliver real value across projects, where each added skill has the potential to support many people at once — turning individual work into a reusable asset. Here, teams can test existing workflows, report issues, contribute new automations, and seek configuration support, jointly developing tools that become part of Boldare’s internal AI ecosystem.\n\n## Summary\n\nBoldare continues to refine its organization-wide approach to working with AI, and the Claude Code marketplace marks another step toward more effective, shared, and thoughtful collaboration. It is a tool that supports teams in their daily responsibilities and demonstrates how AI and the expertise of Bolders can complement each other in delivering high-quality digital products."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1763382627/Group_1000005033_pwjbx4.png","lead":"**Weekly AI Bites is a series that gives you direct access to what’s happening in our day-to-day AI work.** Every post comes straight from our team’s meetings and Slack, sharing insights, tests, and experiences we’re actively applying to real projects. \n\nWhat models are we testing, what challenges are we tackling, and what’s really working in products? You’ll find all of this in our bites. Want to know what’s buzzing in AI? **Check out Boldare’s channels every Monday for the latest weekly AI bite.**\n\n**Boldare treats AI as an integral part of daily processes, not an add-on. A recent feature allows teams to share custom workflows, automations, and best practices within Claude Code. Instead of storing these resources locally in individual projects, the company creates a central space where teams can build on proven solutions and improve them together.**\n\nThis article highlights how Boldare integrates AI seamlessly into its daily work environment, enhancing collaboration and enabling continuous improvement across teams.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-11-17T14:45:19.548Z","slug":"this-weeks-claude-code-at-boldare-shared-workflows-automations-and-best-practices","type":"blog","slugType":null,"category":null,"additionalCategories":["Ideas","GenAI","How to","Tech"],"url":null},"author":"Karol Kasprzak","authorAdditional":"","box":{"content":{"title":"This Week’s: Claude Code at Boldare – Shared Workflows, Automations, and Best Practices","tileDescription":"Discover how Boldare is enhancing collaboration with Claude Code, a platform for sharing custom workflows, automations, and best practices, allowing teams to build on proven solutions and improve together.","coverImage":""},"coverImage":null}},"id":"5631bf8d-5a77-5d7e-9b9a-f661d181db5f"}},{"node":{"excerpt":"","fields":{"slug":"/blog/this-week-s-ai-bite-ai-in-practice-claude-code-from-a-java-developer-s-perspective/"},"frontmatter":{"title":"This week’s AI Bite: AI in Practice: Claude Code from a Java Developer’s Perspective","order":null,"content":[{"body":"## Seamless integration with the existing development environment\n\nOne of my main concerns before testing AI coding assistants was the need to switch IDEs.\n\nI’ll be honest – VS Code isn’t my go-to editor, and most new tools (like Cursor) are built around it. Claude Code, however, works without forcing me to abandon my current setup, which, for a Java developer, is a huge plus. It lets me leverage AI support while staying fully immersed in my natural development flow.\n\n## Quick setup with the init command\n\nMy very first interaction with the tool was a pleasant surprise.\\\nThe init command allows you to easily initialize and configure the environment for a specific project. Thanks to that, I was able to quickly set up Claude for our context – including internal tools and libraries.\n\nThis feature is especially useful in larger projects, where consistent configuration can make onboarding and day-to-day development significantly smoother.\n\n## “Rules” – a step toward team-wide consistency\n\nOne of the most interesting features is the concept of rules, which can be defined globally, per project, or locally. This allows teams to establish consistent standards for how they interact with Claude, for example, preferred coding styles, frameworks, or testing approaches.\n\nI haven’t fully explored this feature yet, but I can already see huge potential in building shared practices and improving team alignment.\n\n## Everyday experience\n\nCompared to Copilot Agent, working with Claude Code feels much more intuitive. The interface, interaction model, and overall UX are simply better thought out. At first, collaboration with the tool was a bit clunky, but after learning some best practices, communication became smooth and natural.\n\nClaude handles project context well, maintains coherence across files, and suggests meaningful improvements, especially when refactoring or analyzing existing code.\n\n## Be careful with tests, AI still needs direction\n\nOne area where I noticed some weaknesses is unit testing.\\\nClaude Code tends to test the implementation rather than the business logic. In one instance, suppressed exceptions led to misleading test suggestions.\\\nOnce those exceptions were restored, the entire test suite started failing.\n\nThis example shows that, despite AI’s growing capabilities, developer oversight is still essential. Claude is an excellent assistant, but it can’t (yet) replace critical thinking.\n\n## Great potential for Event Sourcing projects\n\nGiven our focus on Event Sourcing, I see tremendous potential in using Claude Code for this kind of architecture. It could be particularly helpful for onboarding developers into complex domains, where understanding event flows and business context is key. I haven’t fully tested this scenario yet, but I can already see how it could shorten onboarding time and simplify exploration of existing implementations.\n\n## Final thoughts\n\nAt this stage, I’m probably using no more than 20% of Claude Code’s potential, but even that has been enough to convince me that it’s one of the most promising AI tools for developers. It doesn’t require switching environments, offers smart configuration options, and naturally integrates into team workflows. From my perspective it’s absolutely worth trying."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1763128644/Group_1000005028_mpazet.png","lead":"**Weekly AI Bites is a series that gives you direct access to what’s happening in our day-to-day AI work.** Every post comes straight from our team’s meetings and Slack, sharing insights, tests, and experiences we’re actively applying to real projects. \n\nWhat models are we testing, what challenges are we tackling, and what’s really working in products? You’ll find all of this in our bites. Want to know what’s buzzing in AI? **Check out Boldare’s channels every Monday for the latest weekly AI bite.**\\\n\\\nIn this article, our Java developer shares their experience working with **Claude Code** over the past few weeks, offering insights from a Java-focused perspective. In a field where complex projects, multi-layered domains, and intricate business processes dominate, any tool designed to support daily development must genuinely prove its value. And according to our developer, **Claude Code** has been a very positive surprise.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-11-14T13:46:57.171Z","slug":"ai-in-practice-claude-code-java-developers-perspective","type":"blog","slugType":null,"category":null,"additionalCategories":["How to","GenAI","Tech"],"url":null},"author":"Roksana Kaczmarska","authorAdditional":"","box":{"content":{"title":"AI in Practice: Claude Code from a Java Developer’s Perspective","tileDescription":"This week’s AI Bite explores Claude Code from a Java developer’s perspective, highlighting its seamless integration, quick setup, and AI-driven features that enhance daily development workflows. Discover how Claude Code is transforming development practices.","coverImage":""},"coverImage":null}},"id":"b8875641-bc08-55b5-9f58-c9d0f4e75eb1"}},{"node":{"excerpt":"","fields":{"slug":"/blog/boldare-at-leaddev-berlin-2025-ai-enhanced-leadership-and-the-next-chapter-of-engineering-culture/"},"frontmatter":{"title":"Boldare at LeadDev Berlin 2025 - AI-enhanced leadership and the next chapter of engineering culture","order":null,"content":[{"body":"## AI as an amplifier, not a revolution\n\nOne of the strongest messages that resonated throughout the event was that AI doesn’t replace people, it amplifies systems. As shared in the State of AI-Assisted Software Development 2025 report:\n\n> AI amplifies what already exists. In well-organized teams, it accelerates flow and quality. In struggling teams, it magnifies dysfunction.\n\nThis perspective mirrors our own experience at Boldare. AI adoption alone doesn’t transform organizations – AI-enhanced systems do. When supported by solid communication, feedback loops, and a healthy engineering culture, AI becomes a catalyst for smarter, faster, and more consistent delivery.\n\n## “20–30% of our code is written by AI”\n\nDuring one keynote, a quote from Satya Nadella (Microsoft CEO) captured everyone’s attention:\n\n> I’d say maybe 20% to 30% of the code that is inside of our repos today are probably all written by software \\[AI].\n\nThis isn’t a prediction – it’s reality. AI now contributes meaningfully to production-level codebases. At Boldare, we’re also seeing how AI-assisted coding changes the developer experience: reducing cognitive load, supporting review processes, and freeing space for creative problem-solving.\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1763126694/IMG_8529_ftbzm6.jpg)\n\n## The H.O.L.D. framework – a mindset for AI-enhanced decision-making\n\nOne of the most insightful sessions introduced the H.O.L.D. framework – a model for responsible collaboration with AI systems:\n\n* Halt – Pause with intention\n* Observe – Verify before you trust\n* Loop back – Synthesize and calibrate\n* Deliberate – Reflect and transfer learning\n\nWe loved how this model blends perfectly with our own product mindset:\\\nexperiment, reflect, adapt. AI tools can accelerate delivery, but only when combined with human judgment and deliberate learning cycles.\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1763126694/IMG_8396_iylkal.jpg)\n\n## From “taming hallucinations” to building trust through feedback\n\nAnother memorable talk explored the challenge of AI hallucinations – and how to ensure consistent value through feedback-driven learning loops.\\\nBy connecting user feedback and system observations directly into LLM pipelines, teams can move from “occasional brilliance” to continuous reliability.\n\nAt Boldare, we’re exploring similar ideas in our AI-enhanced engineering workflows – treating models not as code generators, but as context partners that help us think better, design clearer, and learn faster.\n\n## 5 ideas that stuck with us\n\n1. **Fail cheaply. Learn fast. Build smarter.** Small, inexpensive experiments > big, risky bets.\n2. **Clarity scales better than control.** Clear goals and shared understanding scale, micromanagement doesn’t.\n3. **Architects don’t leave chaos** – they leave clarity. Architecture is not documentation; it’s decision visibility.\n4. **Prompts are the new design docs**. The way we ask shapes what we build.\n5. **Broadcast decisions. Build alignment.** Deliver impact. Sharing unfinished thinking helps build momentum and shared clarity.\n\nLeadDev Berlin reminded us that technology evolves, but principles stay the same. Great engineering still comes down to clarity, curiosity, and collaboration — now amplified by AI.\n\nFor us at [Boldare](https://www.boldare.com/blog/), this was more than a conference. It was a confirmation that AI-enhanced leadership isn’t about tools — it’s about people who know how to combine insight, experimentation, and empathy at scale.\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1763126694/IMG_8385_vlmop0.jpg)"}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1763126694/IMG_8448_oahc3n.jpg","lead":"Our team joined hundreds of engineering leaders, architects, and developers at **[LeadDev Berlin 2025](https://leaddev.com/leaddev-berlin/)**,  one of the most influential conferences on technical leadership and software architecture.\n\nThis year’s theme was clear: **AI is no longer a tool. It’s a teammate.**","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-11-10T10:35:08.597Z","slug":"boldare-leaddev-berlin-2025-ai-leadership-engineering-culture","type":"blog","slugType":null,"category":null,"additionalCategories":["News","Ideas","Future","Tech"],"url":null},"author":"Roksana Kaczmarska","authorAdditional":"","box":{"content":{"title":"Boldare at LeadDev Berlin 2025 - AI-enhanced leadership and the next chapter of engineering culture","tileDescription":"Our Boldare team attended LeadDev Berlin 2025 to explore AI-enhanced leadership and engineering culture. Discover insights on AI as a teammate, the H.O.L.D. framework, and how AI amplifies collaboration and productivity.\n","coverImage":""},"coverImage":null}},"id":"28990b9f-e88c-5931-a0c9-99b7e3c2c211"}},{"node":{"excerpt":"","fields":{"slug":"/blog/service-as-a-software-an-executive-guide-to-scaling-business-without-hiring-more-people/"},"frontmatter":{"title":"Service-as-a-Software: an executive guide to scaling business without hiring more people","order":null,"content":[{"body":"Service-as-a-Software provides a scalable solution by shifting service delivery from human expertise to automation and software-driven processes. But before we dive deeper into the definition, let us grab your attention. Do you face challenges such as:\n\n* How to scale a service without increasing headcount?\n\n How to ensure compliance without monthly audits?\n\n How to reduce operational costs in back-office processes?\n\n How to turn consulting into a digital product?\n\nIf yes, then **Service-as-a-Software may be the breakthrough you're seeking.** In this guide, we’ll explore the concept in detail, why it’s a transformative model for scaling, and how it works in practice. \n\nThis isn’t a mistake in naming – until now, we’ve all been familiar with Software-as-a-Service (SaaS). **But Service-as-a-Software represents a new evolution of that model,** shifting the focus from just providing tools to delivering complete automated services. \n\n## The scaling problem: why service models don’t scale?\n\nConsider this common scenario:\n\n> Your company is growing, but with each new client, you find yourself needing to hire more people. Your margins stay flat, and complexity piles up. Processes become harder to manage, and service delivery is increasingly difficult to maintain at the quality and speed that customers expect.\n\nIn this environment, the traditional service model simply doesn’t scale. For service businesses, the solution has typically been to add more staff as demand grows. But this approach introduces more overhead, more complexity, and more inefficiencies. And while hiring is a quick fix, it doesn’t necessarily make the business more efficient or more profitable in the long run. \n\nIf any of this resonates, it may be time to rethink your approach. **The core of the problem lies in the service model itself. When scaling a service, it’s difficult to overcome the inherent limitations** – growing by simply adding people doesn’t solve the deeper issues of inconsistency, rising costs, or the need for constant human intervention.\n\n## When is Service-as-a-Software better than more traditional solutions like outsourcing?\n\n### Outsourcing: a temporary solution\n\nOutsourcing can provide immediate relief by shifting the burden to a third party, but it doesn’t solve the core issue. You still rely on external teams to provide services, which means quality control becomes more difficult, and it remains expensive to scale. Over time, outsourcing only passes the problem along rather than addressing the root cause. Of course, there are situations where outsourcing can be a great solution –[ read more about these scenarios in our blog post.](https://www.boldare.com/blog/software-development-outsourcing-everything-you-should-know/)\n\n### Process automation: a step forward, but not enough\n\nAutomation can improve efficiency by handling repetitive tasks, but it doesn’t fundamentally address the scalability issue. Although automating specific tasks can streamline operations, the underlying service delivery remains dependent on human involvement at some level, which limits scalability.\n\n### Hiring: a costly approach\n\nIncreasing headcount is a natural response to scaling, but it’s not a sustainable one. Hiring more people adds more costs and management overhead without necessarily increasing the efficiency of your processes. It’s a linear solution, and with each new hire, the complexity increases.\n\n### Traditional SaaS: providing tools, not the service\n\nSoftware-as-a-Service (SaaS) is an excellent solution for automating certain tasks, but it doesn’t address the core challenge of transforming the service model. Traditional SaaS provides users with tools to perform tasks themselves, but it doesn’t remove human intervention from the process. The service itself still depends on human expertise to be executed effectively.\n\nUltimately,**the issue isn’t the process, it’s the model.** Scaling a service business with human intervention will always have its limits, and no matter how much you optimize, the traditional model remains inefficient.\n\n## Back to basics: what is Service-as-a-Software?\n\nWe hope you have a better understanding of when your company might need Service-as-a-Software. Let’s now focus on the definition. \n\n**What is Service-as-a-Software?** We could say it’s a logical evolution of Software-as-a-Service (SaaS), but it goes a step further. \n\n**In this model, rather than simply providing a tool for customers to use, the software itself becomes the service. This approach automates entire service processes, from decision-making to task execution, without the need for human intervention.** \n\n**Traditional SaaS:** A platform or application is provided to the user in a subscription model. The user performs the work themselves by interacting with the software. \n\n**Service-as-a-Software:** The software itself takes over the tasks, executing decisions and processes autonomously. The customer pays for the outcome (the service result), not just for access to the tool. \n\nIn essence, the software operates as a **“service agent”** that takes care of tasks and decisions for the client, eliminating the need for manual input. **With advances in AI, automation, and integrated systems, this model is now more feasible than ever.** At its core, Service-as-a-Software offers a seamless, fully automated service that can scale without the need for more employees, ensuring both efficiency and consistent quality.\n\n## The benefits of Service-as-a-Software: how it revolutionizes service delivery?\n\nImagine a world where your service is always available, running smoothly around the clock, delivering the same high-quality experience to every customer, and scaling without the need for additional hires. This is the reality that Service-as-a-Software offers.\n\nLet's look at the key benefits this model can bring to your business:\n\n#### **24/7 availability**:\n\nOne of the most significant advantages of Service-as-a-Software is the ability to offer a service that runs continuously, without needing human intervention. Think about the opportunities this opens up: no more waiting for business hours, no more “we’re closed for the day” messages. \n\n#### **Cost reduction**:\n\nTraditional service models often rely heavily on human labor, **but with Service-as-a-Software, many tasks can be automated,** drastically reducing the need for additional staff. \n\n#### **Consistent quality**:\n\nIn a human-driven service model, **quality can vary depending on who is performing the task**. With automation, however, every customer receives the same consistent service, regardless of who is involved in the process. \n\n#### **Scalability**:\n\nAs your business grows, so do the demands. **Traditionally, this growth would mean hiring more people.** However, with Service-as-a-Software, scaling is seamless. You can expand into new markets or service lines without needing to increase your workforce. \n\n#### **Predictable costs**:\n\nOne of the most common challenges when scaling a business is understanding and managing costs. With traditional models, expenses often rise unpredictably as the business grows. However, Service-as-a-Software offers clear, predictable pricing structures that make budgeting and financial forecasting easier. \n\n#### **Measurable ROI**:\n\nWith Service-as-a-Software, **you can track exactly how much value the service is delivering.** Outcome-based pricing models—where customers pay for the results or outcomes of the service rather than access to the software—allow businesses to calculate ROI easily and accurately. Imagine a company that automates its lead generation process. The time saved, reduced costs, and increase in generated leads are all quantifiable, giving executives clear insights into the return on their investment.\n\nThese benefits make **Service-as-a-Software** not just a technological upgrade, **but a strategic game-changer.** By shifting the focus from human-dependent processes to software-driven services, businesses can unlock new levels of efficiency, scalability, and profitability. \n\nThe power to scale without adding overhead, reduce costs while maintaining high standards, and operate seamlessly across time zones is no longer a futuristic idea — it’s happening now. \n\n## Real-world examples: how Service-as-a-Software transforms industries\n\nWe've discussed the core benefits of Service-as-a-Software, but how does it work in practice? To give you a clearer picture of its transformative potential, let's take a look at a few case studies where businesses from different industries have successfully adopted this model to tackle their scaling challenges.\n\n#### Case 1: Financial sector company (banking)\n\nIn the financial sector, a company faced significant challenges with the security of online transactions, particularly in user identity verification and fraud prevention. They decided to implement a Service-as-a-Software solution for managing the 3D Secure process, adding an extra layer of security during online payment authorization. This service, provided by an external vendor, seamlessly integrated with the bank’s existing systems. By adopting Service-as-a-Software, the company avoided the high costs of maintaining its own infrastructure, while also ensuring continuous updates and compliance with dynamic regulations. Compared to traditional on-premises solutions, Service-as-a-Software provided greater flexibility, reduced costs, and faster updates.\n\n#### Case 2: Consulting firm\n\nFor a consulting firm operating across multiple markets, managing project workflows, reporting, and communication was a struggle due to the use of separate systems in different regions. To solve this, they implemented a Service-as-a-Software platform that integrated CRM, project management, invoicing, and reporting into a single tool. This integration enabled real-time tracking of project statuses, document workflows, and resource management, ensuring smooth operations across departments. The Service-as-a-Software approach allowed remote access for employees, and eliminated the need for costly server infrastructure. In contrast to traditional on-premises models, this solution centralized operations, reduced complexity, and streamlined management.\n\n#### Case 3: Technology sector company\n\nA large technology company struggled with managing multiple SaaS applications, which resulted in excessive licensing fees and redundant tools. The company implemented a SaaS management tool that allowed centralized monitoring and optimization of application usage. This solution gave them full visibility of their application landscape, enabling automated reporting and cost optimization, saving millions by eliminating unnecessary subscriptions. The Service-as-a-Software approach automated the entire license management process, providing real-time adjustments and reducing errors, which ultimately saved the company both time and money.\n\n## How to approach Service-to-Software transformation: two paths to success\n\nWhen it comes to transforming your service into software, **there are two primary approaches: you can take the journey on your own or partner with an experienced team.** Both paths have their merits, but the right approach depends on your specific goals, resources, and the scale of your transformation. Let’s explore both options.\n\n### Do it yourself: building the solution internally\n\nIf you have the internal capabilities and a dedicated team, you may choose to approach the transformation on your own. Here’s how to get started:\n\n* **Map your service**: Begin by identifying exactly **what your service delivers to clients.** Whether it’s a decision, a report, or a recommendation, understanding your core offering is key to translating it into a software solution.\n* **Codify expertise**: **Determine which elements of your service can be converted into logic or algorithms that can run autonomously.** This is where you’ll take the expertise your team has built over years and start packaging it into a model that can be run by software.\n* **Design the client interaction**: Develop a seamless interface where clients can interact with your service, without needing direct human involvement. **This might include creating intuitive dashboards, self-service portals, or automated communication flows.**\n* **Test and scale**: Start small – focus on one specific process, one region, or one team – and then gradually expand. **This phased approach ensures that any potential issues are identified early, allowing you to refine and scale the solution effectively.**\n\nWhile this approach allows for full control, it’s important to recognize that turning your service into software is more than just a technical transformation – it’s a shift in your business model. It requires a deep understanding of your current processes, how they can be automated, and how the software will impact customer experience and internal workflows.\n\n## Partnering with an experienced team: unlock the full potential of SaaS with Boldare\n\nWhen considering a shift from a traditional service model to a scalable, automated solution, partnering with an experienced team can make all the difference**. A trusted partner brings not only technical expertise but also the strategic insights needed to navigate this complex transformation.** \n\nWorking with a partner who specializes in AI, automation, and outcome-based models ensures that you’re not just adopting new technology, but embedding intelligence and future-proofing your business for sustainable growth. \n\nAt Boldare, we specialize in helping businesses transition from traditional service models to fully automated, scalable product-based solutions. Our team combines deep expertise in AI, automation, and outcome-based models to guide you through the transformation process. What sets us apart is that we are AI-enhanced – artificial intelligence powers every aspect of the transformation. \n\nAI is at the core of what we do, driving automation, optimizing workflows, and ensuring your service operates efficiently and scales seamlessly. With AI-driven insights, your service can adapt, improve, and continuously meet evolving customer needs while staying compliant with regulations. \n\nBy partnering with us, you gain access to our full suite of expertise product design, software development, and industry-specific insights – ensuring a smooth, efficient transformation."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1763978641/grafika_na_blog_pz2yf2.png","lead":"In 2025, many business leaders face an ongoing dilemma: how do we scale our service business without simply adding more people? Relying on traditional growth strategies – hiring more staff, increasing manual processes – has become increasingly unsustainable. **Instead of following the conventional path of growing by increasing headcount, many organizations are exploring a new way forward – Service-as-a-Software.** This approach provides a scalable solution by shifting service delivery from human expertise to automation and software-driven processes. If this concept is new to you, you're in the right place. Keep reading to discover how this approach can revolutionize your business, and learn how to implement it with our in-depth guide.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-11-04T13:56:56.196Z","slug":"service-as-a-software-an-executive-guide","type":"blog","slugType":null,"category":null,"additionalCategories":["GenAI"],"url":null},"author":"Magdalena Chmiel","authorAdditional":"","box":{"content":{"title":"Service-as-a-Software: a guide to scaling your service business without hiring more people","tileDescription":"Discover how Service-as-a-Software offers a scalable solution by automating service delivery through AI, reducing operational costs, ensuring compliance, and increasing efficiency – all without the need to hire more staff. Explore the transformative power of this model and how it can revolutionize your business.","coverImage":"https://res.cloudinary.com/de4rvmslk/image/upload/v1763978641/grafika_na_blog_pz2yf2.png"},"coverImage":null}},"id":"9b8ee7be-194b-5878-809f-7ecbf3048ce1"}},{"node":{"excerpt":"","fields":{"slug":"/blog/this-week-s-ai-bite-how-ai-accelerated-end-to-end-test-automation-in-a-project-a-qa-engineer-s-perspective/"},"frontmatter":{"title":"This week’s AI Bite: How AI Accelerated end-to-end test automation in a project - A QA engineer’s perspective","order":null,"content":[{"body":"## The initial challenge\n\nWhen I joined the **TeamAlert** team as a QA Engineer and took over the end-to-end (E2E) test automation from previous engineer, one of the first challenges was understanding the existing structure and framework. The tests at TeamAlert were written in **Playwright**, while my previous experience was mainly with **Cypress**, so my knowledge of Playwright was fairly basic and came mostly from online courses.\n\n## First steps with AI\n\nThat’s when an opportunity arose to try something new. **Milena Cylińska (specializing in Playwright, Selenium, CI/CD, scalable test architecture, and AI in QA),** showed in her project how she implemented **Playwright MCP + Copilot**, an AI-assisted tool for creating tests. We decided to try it in our team as well. After a few short meetings, we managed to set everything up, and the results were visible immediately.\n\nThe pace of creating new tests increased significantly – repetitive elements were automated, and new AI-generated tests were consistent with the existing ones. The most valuable part for me was learning Playwright on a “live project,” without analyzing every line of code or documentation, simply writing new tests and seeing the results instantly. Additionally, the AI analyzed our repository and pointed out areas that were insufficiently covered by tests, which we had previously overlooked.\n\nOf course, AI sometimes makes mistakes – it can “hallucinate” or suggest solutions that don’t work. That’s why I constantly supervise it, refining instructions and prompts to make it as useful as possible.\n\n## Results and takeaways\n\nCreating tests now takes roughly **half the time** compared to writing them from scratch. The greatest value, however, is the ability to quickly get up to speed with Playwright and learn through practice – without needing to study all the code or documentation in detail. AI in end-to-end testing at TeamAlert not only speeds up the process but also helps maintain consistency, detect gaps, and allows the team to focus on more important, creative tasks. Combining human expertise with AI capabilities makes the work **faster, smarter, and more effective**."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1762181907/1761571320938_mdtavi.png","lead":"In this article, I’ll walk you through my experience introducing AI into end-to-end test automation at TeamAlert - how it helped me learn Playwright faster, improve test coverage, and speed up our testing process.\n\n**Weekly AI Bites is a series that gives you direct access to what’s happening in our day-to-day AI work.** Every post comes straight from our team’s meetings and Slack, sharing insights, tests, and experiences we’re actively applying to real projects. \n\nWhat models are we testing, what challenges are we tackling, and what’s really working in products? You’ll find all of this in our bites. Want to know what’s buzzing in AI? **Check out Boldare’s channels every Monday for the latest weekly AI bite.**","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-11-03T14:29:14.299Z","slug":"ai-accelerated-end-to-end-test-automation-qa-engineer-perspective","type":"blog","slugType":null,"category":null,"additionalCategories":["GenAI","How to","Tech"],"url":null},"author":"Roksana Kaczmarska","authorAdditional":"","box":{"content":{"title":"This week’s AI Bite: How AI Accelerated end-to-end test automation in a project - A QA engineer’s perspective","tileDescription":"Discover how AI sped up end-to-end test automation at TeamAlert — from learning Playwright faster to achieving smarter QA workflows.","coverImage":""},"coverImage":null}},"id":"839ccf33-d520-51f7-a0ab-7772a90214ec"}},{"node":{"excerpt":"","fields":{"slug":"/blog/behind-the-scenes-of-ux-research-how-listening-to-users-shapes-better-design/"},"frontmatter":{"title":"Behind the scenes of UX research: how listening to users shapes better design","order":null,"content":[{"body":"UX research, in our view, is not an online survey or a quick call. It’s a process of entering the user’s world, letting them lead us, and then connecting their experiences with both design and business perspectives. That’s why we conducted interviews in the participants’ native languages – so they could speak freely, and we could capture emotions, nuances, and meanings that are often lost in a foreign language.\n\n\n\n## **Why is research key?**\n\nDesign without research is guesswork. An interface may look perfect on mockups, but if it doesn’t support real scenarios, it remains an empty shell. Research is the compass that shows where users face barriers, what feels intuitive to them, and what needs to be improved.\n\nIn the case of Occhio, our goal wasn’t just to “test the configurator.” We wanted to capture the entire service design process: from the client’s first contact in the showroom, through how sales staff work with the configurator, all the way to the purchase decision. It’s within this journey that the brand image is truly shaped.\n\n![The configurator as brand touchpoint](https://res.cloudinary.com/de4rvmslk/image/upload/v1761921598/Container_ptphm5.png \"The configurator is part of the brand, not just a sales tool.\")\n\n## **The process**\n\nWe began with a contextual analysis – mapping the sales and customer service ecosystem. We wanted to understand how different types of showrooms (Flagship, Premium Partners, Partners) operate day to day and what their priorities are.\n\nNext, we prepared a research plan that combined qualitative and quantitative methods. We started with in-showroom interviews and observations, where we could see real interactions. Sales staff showed us how they used the configurator in conversations with customers, when they had to look for workarounds, and where the tool genuinely made their work easier. They shared what delighted them and what discouraged them when using it.\n\nWe carried out these conversations in the participants’ native languages, knowing that this was the only way to hear authentic emotions – from frustration to excitement to moments of hesitation. These are details we would never have caught if the research had been conducted “for convenience” in a foreign language.\n\nAt the same time, we analyzed quantitative data. The numbers showed which features of the configurator were used heavily and which were practically nonexistent in daily practice. This comparison was crucial: the stories and observations from the showrooms explained why the numbers looked the way they did.\n\nFinally, we held workshops where we brought all the insights together and worked with the Occhio team to set priorities – what should be improved right away, and what should be planned for the longer term.\n\n## **Key findings**\n\nWhat struck us most was that the configurator is not just a sales support tool. It’s part of the brand identity – its performance (or shortcomings) directly influences how Occhio is perceived.\n\nWe also found that different user groups have very different needs:\n\n* In Flagship stores, full functionality and advanced options matter most.\n* In Partner stores, simplicity and speed of service are key.\n* For end customers, it’s all about intuitiveness and the “wow” effect.\n\n## **How research shaped the design**\n\nThe findings gave clear direction for further design work. Thanks to them:\n\n* We simplified core workflows, reducing the time sales staff spend on tasks.\n* We identified where advanced features should be developed for Flagship stores.\n* We proposed solutions that increase intuitiveness and customer satisfaction.\n\nThis is proof that research is not theory or a report to be filed away. It’s a tool that directly translates into usability and brand experience.\n\n## **Takeaways for the future**\n\nThis project once again confirmed that research is the foundation of design. It ensures we don’t design “for ourselves” or “on a hunch,” but based on facts, observations, and the real emotions of users.\n\nCombining UX and service design perspectives showed that the configurator is just one touchpoint – but how it works affects the entire customer journey.\n\nThat’s why we say the best design is created when we truly listen to users.*And yet, research is only the beginning of the journey. Improving a product starts here, but it certainly doesn’t end here. Ahead lies the validation of introduced changes, continuous optimization, and further iterations – because great design is never finished, it keeps evolving with its users.*"}],"job":null,"photo":null,"slug":null,"cover":"","lead":"**Every project starts with a question: who are we designing for? At first glance, the answer seems obvious – “for the customers.” But in practice, the reality is much more complex.**\n\n**Working with Occhio, a brand renowned for perfect design and lighting quality, we knew we couldn’t stop at the surface. The lighting configurator we focused on during the research is not just a sales tool. It’s a crucial element of the entire brand experience – in the showroom, in interactions with sales staff, and in the eyes of end customers.**\n\n**We wanted to understand how this system really works – not in theory, but in everyday practice. That’s why we stepped out from behind our desks. It meant traveling, spending hours in showrooms, talking to salespeople and customers, and getting to know the company from the inside.**","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-10-31T14:23:23.376Z","slug":"behind-the-scenes-of-ux-research-occhio","type":"blog","slugType":null,"category":null,"additionalCategories":["How to"],"url":null},"author":"Aleksandra Maslon","authorAdditional":"","box":{"content":{"title":"Behind the scenes of UX research | Occhio case study","tileDescription":"Discover how Boldare’s UX research with Occhio transformed a lighting configurator into a seamless brand experience through real user insights and design.","coverImage":"https://res.cloudinary.com/de4rvmslk/image/upload/v1761923038/Blog_thumbnail_y6yykf.png"},"coverImage":null}},"id":"4fb18ed0-51f6-5af4-b0db-162f39414da9"}},{"node":{"excerpt":"","fields":{"slug":"/blog/this-week-s-ai-bite-ai-in-action-accelerating-symfony-migration-from-5-4-to-6-4/"},"frontmatter":{"title":" This week’s AI Bite: AI in action: accelerating Symfony migration from 5.4 to 6.4","order":null,"content":[{"body":"## **What was the task we decided to use AI for?**\n\nWhen we took on the upgrade of Symfony from version 5.4 to 6.4, we faced a challenge that normally consumes days of developer effort with little direct business value: going through changelogs, documentation, and deprecations. By applying AI to automate these steps, we reduced the process from days to just a few hours – cutting costs, minimizing risk, and freeing our team to focus on innovation and product growth.\n\n## How much time does a framework upgrade usually take?\n\nCarrying out such a task manually can take several days. Our estimation was that the discovery phase and preparation of the change list alone would take at least one full day. This is exactly where AI-powered development tools prove their value, drastically reducing the time spent on repetitive tasks and allowing developers to focus on higher-impact, creative work.\n\n## Is it worth using AI in this particular case?\n\nAbsolutely. With Claude, we completed the upgrade in just a few hours. Automating routine tasks fast-tracked the process and reduced the manual effort usually required for framework migrations – a clear demonstration of how Polish software developers are leveraging AI to improve both productivity and quality.\n\nHow did we use Claude for the system upgrade?\\\nWe relied on Claude (Opus) to gather information from Symfony changelogs and documentation and generate a detailed action plan within minutes. Claude (Sonnet) then supported the coding process by:\n\n* automatically removing deprecations and adjusting code to new interface signatures,\n* updating configuration and Composer packages,\n* fixing User classes to ensure compliance with the latest changes.\n\n### The crucial role of automated tests\n\nOne key factor behind the success of this migration was our strong testing culture. Thanks to comprehensive automated tests, Claude could instantly verify whether the applied changes preserved the application’s behavior — and correct itself when needed. This turned the developer’s role into a supervisory one rather than manual debugging. Without tests, identifying errors introduced during the AI-driven upgrade would have been far more time-consuming.\n\nAt Boldare, we treat testing as a fundamental part of development: every new functionality comes with tests by default. This ensures that when we introduce changes — whether made by humans or AI — the system’s behavior remains stable and predictable.\n\n## What does this all mean?\n\nThis Symfony upgrade proved to us that AI is not just a buzzword but a practical tool delivering measurable results: a multi-day task reduced to hours, with lower risk and higher accuracy. By letting AI handle the heavy lifting of framework migrations, we keep our codebase modern and compliant while our developers focus on building features that directly impact customers and business outcomes."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1760352625/Group_1000004996_uwigif.png","lead":"Incorporating AI into daily development routines is transforming how teams approach complex tasks. From analyzing changelogs to automating code updates, AI can significantly reduce effort and risk. **This article shows exactly how we applied AI during a Symfony 5.4 to 6.4 migration,** cutting days of work down to mere hours and enabling our team to focus on delivering real business value.\n\n**Weekly AI Bites is a series that gives you direct access to what’s happening in our day-to-day AI work.** Every post comes straight from our team’s meetings and Slack, sharing insights, tests, and experiences we’re actively applying to real projects. \n\nWhat models are we testing, what challenges are we tackling, and what’s really working in products? You’ll find all of this in our bites. Want to know what’s buzzing in AI? **Check out Boldare’s channels every Monday for the latest weekly AI bite.**","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-10-13T10:42:33.877Z","slug":"ai-in-action-accelerating-symfony-migration-from-5.4-to-6.4","type":"blog","slugType":null,"category":null,"additionalCategories":["GenAI","How to","Ideas","News","Tech"],"url":null},"author":"Szymon Kopa","authorAdditional":"Roksana Kaczmarska","box":{"content":{"title":"AI in action: accelerating Symfony migration from 5.4 to 6.4","tileDescription":"Discover how AI can accelerate and simplify framework upgrades. Learn how we used AI to streamline the Symfony 5.4 to 6.4 migration, reducing days of work to just a few hours while improving efficiency and accuracy.","coverImage":""},"coverImage":null}},"id":"71645794-faca-52ec-8f6d-3427c77ecdcd"}},{"node":{"excerpt":"","fields":{"slug":"/blog/introducing-teamalert-desktop-app-3-0-a-new-era-of-workplace-safety/"},"frontmatter":{"title":"Introducing TeamAlert desktop app 3.0: a new era of workplace safety ","order":null,"content":[{"body":"## What is TeamAlert?\n\nTeamAlert is more than just an app; it’s a trusted tool that’s become synonymous with workplace safety. From municipalities to healthcare, education, and private businesses, TeamAlert empowers employees to discreetly send alerts for immediate assistance, ensuring that no one is left alone during critical moments. \n\nWith a presence across six countries and 48 U.S. states, it’s a solution that organizations rely on when safety is paramount. **TeamAlert is changing the game in public safety.** \n\nBoldare has partnered with TeamAlert from the beginning, shaping their digital product into one that now protects hundreds of organizations. \n\n[](https://www.boldare.com/work/teamalert-transition-from-mvp-to-pmf/)\n\n## What’s new in TeamAlert desktop app 3.0? \n\n**TeamAlert Desktop App 3.0** introduces key updates to enhance user experience and functionality. \n\nThe app now offers improved communication tools, a streamlined design, and better information-sharing capabilities, making it a more efficient solution for users. Want to see the 3.0 app in action? Visit the [TeamAlert website and request a demo.](https://teamalert.com)\n\n## Overcoming distance: a global partnership \n\nAs Allan Wilson, TeamAlert’s CEO, [shared in our interview about Polish-American cooperation,](https://youtu.be/LRyBohtWFdo?feature=shared) overcoming time differences and cultural barriers has played a pivotal role in the success of this partnership — alongside the expertise and capabilities of Boldare.\n\n\n\n## A big shout-out to TeamAlert: the future of workplace safety is here \n\nWe want to extend our thanks to all the users who have shared their feedback, helping shape TeamAlert into the powerful tool it is today. Your input has been essential in ensuring the app remains useful, safe, and effective. \n\n**A special thank you goes to TeamAlert for their trust and continuous partnership. The journey continues, and we can’t wait to see how TeamAlert Desktop App 3.0 makes a difference for workplaces around the world.** \n\n**The future of workplace safety is here, and we’re excited to see how TeamAlert Desktop App 3.0 transforms workplaces globally.**"}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1761831426/Zrzut_ekranu_2025-10-30_o_14.36.56_xkkvhb.png","lead":"Today, we’re excited to introduce the all-new **TeamAlert Desktop App 3.0**. This release marks a significant leap forward for TeamAlert, showcasing their commitment to not just meet, but exceed the evolving needs of its users. With new features developed directly from user feedback, **[TeamAlert](https://teamalert.com)** continues to lead the way in workplace safety, offering innovative solutions that stay ahead of the curve. \n\nIn collaboration with Boldare, a trusted software and strategic partner, every detail of the app has been refined to deliver a more intuitive, responsive, and reliable experience. Together, we’ve crafted an app that equips teams with the tools they need to act swiftly in critical moments. \n\nA special congratulations to the TeamAlert team for their constant focus on user needs and their ongoing dedication to making a real impact. TeamAlert always puts its users first. Now, let’s take a quick tour of the exciting new features behind **[TeamAlert ](https://teamalert.com)Desktop App 3.0.**","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-10-09T08:56:28.723Z","slug":"introducing-teamalert-desktop-app-3-0-a-new-era-of-workplace-safety","type":"blog","slugType":null,"category":null,"additionalCategories":["Digital Product","News","Ideas","How to"],"url":null},"author":"Roksana Kaczmarska","authorAdditional":"Magdalena Chmiel","box":{"content":{"title":"Introducing TeamAlert desktop app 3.0","tileDescription":"Stay protected with TeamAlert 3.0 – the smarter desktop app designed to enhance workplace safety, streamline alerts, and keep your team secure.","coverImage":""},"coverImage":null}},"id":"8ecce74d-db40-58e2-a27e-34563d741a5a"}},{"node":{"excerpt":"","fields":{"slug":"/blog/how-ai-and-figma-mcp-changed-the-way-i-build-frontend-in-large-scale-projects/"},"frontmatter":{"title":"How AI and figma MCP changed the way I build frontend in large-scale projects?","order":null,"content":[{"body":"## What is MCP?\n\n**The Model Context Protocol (MCP)** is an open standard that gives AI models access to external tools and data. In practice, this means that instead of dumping entire project files or documentation into a prompt, we can expose “MCP servers” – small intermediary services that return structured, precise context. Communication usually happens over HTTP or SSE, and in Figma’s case, the server runs locally within the desktop app.\n\n## Configuring Figma MCP\n\nSetting up Figma MCP is very straightforward – just two steps:\n\n1. **Enable the MCP Server in Figma**\n2. * Make sure you’re on the latest version of Figma Desktop\n * Open any design file\n * Go to **Figma → Preferences → Enable local MCP Server**\n * You’ll see a confirmation at the bottom of the screen that the server is running locally\n3. **Add the MCP Server in Cursor**\n4. * Open **Cursor → Settings → MCP**\n * Click **+ New MCP server**\n * Paste and save the following configuration:\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1759757351/Zrzut_ekranu_2025-10-6_o_15.28.31_ssnvmt.png)\n\n## Using MCP in Practice\n\nFrom now on, you can pass Figma designs as context to AI in Cursor in two ways:\n\n* **Selection-based** – select a frame or layer in Figma, then ask Cursor to help you implement that specific element.\n* **Link-based** – copy the link to a frame/layer in Figma and paste it into your prompt; AI will reproduce the design based on the URL.\n\nMCP provides several tools, including:\n\n* **get_code** – generates component code from a selection (React, Vue, HTML/CSS, etc.)\n* **get_variable_defs** – returns variables and styles (colors, spacing, typography)\n* **get_code_connect_map** – maps Figma elements to code components if Figma Code Connect is configured\n* **get_screenshot** – generates a screenshot of the selected fragment\n* **create_design_system_rules** – produces design system rule files\n* **get_metadata** – returns XML with layer properties (IDs, names, types, positions, sizes)\n\n## A Practical Example\n\n### Design\n\nTo illustrate how MCP works, let’s implement a **notification menu** component.\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1759757582/Zrzut_ekranu_2025-10-6_o_15.32.37_pdr0ns.png)\n\n### Prompt\n\n***Implement component. Make it look 1:1 as it looks in Figma, it should be exactly the same. Write only professional styles, forget about !important, avoid absolute positioning where possible etc. Use MUI components. Use design system defined in @theme.ts. Place all the code in ./components/notifications directory.***\n\n### Model Results\n\nI used two state-of-the-art models: **GPT-5 (OpenAI)** and **Claude Sonnet 4 (Anthropic)**.\n\nBoth generated code as logically separated components (NotificationItem, NotificationMenu, NotificationHeader). Importantly, the AI correctly pulled in Material UI components (e.g., Popover), ensuring proper HTML semantics and accessibility out of the box.\n\n**What worked immediately:**\n\n* Correct use of MUI (no unnecessary wrappers)\n* Component structure aligned with project logic\n* Consistency with our design system (theme.ts) – most spacings and colors were token-based\n* Interactivity – the component wasn’t just static HTML: AI prepared callbacks (onSettingsClick, onNotificationClick, onMarkAllAsRead), and tabs already filtered notifications between sections\n\n**What needed adjustments:**\n\n* Spacing – some margins were off by a few pixels\n* Tabs – filtering worked, but UI needed polishing\n* Styling details – e.g., icon and text alignment for pixel-perfect match\n\n### Final Result\n\nAfter about **20–30 minutes** of manual tweaks, I had a ready, production-quality component: pixel-perfect, fully interactive, and aligned with the project’s design system.\n\n## Comparing to the Traditional Approach\n\nWithout MCP and AI, this implementation would have taken several hours – mostly spent copying spacing, checking font sizes, writing tab logic, callbacks, and searching through MUI docs.\n\nWith MCP + AI, the process looked like this:\n\n* ~5 minutes: generate the first version\n* 20–30 minutes: manual fixes and refactor\n* **Total: <1 hour instead of half a day**\n\n## Challenges and Concerns with LLMs in Frontend\n\nLike any tool that automates developer work, LLMs raise questions about quality and risks. I had similar doubts at first:\n\n**Will the generated code be poor quality?**\n\n* Concern: “AI will create spaghetti code no one wants to maintain.”\n* Reality: quite the opposite. The code was split into clean components, used MUI properly, and applied our theme. This worked because AI had project context (e.g., theme.ts, existing components).\n\n**Can AI handle all types of UI?**\n\n* Not always. It’s excellent for static and repetitive components – cards, lists, layouts, simple modals.\n* But for highly interactive elements (drag & drop, custom animations, unique behaviors), it often struggles. In those cases, treat AI’s output as a starting skeleton, not a finished feature.\n\n**How to keep consistency with project best practices?**\n\n* Cursor helps here. The IDE indexes the project and gives the LLM context (folder structure, existing components, theme files, even tests). This ensures generated code fits the repo’s style and avoids duplication.\n\n**When to use AI vs not?**\n\n* Use AI: new screens from Figma, repetitive components, when speed and style consistency matter.\n* Don’t rely on AI: critical business logic, complex interactive features.\n\n## Conclusion\n\nAI in frontend isn’t replacing developers anytime soon – but it’s already an excellent assistant. It takes care of repetitive tasks, helps us move faster from mockup to working component, and frees us up for what matters most: conscious engineering decisions.\n\nExperiments are ongoing, and the market is evolving rapidly – what’s cutting-edge today may become standard tomorrow. At **Boldare**, we share these learnings within our **Next Gen Guild**, where we test AI tools in practice and exchange insights in real time. I highly recommend this approach to any team – it’s not just about using the technology, but about building knowledge together and learning continuously."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1759756615/Group_1000004983_cesova.png","lead":"**Frontend development** is mostly about working with code – but not always in the most creative way. Instead of focusing on application logic or interactions, we often spend hours replicating Figma designs: font sizes, spacing, and colors. On top of that, we need to dig through UI library documentation to find the right component and manually adapt it to the design system.\n\n**Thanks to Figma’s Model Context Protocol (MCP)** and AI tools like **Cursor,** this process can be significantly shortened. In my case, even on the very first try, the difference was huge: the AI automatically suggested the right Material UI components, generated styles and layouts, and my role was reduced to polishing the details.\n\nIn this article, I’ll show what this approach looks like in practice: from configuring **Figma MCP**, through automatic generation of **React/TypeScript** components, to reflections on how AI is already reshaping a frontend developer’s daily workflow. Importantly, I use this approach daily in a large-scale production system (over 70,000 lines of code), which proves that AI is not only useful for rapid MVP prototyping, but also for advanced enterprise projects.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-10-06T13:04:48.801Z","slug":"how-ai-and-figma-mcp-changed-the-way-I-build-frontend-in-large-scale-projects","type":"blog","slugType":null,"category":null,"additionalCategories":["GenAI","Future","How to"],"url":null},"author":"Roksana Kaczmarska","authorAdditional":"","box":{"content":{"title":"How AI and figma MCP changed the way I build frontend","tileDescription":"Discover how AI and Figma MCP revolutionized my frontend workflow, cutting development time and boosting efficiency in large-scale projects.","coverImage":""},"coverImage":null}},"id":"e77beb37-5dd9-56a5-8104-097f864de460"}},{"node":{"excerpt":"","fields":{"slug":"/blog/beyond-the-hype-real-world-ai-development-workflows-practical-insights-by-michal-czmiel/"},"frontmatter":{"title":"Beyond the hype: real-world AI development workflows - practical insights by Michał Czmiel","order":null,"content":[{"body":"**Piotr (host):** Cześć! I'm Piotr, co-CEO of Boldare, and you are watching Agile Product Builders Tech Edition, a 25-minute series for product builders. In this episode, we go deeper into real-world AI development workflows, as we are interested in how developers actually use these tools beyond the hype. Today's guest is Michał Czmiel, senior software developer and tech lead here at Boulder. \n\nMichał is joining us, as you guessed, for the second time on the show, so we have a chance to talk about what's changed in the state of AI since we last spoke. And that was already four months ago. Michał has been with Boulder for over seven years now, and he has shaped some of the most successful products we've built.\n\nHe's part of the guild of AI early developers, where we experiment with the newest tools, and at the same time, he's building an AI-first product from the ground up. I think it's a nice welcome chapter for you, Michał. It's great to have you—welcome to the show.\n\n**Michał (guest)**: Yes, very nice. I hope I can live up to it, but yeah, nice to be here. \n\n## The AI revolution: Hype, progress, and the pace of change\n\n**Piotr:** It feels like the AI revolution is moving at lightning speed, maybe even faster than JavaScript frameworks. Remember the last decade? What do you think about it?\n\n**Michał:** I think you can approach it from different angles. In terms of tooling, it’s definitely very fruitful. We had a period when new AI tools for building applications were appearing constantly — things like LLM callings, cost analysis tools, proxies, and gateways. Now I feel like we are in the hype cycle for coding agents.\n\nMaybe this is connected to the large investments and evaluations, with many believing you can build your own agent software, sell it, and succeed. But yes, I do feel we are progressing.\n\nSome people expected bigger leaps with the release of GPT-5, as if it would open a completely new frontier, but I still think each new model is an improvement over the last one. Overall, I think it’s a very interesting time right now. \n\n## **AI in action: From one-shot tasks to complex workflows**\n\n**Piotr:** It's really interesting. So tell us — walk us through your typical coding workflow with AI. Let’s say, from small one-shot changes to more complex tasks where you use plan documents or step-by-step scaffolding. How does it look?\n\n**Michał:** All right, so I think what changed from the last webinar to this one is I try to use the AI-first experience more often. And in terms of adding new code, developing new features, I would highlight three main approaches, three workflows as you said.\n\nFirst is just one-shot, where you specify a prompt, usually for small changes like adding a new test case to an existing suite, or adding a new field to the database and returning this field from the REST API, or adding a new page in an existing onboarding flow. So you have to feel in your project what is the best scope for this one-shot. But I would say most of the tasks can be one-shotted by agents right now. And when I say agent, I primarily use the Cursor agent, but we'll talk about other tools later.\n\nAnd if the output is big—sorry, if the output is wrong, if it couldn’t be one-shotted—often it’s that the task was too big or just your prompt was not specific enough or too vague.\n\nThen the second approach I like to employ is the plan document. So either I write the plan myself and AI just refines it, pokes holes in the plan, suggests improvements, or AI prepares the plan and I read through it, edit it, and then the agent executes this plan. This plan is almost 100% of the time written in a Markdown file. It’s just better to edit. You can open it in full screen rather than having a small prompt window. You can use your keyboard shortcuts for this plan and also tab completion, which also speeds things up a lot.\n\nAnd here, for the plan, I use it for larger features. For example, we already have a Gmail sign-up flow, let’s add sign-in with Microsoft. Or also for something I call intelligent refactors. So not only search-and-replace, but also search-and-replace if/when conditions apply.\n\nAnd just a final tip on the plan approach: often the plans generated by AI have lots of fluff that is necessary for humans, like estimations, observations, risks. In my opinion, the best way is to prune it once before you feed it to the AI agent, so it doesn’t pollute the context.\n\nAnd my normal workflow that I use for maybe complex features or a big user story: I research the plan, I research the feature. In the previous webinar I mentioned that I was using AI Studio by Google. Now I switched to using the Claude UI because I like that it gives more compact responses. You can specify whether it should be “thinking” or not, and I also like the conversational style.\n\nSo once I have the idea in my mind of what I want to build, I manually scaffold most of the structure in the code, because I like to keep control and guide the AI into specific approaches. Then the agent does the unit of work: I prompt the agent to do something, then it runs the automatic validation—linting, type checking, tests, building the app—and this is triggered by the LLM.\n\nThen I validate this piece of code myself. I review the code. If something is terribly wrong, I run it again with an improved prompt. If it’s good enough, I make manual changes. Then I commit this piece of code and repeat this flow.\n\n> So instead of creating a crazy prompt saying “create this whole onboarding flow with X pages,” I still try to use the AI agent but work in these unit-of-work approaches.\n\n**Piotr:** Yeah. And I still think we are at the early stage of discovering these workflows, especially within a team. For more complex tasks you have a lot of artifacts you can share — not only with other engineers, but also with quality-assurance engineers. Like last time we had Milena, for instance. Documentation is becoming something that is not only needed for users but also for the creators. Whether it’s an LLM creator or yourself, it doesn’t matter — you still own the code. You’re not handing ownership over to an agent.\n\n## Keeping AI coding secure: Human oversight and best practices\n\n**Piotr:** So do you already have security measures that you apply in this setup? I’m even thinking of hashtag AI-generated line markings, or obviously code reviews, which should be mandatory. But things like API keys, or personal data being pasted into LLMs — what do you do in this case?\n\n**Michał:** Yeah, so you can spot AI-generated code. Often it has a lot of comments, but it’s not necessarily bad. In terms of security measures in a team, you said a very good thing — it’s a team effort right now, right? Everybody has a coding agent.\n\nSo one of the security measures is to make sure that all of your developers know what the limitations are and have agreed on certain rules or standards. This way, code that could be malicious, isn’t performant, or could cause issues won’t enter the codebase.\n\n> I think the crucial thing is still a mandatory code review culture. You should be the first reviewer. Even if you only have a fragment of AI-generated code, even just tab completion, you should look at it as if you didn’t write the whole thing. So I think code reviews should spot a lot of issues.\n\nOf course, you can also add GitHub Copilot or another tool that can pre-review the code. Lots of tools like static analysis could be included as an automatic step after the LLM finishes. There are also a bunch of tools that search for secrets or other hardcoded things.\n\nFor example, there was a famous attack in recent weeks where a malicious infected library used local Claude code to search your codebase and directories for secrets, environment variables, and API keys. So this is not super related, but often those agents — like Cursor or Claude Code — can access all the files you have.\n\nPersonally, in terms of secrets, I prefer fetching them dynamically. For example, when I need to run some script, I can fetch those dynamically from something like AWS Parameter Store or another password manager. They also have CLI tools that can inject security environments into your running process.\n\nSo yeah, I wish I had some magic solution to the security problem in AI, but I think it still comes down to human oversight.\n\n**Piotr:** Yeah, but we have to remember that we still own the code. And the second thing is maybe dialogue in the team — so a team contract or a policy is a good way to go. Okay.\n\nAI Beyond Coding: Automating Chores, Documentation, and Cross-Disciplinary Tasks\n\nPiotr: But outside of pure coding, which AI use case has been the biggest change, the biggest game changer for you? I’m thinking about diagrams, log analyzers, or refactoring documentation.\n\nMichał: Yeah, I would say all the chores that some people — myself included — in the team didn’t want to do or were lower priority.\n\nFor example, some stakeholders or external teams want up-to-date documentation of all the functionalities that one of our services provides. Keeping this documentation current can be tough and can easily go out of sync. So you can use an AI agent: “Hey, please analyze these files and generate a markdown text.” Then you can run a tool called Pandoc, which translates markdown to PDF, and voila — you have a very nice report.\n\nAlso, I did one project where we had to move one database to another, and there was a bunch of wrong data, invalid formatting, and inconsistent casing. What I found interesting is that I made the agent work in a loop, and I think that’s a very good solution for these kinds of problems. Keep the agent in this closed loop: in this case, “Here are the CSV files, here are the problems, and here are the tools.” We were using Pandas in Python for cleanup and CSV processing. Unless everything is fixed, you need to iterate over it until all issues are resolved.\n\nThere are a bunch of other use cases I found in documentation. I’m a big fan of diagramming and creating concepts in Excalidro, which are often very low fidelity and maybe not the best to showcase. I can paste it into an AI agent and say, “Please generate a diagram as code in PlantUML” from this low-fidelity diagram. Or I can even add it to a prompt to implement something.\n\nI also see use cases for self-review, like: “Please debug this code, please improve it, or analyze potential refactoring issues.” One team in Boulder used AI to conduct a migration of the Symfony framework. They had one version and wanted to migrate to another. They ran an AI agent with Opus (Sonnet for Opus), which created a migration plan, and then another agent executed it.\n\nFinally, having this AI “body” is very empowering for crossing into different areas. For example, if you’re mainly a frontend developer, you can use AI to learn more about databases and endpoints and contribute to that part of the system. If you’re a backend engineer, you might explore DevOps with AI, for example using Terraform or CDK.\n\nI also think these agents are perfect for prototypes or landing pages. Those used to take a lot of time to develop, but now they can be easily “uncoded.”\n\n**Piotr:** Yes, I think you brought a very interesting point — these LLMs can make Agile teams more multidisciplinary. And also, when I hear developers talking about documentation, I see some kind of change. So yes, the boring stuff can be fun again. That’s great.\n\n## Choosing the right AI tools: Cursor vs. Cloud Code\n\n**Piotr:** Okay, when we last talked about four months ago, the big shift was AI-first IDEs, Cursor, and now Cloud Code has also become mainstream. I think maybe even 50/50 among developers at Boulder. How does it look on your side today?\n\nMichał: So I tested both, just to have some opinions and get a feel for them. But my primary tool is still Cursor, and recently I’ve been testing it with GPT-5.\n\nIt had some problems at the beginning with the intelligent router that detected which models to run and with performance, but they have slowly improved it. So yeah, I’m very happy with this setup. Of course, interchangeably I use Cursor with Claude and Sonnet models, as sometimes GPT-5 just takes very long to process. But still, I think the full package you get with Cursor is very nice, and I also still use its tab completion a lot.\n\nI write some of the code manually, if you want to note that. The other approach I tested is with Cloud Code, where I used the Z Editor, which has a very nice tab completion model. Actually, they’ve been adding lots of features, like a UI for Cloud Code. They’re even working on a new Git-like system for AI agent collaboration.\n\nI feel that with Cloud Code, you mostly go into a delegate approach, where you don’t need to read every div. I think it’s a simpler tool because it doesn’t have a UI or all the extra layers that Cursor adds by indexing your codebase. So Cloud Code is much simpler, and that’s very nice.\n\nI found it very suitable for smaller projects, where I don’t need to dive deep into every error. You can just say, “Here’s the screenshot of the problem, please fix it.” So it’s more AI-driven than manually code-driven.\n\nI’ve also seen interesting use cases. Some people installed Cloud Code on a server, so you can SSH into the server, say, “Hey Claude, please write a Python script that serves something,” and then run it. Or it could configure the whole server.\n\nTo close this topic, we have this shift: some people use Cursor, some Cloud Code, some Copilot. As long as you are productive, and even if there’s disagreement about which tool to use, it’s fine. Most tools have now caught up. Now it’s mostly a matter of pricing, because these tools have very different pricing models — API-based, API-key-based, or subscription-based — and also different access to models.\n\nFor example, Cloud Code is currently limited to Anthropic models, which isn’t an issue as they are top models, but who knows what will happen in four months.\n\n**Piotr:** Yeah, exactly. I can maybe oversimplify it: for you, Cursor is still the more advanced and better option. On the other hand, Cloud Code is simpler — you don’t have to leave your favorite IDE, and you still benefit from many AI-enhancing functions. The ideal combination would of course be… a “starting flame world.”\n\n**Michał:** Yeah, I feel the ideal combination would be Cloud Code with Cursor’s tab completion and div statements, which I don’t know if it’s financially viable because you’d pay for both subscriptions. But I think that’s the best approach.\n\n**Piotr:** Oh, we have to try it. Okay. And I think you’ve tried it, but maybe that’s for another time.\n\n## Best practices for AI agents: Prompts, validation, and trust\n\n**Piotr:** Let's go to the best practices working with AI agents — prompt, validating results, and deciding when to trust the agent versus doing it manually.\n\n**Michał:** So recently I've shifted more into AI-first. I'm sometimes even curious how AI would solve a problem rather than me. And then, being inspired, I'm like, “Okay, yeah, maybe it's a good approach, but let's try it,” or let's throw it out and start with something new. There are a bunch of best practices I could talk about.\n\nOne I think is really important is that you should be driving the AI agents not only with your rules. I think we haven't mentioned roles in the previous webinar, but those are essentially whether it's Cloud MD, Agents MD, or Cursor rules. There should be… there should be an elevator pitch about their product: what tools the AI coding agent can use, what the guidelines are, what it should do, what it shouldn’t do, and the product structure, so it doesn’t have to search everything every single time.\n\nAnd this automatic validation — I highly recommend attaching a sentence saying: after making a change, please run, make sure the tests are valid, that the types are valid, and that linting is checked, for example. Then you have this loop: the agent can finish the code, finish the implementation, and if something is broken, it can automatically fix it and run it again.\n\nAnother tip: some tools are more advanced. They can run automatically only on the changed files, so they don’t have to run the whole test suite for the entire project, only the changed parts.\n\nOther improvements or best practices I’ve found: you should drive the agent with your code. I’ve seen lots of solutions feature mocks or very untestable code. So you can start using dependency injection and patterns that guide the AI toward a good solution. You mentioned MCP in another webinar — one big use case I’ve seen some developers in Boulder use is Context7 MCP, to make sure the AI has up-to-date documentation and all the context needed for all the features.\n\nTo close down my rant: I think most of the tips can be boiled down to your prompts. From what I’ve learned, I try to structure each prompt with three parts.\n\nFirst is the context: “Analyze these files, understand this mechanism, look here.” Then there’s the action: what exactly we want to do — be very specific. And this is my second biggest tip: use very specific action. Talk to the AI as you would to another advanced developer. For example: use dependency injection, use server actions, use indexes — very specific wording.\n\nFinally, the third part is limitations: for example, “Think before you execute, don’t worry about backwards compatibility, simplify this code without changing any functionality.”\n\n**Piotr:** Thank you, Michał. That was very valuable for me, and thanks for all these practical insights. I hope the viewers liked it as much as I did.\n\n## From manual to AI-first: Are workflows ready?\n\n**Piotr:** Do you think the AI workflows are already ready to become the default way of working?\n\n**Michał:** So, speaking with other colleagues, I feel like—as I mentioned before in the webinar—there's this lever, right? On one side, you have more manual coding, and on the other, more agentic coding. I feel like I'm still in the lower part because, once you hear all the blog posts from major LLM labs like Anthropic, they said most of the code for cloud code is written by agentic coding.\n\n> And I think that's the goal. Implementing those practices—and the practices your team develops—aims to have the agent handle as much of the boring work as possible. You do the thinking, but the agent implements it, so it can increase your velocity and help you deliver more.\n\nAnd, of course, you still need to make sure the quality is there. But yeah, I think agentic workflows are the way to go.\n\n## SUMMARY:\n\nMichał shows that AI is more than just hype—it’s actively reshaping how developers work. By combining AI agents with human oversight, teams can focus on creative, high-impact tasks while letting AI handle repetitive work. Here are the key takeaways from the interview:\n\n**Key Takeaways:**\n\n1. **AI Tools Are Rapidly Evolving:** More options, faster innovation, but hype can be misleading.\n2. **Workflows Matter:** One-shot for small tasks, structured plans for bigger features.\n3. **Human Oversight Is Essential:** Code reviews, security checks, and team policies are a must.\n4. **AI Handles Repetitive Work:** Documentation, refactoring, and data cleanup done faster.\n5. **Cross-Disciplinary Learning:** AI helps developers explore new areas beyond their core expertise.\n\n**Piotr:** Very interesting. Thank you very much, Michał.\n\n**That's it for today's episode of Agile Product Builders Tech Edition. Thanks for joining us, and stay tuned for more practical insights on how AI is reshaping the way we build software. Thank you.**"}],"job":null,"photo":null,"slug":null,"cover":"","lead":"In this episode of **Agile Product Builders Tech Edition**, Piotr Majchrzak sits down with Michał Czmiel, senior software developer and tech lead at Boldare, to explore the evolving landscape of AI in real-world software development.\n\nMichał shares insights from his experience building AI-first products, experimenting with the latest AI tools, and shaping best practices for developers. From one-shot coding tasks to complex multi-step workflows, **he dives deep into how AI agents are integrated into modern development pipelines, transforming productivity, collaboration, and code quality.** Check out the full transcript and watch the episode.\n\n","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-10-03T09:06:35.476Z","slug":"beyond-the-hype-real-world-ai-development-workflows-practical-insights-by-michal-czmiel","type":"blog","slugType":null,"category":null,"additionalCategories":["GenAI","Ideas","Tech"],"url":null},"author":"Roksana Kaczmarska","authorAdditional":"","box":{"content":{"title":"Beyond the hype: real-world AI development workflows","tileDescription":"Beyond the Hype: Discover practical AI development workflows and real-world insights shared by Michał Czmiel in this episode","coverImage":"https://res.cloudinary.com/de4rvmslk/image/upload/v1759486280/Group_1000005282_fkgnjn.png"},"coverImage":null}},"id":"c1c19df9-b2af-5482-9ef5-d3c4b70b86e8"}},{"node":{"excerpt":"","fields":{"slug":"/blog/anna-zarudzka-joins-program-board-of-infoshare-katowice-for-the-second-time/"},"frontmatter":{"title":"Anna Zarudzka joins Program Board of Infoshare Katowice for the second time","order":null,"content":[{"body":"This year’s edition will take place on **November 24–25 at the International Congress Centre in Katowice**. The program will feature seven thematic stages, six side events, and numerous networking opportunities. Attendees can expect discussions on artificial intelligence, automation, cybersecurity, and the latest trends in leadership and business growth.\n\nAs a member of the Program Board, Anna Zarudzka will help shape the conference agenda, ensuring the topics reflect **the current challenges and opportunities in the tech and business sectors.** \n\nInfoshare Katowice 2025 is set to be one of the region’s most significant tech events of the year."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1759478483/infoshare-eventshooters-pl-202411261707-DSC00303_tqvwcj.jpg","lead":"Anna Zarudzka, co-founder and co-CEO of Boldare, **has been invited for the second time to join the Program Board of Infoshare Katowice 2025.** Infoshare is the largest tech and business conference in Central and Eastern Europe, attracting hundreds of industry leaders, innovators, and experts each year.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-10-03T07:46:03.037Z","slug":"boldare-at-infoshare-2025","type":"blog","slugType":null,"category":null,"additionalCategories":["News"],"url":null},"author":"Magdalena Chmiel","authorAdditional":"","box":{"content":{"title":"Anna Zarudzka joins Program Board of Infoshare Katowice","tileDescription":"Boldare co-founder Anna Zarudzka joins the Program Board of Infoshare Katowice 2025, Central and Eastern Europe’s largest tech and business conference. Learn about the event, key topics, and Boldare’s role in shaping innovation.","coverImage":""},"coverImage":null}},"id":"7e3e1a88-541e-5df9-9837-4ec01baecdd3"}},{"node":{"excerpt":"","fields":{"slug":"/blog/how-to-use-mcp-and-ai-to-speed-up-testing-in-your-digital-product/"},"frontmatter":{"title":"How to use MCP and AI to speed up testing in your digital product? Insights from Milena Cylińska","order":null,"content":[{"body":"**Piotr:** Hi, I'm Piotr, co-CEO of Boldare. You’re watching Agile Product Builders Tech Edition, a 25-minute series for today’s product builders. This time, we might stretch the time box a bit because MCP is best understood when you see it in action. Our Swiss guests are going to show us that, so I hope you'll forgive the slight delay.\n\nIn this episode, we’re diving into the world of quality assurance in the age of AI. In particular, we explore how Model Context Protocol (MCP), a new API for LLMs, is reshaping the way QA engineers test and ensure product quality.\n\nOur guest today is Milena Cylińska, a QA engineer with over 10 years of experience, and someone I’ve had the pleasure of collaborating with on several projects. What makes Milena stand out is her mission-driven approach. She’s not here to build massive testing departments or create bottlenecks. Instead, she embeds quality into every step of the software development lifecycle, and above all, she’s a true builder.\n\nSo, Milena, it’s great to have you here. Welcome to the show.\n\n**Milena:** Thank you for the invite.\n\n**Piotr:** Let’s start simple. Could you briefly introduce yourself and tell us how you became interested in experimenting with AI tools?\n\n**Milena:** Sure. I think you gave a pretty good introduction, so thanks for that. I’m basically a QA engineer, and I’ve been helping companies with their QA processes, test automation, and all the magic that happens around assuring quality.\n\nWhat brought me to AI… the short story is that I realized Playwright MCP existed, and ChatGPT before that. The longer story is that my journey started in 2018, when I went to a QA conference about AI with my friends. I must admit I wasn’t very excited at the time. Listening to all those speeches, I didn’t see it coming that we could actually use AI on a daily basis as we do nowadays. Back to the main topic—my real hands-on use of AI in daily tasks began when Playwright MCP was released.\n\n## What Model Context Protocol is and why it matters\n\n**Piotr:** Okay, so the Playwright MCP was this “aha” moment. That’s really nice. For those who aren’t familiar with Model Context Protocol, could you describe it in simple terms? How would you explain it to other QA engineers or engineering managers?\n\n**Milena:**\n\n> People like to call it the “USB-C” or “OpenAI standard” that connects LLMs with different tools. By different tools, I mean it could be a database, a browser, or even just another app. This connection allows LLM models to actually perform tasks for us.\n\nFor example, if you picture ChatGPT, you can ask questions and get answers, but ChatGPT itself won’t, say, go and book a flight for you because it’s missing this connection. That’s exactly what MCP does—it’s basically the connector between AI and other tools.\n\n## How AI agents support testing and automation\n\n**Piotr:** Okay, so the Playwright MCP was this “aha” moment. That’s really nice. For those who aren’t familiar with Model Context Protocol, could you describe it in simple terms? How would you explain it to other QA engineers or engineering managers?\n\n**Milena:** People like to call it the “USB-C” or “OpenAI standard” that connects LLMs to different tools. By different tools, I mean it could be a database, a browser, or even just another app. This connection allows LLM models to actually perform tasks for us.\n\nFor example, if you picture ChatGPT, you can ask questions and get answers, but ChatGPT itself won’t, say, go and book a flight for you because it’s missing this connection. That’s exactly what MCP does—it’s the connector between AI and other tools.\n\n**Piotr:** So, can you tell us how this works in your daily QA workflow?\n\n**Milena:** Sure. I actually have a demo for you—let me share my screen. We’re going to see how MCPs work and how we can embed them in QA processes. This is a very simple example because we’ll be testing my own website.\n\nI won’t show you how to install everything, but it’s quite simple. In VS Code, under Extensions, there’s a section for MCP servers. I’ve installed three that we’ll be using, but you can also browse many others. Highly recommended—it’s easy to set up. This was one of the things that drew me into experimenting with AI: how simple it is to get an MCP running.\n\nFor this demo, we’ll be using three MCPs: Playwright MCP, GitHub MCP, and Linear MCP, to automate a basic QA flow. Normally, we start by searching for a Jira ticket (or any project management ticket), write test cases for it, explore the app manually, automate the test cases later, and finally commit changes and create a PR.\n\n**Piotr:** So basically, from the IDE you’re showing, you can access GitHub, Linear, and Playwright through LLMs—all in one enhanced environment?\n\n**Milena:** Exactly. For this, I “hired” two agents. One is a manual tester, who creates test cases, does exploratory testing, and files bug tickets if necessary. The other is a Playwright tester, our test automation agent.\n\nWe define the agents in a simple MD file, specifying their roles, tasks, and the tools they can use. For example, the manual tester uses the three MCPs and can also search or edit the repository. I also include guidance to prevent hallucinations and ensure the output meets expectations, including security constraints like never revealing API keys.\n\nThe workflow is strict: first, the agent gathers context by reviewing the codebase, then uses Playwright to navigate the app. Next comes test planning with Linear to gather acceptance criteria and convert them into test scenarios. Then exploratory testing is performed, including edge cases, followed by test case design. Finally, any bugs found are checked against existing tickets to avoid duplicates.\n\nThe second workflow is for test automation. The agent converts manual test cases into Playwright scripts, following best practices and the page object pattern. The agent also stabilizes the tests by running them, detecting flakiness or incorrect locators, and fixing issues autonomously. After all tests run, the agent commits the changes and creates a PR.\n\n**Piotr:** So basically, you could go to the Swiss Alps, enjoy the sun, and the agents would do the work for you.\n\n**Milena:** Yes, kind of—but let’s see how it works. I prepared a quick prompt, and on the right side, you can choose which agent to run. We start with the manual tester, who gathers context, writes test cases, and does exploratory testing. I set the workflow to run only for the home page.\n\nWhen the agent runs, it asks for permission to use the MCPs. You can allow each action individually, for the session, or always. I clicked “allow,” and it opened my website and took a screenshot, as I instructed. Then it searched Linear for the ticket with “home” in the name, retrieved context, and converted the acceptance criteria into test scenarios, flagging any missing context.\n\nNext, the exploratory testing should run automatically, navigating the app and testing inputs, like mandatory fields. It also takes screenshots, which are saved in the Playwright MCP folder. The findings from exploratory testing are collected, and the workflow moves to test case design.\n\n## Boosting Efficiency\n\n**Piotr:** It’s still running while it works, right? You know, it still amazes me that you can reach different contexts and have everything work together. Can you tell us how much time this approach saves?\n\n**Milena:** It really depends. You do need some pre-work, like making sure your documentation is ready so the agent gets proper context—that’s very important. From my perspective, because I work on many projects with many companies, there’s often a similar onboarding process for each project.\n\nTasks like this—getting context, creating documentation, writing test cases, and automating them—can be 60–70% faster. Sometimes I save days. QA engineers would agree that creating documentation, especially test cases, is very time-consuming and can be a bit boring.\n\nI treat this as a base. It allows me to focus on edge cases, talking with the business to understand context, participating in user interviews, and getting closer to what users actually need. These base test cases would apply to maybe 95% of the websites I test, while I can focus on the things that are very specific to the business.\n\n**Piotr:** Yeah, and you mentioned documentation. It really needs to be up to date. Nowadays, developers are using documentation too, and some are even automating it. With AI, we can share artifacts more easily and focus on things like edge cases, just as you said.\n\nHow AI shifts the QA role to higher-level decision making\n\n**Piotr:** So, what do you think? Is it possible that in a few years your job might no longer involve actual testing, and you’d just be orchestrating agents through MCP or similar AI tools?\n\n**Milena:** Honestly, maybe if I were only testing my own website, that could work. It’s a cool approach and saves lots of time, letting you focus on more creative tasks. But I don’t think most projects today have the capacity to fully apply this approach. Often, as you mentioned, documentation is missing.\n\nWith AI-assisted flows, we need to shift our work left in the software development lifecycle. We have to focus more on documentation, refine requirements, and keep tickets organized. Testing is only part of the QA role—it gives some breathing room, but I still think manual checks are important. After all, we’re creating software for people, and you want to maintain that human touch. Development might be easier for AI to take over, but with testing, I’m still hesitant.\n\n**Piotr:** Okay, so ownership remains with you, and you can’t fully let it go. Also, as you mentioned, most of the work requires context and documentation before automation. When that’s in place, do you feel your QA role becomes more strategic, or is it just faster work with the tool?\n\n**Milena:** I’d say it’s both.\n\n> It’s definitely faster, but it’s also more strategic. I focus on reviewing what AI produces, and I spend more time on the business side—requirements and documentation.\n\nEven when I create chat modes for the agents, you still need skills beyond test automation: test management, best practices, and coding knowledge—but from a supervisory perspective, overseeing the process. So yes, it’s definitely strategic. And of course, it’s much faster to review AI outputs than to create everything from scratch.\n\n## Getting Started with MCP \n\n**Piotr:** Okay, last question—if someone has never tried MCP with Playwright or other tools, what’s your advice? How should they start?\n\n**Milena:** I think the way I started worked well, so I’d recommend it. Definitely try VS Code and experiment with Playwright MCP. It’s a really interesting experience. Playwright MCP also has tons of YouTube videos and other learning materials you can reuse, so that would be my go-to starting point.\n\n## SUMMARY & KEY takeaways:\n\nThe conversation with Milena highlighted how AI, through Model Context Protocol (MCP), is reshaping the daily life of QA engineers. By connecting AI models to tools like Playwright, GitHub, and Linear, MCP allows teams to automate repetitive testing tasks, reduce errors, and focus on more strategic, high-value work. The demo showed not just speed gains, but also how AI agents can assist in planning, exploratory testing, and even creating automated scripts, all while requiring careful setup and human oversight.\n\n**Key Takeaways:**\n\n* **MCP Enables Integration:** Connects AI models with multiple QA tools for seamless workflow.\n* **Automation of Routine Tasks:** Agents handle exploratory testing, test case creation, and script automation.\n* **Time Savings:** Processes can be completed 60–70% faster, freeing engineers for strategic activities.\n* **Strategic Oversight Remains Key:** Human judgment is crucial for edge cases, documentation, and final QA decisions.\n* **Accessible Learning Path:** Hands-on experimentation in VS Code and Playwright MCP is the best way to get started.\n\nOverall, Milena’s insights reveal that AI is not replacing QA engineers but amplifying their impact. By handling repetitive tasks, it allows engineers to focus on creative problem-solving, business context, and user-centered testing, keeping quality assurance both efficient and human-driven.\n\n**Piotr:** Great, thank you. Milena, thank you for showing us how MCP works for QA engineers. It’s really fascinating. I wish we had more time to see even more, but this glimpse alone is impressive. It’s amazing to see how many aspects today’s tools can reach and how they help us work faster and focus on what truly matters.\n\nThank you for sharing your insights with us. If anyone in the audience is curious about MCP, let us know—we can help, and Milena can point you to additional resources if needed.\n\nThank you very much! This was Agile Product Builders Tech Edition. I’m Piotr, and this was Milena. See you next time. Bye!"}],"job":null,"photo":null,"slug":null,"cover":"","lead":"In this episode of **Agile Product Builders Tech Edition**, Piotr, co-CEO of Boldare, explores the evolving landscape of quality assurance (QA) in the age of AI with Milena Cylińska, a seasoned QA engineer with over a decade of experience. The discussion centers on Model Context Protocol (MCP), an innovative API connecting large language models (LLMs) with various tools to streamline QA workflows. Milena demonstrates **how AI-powered agents can assist in manual and automated testing, helping QA engineers focus on strategic tasks while maintaining high-quality software development.**\n\n","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-09-30T13:09:54.535Z","slug":"how-to-use-mcp-and-ai-to-speed-up-testing-in-your-digital-product","type":"blog","slugType":null,"category":null,"additionalCategories":["How to","Ideas","Strategy","Tech","Video"],"url":null},"author":"Roksana Kaczmarska","authorAdditional":"","box":{"content":{"title":"How to use MCP and AI to speed up testing?","tileDescription":"Discover how MCP and AI can accelerate testing in your digital product, reduce errors, and boost quality while saving time and resources","coverImage":"https://res.cloudinary.com/de4rvmslk/image/upload/v1759238423/Group_1000005075_provp1.png"},"coverImage":null}},"id":"8fe5e979-4a92-57b4-aedb-e91d10cfd271"}},{"node":{"excerpt":"","fields":{"slug":"/blog/how-ai-helped-us-create-project-documentation-80-faster-a-practical-guide/"},"frontmatter":{"title":"From 60 minutes to 10: speeding up documentation with AI","order":null,"content":[{"body":"\\*This article was created by **Przemysław Polak, Szymon Kopa, and Karol Kasprzak.***\n\nEvery software team knows the struggle: balancing clean, efficient code, hitting deadlines, and keeping documentation up to date. Writing docs for every new feature often feels endless, draining time and energy that could otherwise go into actual development. The more effort spent on writing descriptions, the less is left for building features that add real value. \n\n**So, how can we ease the workload without losing quality?** \n\nWe looked into AI solutions to optimize the process. The outcome was impressive: an 80% reduction in documentation time, five times higher accuracy and relevance, and all of it delivered at a fraction of the original cost.\n\n## Coding vs. documentation\n\nOur client had strict expectations. Documentation was an essential KPI, and even though the process was standardized, it caused pain points. The team relied on Word, which limited collaboration and made version control complicated. Preparing docs for a single feature took 45–60 minutes — a huge inefficiency when the focus was meant to be speed and delivery.\n\n## How we automated documentation with AI?\n\nAI turned out to be the ideal way to streamline documentation and improve efficiency. We designed a setup where AI handles most of the repetitive groundwork. Here’s the breakdown:\n\n1. **Generating content from code:** AI tools, such as advanced code completion, read through the source code (controllers, modules, cron jobs, adapters) and automatically produce documentation. Instead of manually writing explanations for every feature, the team now gets auto-generated drafts to refine and improve.\n2. **Markdown for flexibility:** We moved away from heavy Word files and switched to Markdown. It’s lightweight, version-friendly, and works perfectly with repositories. Edits appear immediately, collaboration is smoother, and updates are faster. With AI added into the workflow, managing documentation became simpler and far less error-prone.\n3. **PDF exports for client delivery:** Using a script, we automatically convert Markdown into PDF, formatted exactly as the client requires. This eliminates manual styling and ensures consistent results.\n\n## The benefits we achieved\n\nThe most visible change came with time. Preparing documentation used to be a slow, manual process, often stretching to nearly an hour for a single feature. **Now, the same work can be completed in just 5–10 minutes.** That shift means developers can dedicate the majority of their time to coding, while documentation becomes a quick, almost seamless step in the workflow rather than a roadblock.\n\nThe financial impact was just as striking. **Because the process is so much faster and lighter, the cost of producing documentation for one feature dropped to under one dollar.** For projects with dozens or even hundreds of features, this translates into significant long-term savings.\n\nBuilding and testing the system didn’t require a major investment either. **We managed to design, implement, and validate the entire approach for only $15–30.** In other words, the solution paid for itself almost immediately, both in terms of money and developer hours.\n\nBut perhaps the most valuable outcome was the new starting point AI provided. Instead of staring at an empty document, developers begin with an automatically generated draft based on the code itself. **These drafts may not be final, but they offer a strong foundation that can be quickly refined and customized.** That shift reduces friction, lowers the mental load, and ensures that the team can consistently produce documentation that matches the project’s needs without slowing down development.\n\n## Where AI still falls short?\n\nWhile the results were impressive, the system is not flawless. Sometimes the AI leans too heavily on detail, producing descriptions that feel longer than they need to be. In this project, the strict formatting rules imposed by the client also limited how useful the documentation was for day-to-day work within the team. Another challenge is timing: the best documentation comes when it’s generated in real time, as the feature is being built. If that step happens too late, the quality quickly suffers.\n\n## What’s next for AI-driven documentation?\n\nThe journey doesn’t end with our first success. Building on what we’ve achieved, we see clear opportunities to take this approach further. **One of our goals is to make documentation fully automatic — every change in the codebase would immediately generate matching documentation, much like continuous integration ensures that new code is instantly tested and deployed.**\n\nWe also want to better understand how teams actually use documentation. By monitoring usage patterns, we can fine-tune the content so it reflects what the team truly needs, rather than simply following a rigid template.\n\nFinally, **our vision is to make this system scalable beyond a single project.** By adapting the solution for different contexts and organizations, we hope to encourage a broader adoption of AI in development workflows, allowing more teams to benefit from faster, more reliable documentation.\n\n## Conclusion\n\nThis case shows how AI-driven coding tools can help teams save time, cut costs, and raise the overall standard of their work. **When repetitive tasks are handled by automation, developers are free to focus on what matters most — building features and solutions that bring real value to clients and users.** Technology in this context doesn’t just support the process; it actively accelerates it. \n\nBringing AI into development workflows means less time lost to routine work, more room for creativity, and faster delivery cycles. **The partnership between human expertise and AI capabilities is opening the door to more efficient, innovative, and higher-quality software development**. Looking ahead, AI will continue to grow as a trusted partner in coding, making the role of software engineers even more dynamic and impactful."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1759149481/Group_1000004978-2_ckjnps.png","lead":"**AI is everywhere** – but what’s it really like **on the frontlines of AI implementation**? Get into the **daily thoughts and challenges faced by AI engineers** – the real stuff that happens when **AI meets digital products**.\n\n**Weekly AI Bites** is a series that gives you **direct access to our day-to-day AI work**. Every post comes straight from our **team’s meetings and Slack**, sharing **insights, tests, and experiences** we’re applying to **real projects**. **What models are we testing, what challenges are we tackling, and what’s really working in products?** You’ll find all of this in our bites. Want to know **what’s buzzing in AI**? Check out **Boldare’s channels every Monday** for the latest **weekly AI Bite**.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-09-29T12:23:24.044Z","slug":"speeding-up-documentation-with-AI","type":"blog","slugType":null,"category":null,"additionalCategories":["How to","GenAI","Digital Product","Strategy"],"url":null},"author":"Karol Kasprzak","authorAdditional":"","box":{"content":{"title":"How AI helped us create project documentation 80% faster? A practical guide","tileDescription":"Discover Weekly AI Bites: real insights from Boldare’s AI engineers, tests, and challenges in digital products. New posts every Monday.","coverImage":""},"coverImage":null}},"id":"82ccc1a4-b29a-5c88-b81e-90413690f4f3"}},{"node":{"excerpt":"","fields":{"slug":"/blog/top-7-artificial-intelligence-companies-in-2025/"},"frontmatter":{"title":"Top 7 Artificial Intelligence Companies in 2025","order":null,"content":[{"body":"## [Boldare – Human-first, AI-augmented digital product builders](https://www.boldare.com)\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1759139538/65_smol6v.png)\n\nWe develop digital products at all stages — from MVPs and achieving product-market fit to scaling platforms and expanding into new markets. Our team also handles complex engineering challenges, including system migrations, legacy modernization, architecture optimization, and large-scale integrations.\n\nAI accelerates our workflow by 20–40%, improving quality, testing, and project visibility — while maintaining high craftsmanship. We use AI daily to work faster without compromising ownership or engineering excellence.\n\nDesign is central to our approach. Designers collaborate closely with developers to deliver intuitive, elegant products — and when design is already defined, we integrate directly with your design system for speed and consistency.\n\nFounded in 2004, Boldare has successfully delivered digital products through multiple market shifts. Trusted by global brands like BlaBlaCar, Bosch, and Decathlon, we especially support mid-sized companies, such as Sonnen, Prisma, and e.l.f. Cosmetics, helping them scale intelligently, modernize technology, and grow confidently.\n\n### Boldare – Services\n\n**[Software Development:](https://www.boldare.com/services/software-development-outsourcing/)** We build digital products at every stage, from MVPs to fully-scaled platforms and market expansion.\n\n[**Generative AI**:](https://www.boldare.com/services/ai-software-development-consulting/) AI accelerates development, boosts product performance, and streamlines processes.\n\n**[Digital Design:](https://www.boldare.com/ux-ui-design-consulting-services/)** User-focused design seamlessly integrated with development for elegant, functional products.\n\n**[Product Strategy & Innovation:](https://www.boldare.com/services/product-innovation-and-strategy/)** We help refine offerings to meet market needs and long-term goals.\n\n[**DevOps & Infrastructure:** ](https://www.boldare.com/services/devops-consulting-services/)Reliable, scalable infrastructure with optimized deployment pipelines.\n\n**[Quality & Testing:](https://www.boldare.com/services/testing-and-quality/)** Rigorous QA ensures robust products across platforms.\n\n**[Consulting & Scaling:](https://www.boldare.com/services/consulting-and-scaling)** Guidance on tech modernization, scaling, and growth strategies.\n\nBoldare combines AI expertise with full-cycle product development, helping clients innovate, scale, and deliver high-quality digital solutions.\n\n### Why Choose Boldare?\n\n* AI-first expertise: We leverage generative AI to accelerate development, enhance product performance, and streamline complex processes.\n* Full-cycle digital products: From MVPs to large-scale platforms, Boldare covers every stage of product development.\n* Human-centered approach: Our designers and developers work together to create intuitive, elegant, and user-focused products.\n* Proven track record: Since 2004, we’ve helped global brands and mid-sized companies innovate, modernize, and scale confidently.\n* Tailored solutions for growth: Consulting, DevOps, testing, and strategic guidance designed to meet your unique business goals.\n* Quality & reliability: Rigorous QA and project management ensure robust products that perform across platforms and environments.\n\n## [STX Next](https://clutch.co/profile/stx-next)\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1759140343/Projekt_bez_nazwy-75_sdexyo.png)\n\nSTX Next is an international digital engineering consultancy focused on delivering Data & AI solutions, integrated into modern Cloud infrastructure with exceptional UX & Design. The business services a global customer base through a flexible, multi-location nearshoring model from two delivery centers in Poland and Mexico with a deep, educated, cost-effective talent pool. With nearly 20 years of expertise and a talented team of 500 professionals, STX has successfully delivered over 1,000 projects.\n\nSTX has been fundamentally built on a strong Python foundation, capitalizing on its wide range of applications—from web development and data analysis to AI—that have been instrumental in STX's growth.\n\nBeing an ISO-certified business allows them to adhere to international data security standards and regulatory compliance, ensuring consistent, efficient, and high-quality processes and services.\n\n### Services\n\n* AI & Data Solutions\n* Cloud Infrastructure Integration\n* UX & Design\n* Python-based Development\n* Nearshoring Services\n\n### Why Choose STX Next?\n\n* Strong Python foundation for AI and data solutions.\n* ISO-certified for data security and compliance.\n* Multi-location nearshoring model for flexibility.\n* Proven track record with over 1,000 projects delivered.\n* Expertise in modern cloud infrastructure and UX design.\n\n## [Neoteric ](https://clutch.co/profile/neoteric)\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1759139539/67_xqmfgt.png)\n\nNeoteric focuses on AI-driven software and digital transformation. They develop machine learning models, AI-powered applications, and scalable platforms for startups and enterprises. Their team combines design, development, and AI expertise to deliver end-to-end digital solutions.\n\n### Services:\n\n* AI & Machine Learning Development\n* Custom Software Development\n* Digital Transformation Consulting\n* UX/UI Design\n* Cloud Solutions\n\n### Why Choose Neoteric?\n\n* Expertise in AI and machine learning development.\n* End-to-end digital transformation services.\n* Strong focus on UX/UI design and user experience.\n* Proven success with startups and enterprises.\n* Agile and flexible development processes.\n\n## [Coherent Solutions ](< https://clutch.co/profile/coherent-solutions>)\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1759139539/68_xyef3q.png)\n\nCoherent Solutions is a leading global digital engineering company with a presence in 10 countries and a team of over 2,000 skilled engineers. By combining the latest technologies, top-notch talent, and streamlined processes, they help clients achieve their business goals and stay ahead in today’s competitive digital landscape.\n\n### Services\n\n* Custom Software Development\n* IT Staff Augmentation\n* UX/UI Design\n* Web Development\n* Enterprise App Modernization\n* CRM Consulting and SI\n* DevOps Managed Services\n* Mobile App Development\n* Application Testing\n* Architectural Design\n* API Development\n* Cloud Consulting & SI\n* Graphic Design\n\n### Why Choose Coherent Solutions?\n\n* Over 30 years of experience in digital engineering.\n* Presence in 10 countries with a global team.\n* Expertise in a wide range of technologies and services.\n* Proven track record with clients across various industries.\n* Strong focus on delivering high-quality, scalable solutions.\n\n## [Digica ](https://clutch.co/profile/digica)\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1759139540/69_mv5qox.png)\n\nDigica is an independent Data Science and Artificial Intelligence company. They help clients by applying the latest Data Science and AI tools and techniques to their systems and products. This leads to highly customized solutions for customers. Based on their own extensive research program, their experienced team ensures that the best available approach is applied in any situation.\n\nThey have deep expertise in the field of image processing, including Deep Learning for Computer Vision and leading-edge commercial implementation of Synthetic Imaging. Their work also covers the fields of Large Language Models, Audio Analysis, and Predictive Maintenance.\n\n### Services\n\n* AI & Machine Learning Development\n* Deep Learning for Computer Vision\n* Synthetic Imaging\n* Audio Analysis\n* Predictive Maintenance\n* Data Science Consulting\n\n### Why Choose Digica?\n\n* Deep expertise in AI and machine learning.\n* Specialization in computer vision and synthetic imaging.\n* Strong research-driven approach to problem-solving.\n* Proven success in delivering customized AI solutions.\n* Focus on innovation and cutting-edge technologies.\n\n## [Deviniti](https://clutch.co/profile/deviniti)\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1759139542/70_anim2d.png)\n\nDeviniti focuses on intelligent software solutions for enterprise clients, integrating AI to optimize business processes. They provide consulting, custom development, and automation tools that support scaling and digital growth.\n\n### Services\n\n* AI & Machine Learning Solutions\n* Enterprise Software Development\n* Business Process Automation\n* IT Consulting\n* Custom Application Development\n\n### Why Choose Deviniti?\n\n* Expertise in delivering AI solutions for enterprises.\n* Strong focus on business process optimization.\n* Proven track record with large-scale projects.\n* Ability to scale solutions to meet business needs.\n* Comprehensive approach from consulting to development.\n\n## [Addepto ](https://clutch.co/profile/addepto)\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1759139543/71_hstrag.png)\n\nAddepto specializes in machine learning, data science, and AI consulting. Their services include predictive analytics, NLP applications, and AI model deployment, helping companies leverage data-driven insights for strategic decisions.\n\n### Services:\n\n* Machine Learning Development\n* Data Science Consulting\n* AI Model Deployment\n* Predictive Analytics\n* Natural Language Processing (NLP)\n\n### Why Choose Addepto?\n\n* Specialization in machine learning and data science.\n* Expertise in deploying AI models for real-world applications.\n* Strong focus on predictive analytics and NLP.\n* Proven success in delivering data-driven solutions.\n* Ability to tailor solutions to specific business needs.\n\nThis Top 7 list highlights companies that combine AI expertise with digital product development, design, and consulting to deliver innovative, high-quality solutions. Whether scaling existing platforms or creating entirely new products, these firms are at the forefront of AI-driven transformation in 2025."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1759137948/Group_26086317_bcejuf.png","lead":"**Artificial Intelligence** is transforming industries worldwide, and 2025 marks a new era of innovation, efficiency, and digital transformation. Companies leveraging AI to accelerate development, enhance product performance, and deliver cutting-edge solutions are leading the way. In this ranking, **we highlight seven AI companies that stand out for their expertise, innovation, and impact.**","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-09-29T09:21:39.927Z","slug":"top-7-artificial-intelligence-companies-in-2025","type":"blog","slugType":null,"category":null,"additionalCategories":["Tech","GenAI","Ideas","Digital Product"],"url":null},"author":"Roksana Kaczmarska","authorAdditional":"","box":{"content":{"title":"Top 7 Artificial Intelligence Companies in 2025","tileDescription":"Discover the Top 7 Artificial Intelligence Companies in 2025, featuring Boldare, STX Next, Neoteric, Coherent Solutions, Digica, Deviniti, and Addepto. Learn how these leaders leverage AI, digital product development, and innovative strategies to drive growth and transform industries.","coverImage":""},"coverImage":null}},"id":"dfa82e64-921d-582b-bd5d-a1aa39ce2e1e"}},{"node":{"excerpt":"","fields":{"slug":"/blog/this-week-s-ai-bite-a-gift-for-warsaw-ai-community/"},"frontmatter":{"title":"This week’s AI Bite: a gift for Warsaw AI Community","order":null,"content":[{"body":"**Weekly AI Bites is a series that gives you direct access to what’s happening in our day-to-day AI work.** Every post comes straight from our team’s meetings and Slack, sharing insights, tests, and experiences we’re actively applying to real projects. \n\nWhat models are we testing, what challenges are we tackling, and what’s really working in products? You’ll find all of this in our bites. Want to know what’s buzzing in AI? \n\n**Check out Boldare’s channels every Monday for the latest weekly AI bite.**\n\n\n\n## **This Monday we’re sharing something special with the Warsaw AI community**\n\nAlongside our weekly AI Bites, we’ve got a treat for everyone hungry for real-world AI events. Oliver, our AI engineer at Boldare, created the **[Warsaw AI Events Calendar](http://lu.ma/warsaw-ai-events?utm_source=boldare_some)** — one place to stay updated on every meetup, workshop, and gathering in the city. No more missing out or browsing dozens of websites — just subscribe once and explore what’s next on Warsaw’s AI scene.\n\n## See a **personal invitation from Oliver**\n\n\n\n\n\nThat’s all for today — stay tuned and **be with us next Monday for another Weekly AI Bite**!"}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1758618759/Blog_2_vad9j7.png","lead":"AI is everywhere, **but what’s it really like on the frontlines of AI implementation?** Get into the daily thoughts and challenges faced by AI engineers – the real stuff that happens when AI meets actual digital products.","templateKey":"article-page","specialArticle":true,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-09-22T11:01:28.309Z","slug":"weekly-ai-bites-1","type":"blog","slugType":null,"category":null,"additionalCategories":["Tech"],"url":null},"author":"Magdalena Chmiel","authorAdditional":"","box":{"content":{"title":"Weekly AI Bites — Warsaw AI Events Calendar","tileDescription":"Discover Boldare’s Weekly AI Bites and explore the new Warsaw AI Events Calendar — your one-stop hub for all local AI meetups and workshops.","coverImage":""},"coverImage":null}},"id":"e884d579-4434-5130-8adc-b9531d56a529"}},{"node":{"excerpt":"","fields":{"slug":"/blog/top-7-ux-ui-design-and-development-companies-in-poland/"},"frontmatter":{"title":"Top 7 UX/UI design and development companies in Poland","order":null,"content":[{"body":"## Boldare\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1756733401/52_hrkjq3.png)\n\nBoldare is a Poland-based technology company with a team of over 70 professionals. Established in 2004, it has become a trusted European partner for building digital products that combine outstanding user experience with advanced engineering.\n\nThe company was born from the merger of two recognized organizations: Chilid, one of the leading UX/UI design agencies, and XSolve, an experienced software house known for scalable solutions. This fusion of creative design and strong engineering expertise laid the foundation for Boldare’s unique ability to deliver both visually compelling and technologically advanced digital products.\n\n### What Makes Boldare Stand Out?\n\n* User-first mindset – Every project begins with in-depth UX research to ensure solutions are intuitive, functional, and tailored to real user needs.\n* AI-powered design – By integrating artificial intelligence into design processes, Boldare predicts user behavior and develops products that go beyond expectations.\n* Innovation backed by research – The company invests in market analysis, user testing, and A/B experiments to ensure data-driven decisions and measurable outcomes.\n* High standards of quality – Boldare’s multidisciplinary team delivers secure, scalable, and beautifully designed applications that help businesses grow.\n\n### Beyond Design: Strong Engineering Foundations\n\nThanks to its XSolve legacy, Boldare also excels in development. The team builds scalable software products for global clients, covering everything from cloud platforms and APIs to mobile apps and enterprise-grade solutions.\n\n### Full Spectrum of Services\n\nBoldare supports organizations through end-to-end digital transformation with services such as:\n\n* Software Development – Secure, scalable, and customized software solutions.\n* Generative AI – Leveraging AI to enhance product design and functionality with data-driven insights.\n* UX/UI Design – Intuitive, user-centered, and visually appealing digital interfaces.\n* Product Innovation & Strategy – Defining product vision, roadmaps, and innovation strategies.\n* Project Management & Quality Assurance – Agile processes ensuring timely, high-quality delivery.\n* DevOps & Infrastructure – Cloud services, DevOps, and infrastructure scaling.\n* Consulting & Scaling – Guiding companies through digital transformation and growth.\n* Testing & Quality – Rigorous QA to ensure reliability, performance, and security.\n\n### Awards & Recognition\n\nBoldare has received international recognition, including:\n\n* [Lovie Award – Excellence in digital design](https://www.lovieawards.com/?utm_source=chatgpt.com)\n* [Indigo Award (Silver) – Creativity in UX/UI design](https://www.indigoaward.com/?utm_source=chatgpt.com)\n* [Webby Award Honoree – Innovation in digital experiences](https://www.webbyawards.com)\n* [CSS Design Award – Exceptional web design](https://www.cssdesignawards.com/?utm_source=chatgpt.com)\n* [German Design Award – Outstanding design achievements](https://www.german-design-award.com/en)\n* [NextGen Enterprise Award – Leadership in digital transformation](https://www.nextgen.com/company/awards?utm_source=chatgpt.com)\n* [Awwwards Honorable Mentions – Multiple acknowledgments for design excellence](https://www.awwwards.com/Boldare/submissions?utm_source=chatgpt.com)\n\n## Webview\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1758013188/60_tcoigm.png)\n\nClutch profile: [Webview](https://clutch.co/profile/webview)\n\nWebview is a Poland-based digital studio focused on building fast, user-friendly, and reliable interfaces. The company supports both startups and larger organizations, ensuring that products perform seamlessly across platforms and devices. Their strength lies in combining technical precision with modern, clean design.\n\nWith an agile approach and transparent communication, Webview positions itself as a dependable partner for businesses that want to deliver products quickly without compromising on quality.\n\n**Why choose Webview?**\n\n* Specialization in front-end development and UI design with a strong performance focus.\n* Agile workflows and quick iteration cycles suited for startups.\n* A proven track record in building scalable solutions for enterprises.\n\n## The Story\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1758013188/61_g5n1nk.png)\n\nClutch profile: [The Story](https://clutch.co/profile/story-ai-development-user-experience)\n\nThe Story is a design and development company that brings together human-centered UX design and artificial intelligence. By leveraging data analytics and machine learning, they create intelligent digital experiences tailored to real user needs.\n\nThe company is especially well-suited for organizations seeking innovation, as they focus not just on aesthetics but also on predictive and adaptive product behavior. This makes The Story an ideal choice for clients aiming to stand out in competitive, tech-driven markets.\n\n**Why choose The Story?**\n\n* Deep expertise in AI-powered design and intelligent user experiences.\n* Strong emphasis on UX research, usability, and product strategy.\n* A perfect partner for companies aiming to innovate and disrupt industries.\n\n## Phenomenon Studio\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1758013191/62_vmyl8z.png)\n\nClutch profile: [Phenomenon Studio](https://clutch.co/profile/phenomenon-studio)\n\nPhenomenon Studio is known for blending creativity with functionality. Their portfolio showcases projects for startups, scale-ups, and established global brands, all unified by sleek design and strong usability. The studio’s approach is holistic — from branding and product identity to UX/UI execution.\n\nTheir work stands out for being both visually appealing and business-driven. By aligning design decisions with business goals, they ensure products not only look great but also perform effectively in the market.\n\n**Why choose Phenomenon Studio?**\n\n* Creative, visually engaging design that makes products stand out.\n* Expertise in branding + product design for a consistent digital identity.\n* Experience delivering solutions that balance aesthetics, usability, and business value.\n\n## TechWings\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1758013191/63_lyxp72.png)\n\nClutch profile: [TechWings](https://clutch.co/profile/techwings)\n\nTechWings positions itself as a full-cycle product development partner. With expertise in both UX/UI design and advanced engineering, they deliver platforms and applications that scale with client needs. Their process combines design thinking with technical precision, ensuring every product is both user-centered and robust.\n\nFrom startups to global enterprises, TechWings supports clients through ideation, design, development, and deployment. They are recognized for building secure and scalable solutions across multiple industries.\n\n**Why choose TechWings?**\n\n* Comprehensive end-to-end services — from UX research to launch.\n* Proven expertise in software engineering and product scalability.\n* Focus on security, performance, and long-term sustainability.\n\n## Widelab\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1758013193/64_ztpgpv.png)\n\nClutch profile: [Widelab](https://clutch.co/profile/widelab)\n\nWidelab is one of Poland’s most recognized design-first studios. Known for their clean, detail-oriented approach, they deliver seamless user experiences supported by well-structured design systems. Their team has been praised for delivering polished products that prioritize usability, accessibility, and consistency.\n\nAcknowledged by Clutch as a “game-changing” UX agency, Widelab is an excellent choice for companies that value premium design and flawless execution.\n\n**Why choose Widelab?**\n\n* Strong focus on design systems for scalable and consistent UX.\n* Attention to detail and a reputation for pixel-perfect execution.\n* Recognized as a leading design-first agency in Poland.\n\n## Merge Rocks\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1758013196/65_ltllqe.png)\n\nClutch profile: [Merge Rocks](https://clutch.co/profile/merge-rocks)\n\nMerge Rocks is a versatile digital studio that delivers both UX/UI design and development services. Their portfolio covers industries such as SaaS, e-commerce, and digital platforms, showcasing adaptability and technical range.\n\nThe company emphasizes collaboration and co-creation, ensuring clients remain engaged throughout the process. This approach leads to digital products that are not only visually attractive but also well-aligned with business strategies.\n\n**Why choose Merge Rocks?**\n\n* Versatility in handling projects across diverse industries.\n* Balanced expertise in design and engineering.\n* A collaborative approach that ensures solutions meet client goals.\n\n## Summary\n\nPoland has established itself as a leading hub for UX/UI design and software development, offering innovative, scalable, and user-centered digital solutions for clients worldwide. Companies like Boldare, Webview, The Story, Phenomenon Studio, TechWings, Widelab, and Merge Rocks combine creative design, technical expertise, and agile processes to deliver high-quality products across diverse industries, from fintech and healthtech to e-commerce and SaaS. These firms are recognized for their strong focus on usability, cutting-edge technologies, and client-centered approaches, ensuring that every solution is not only visually compelling but also robust, scalable, and aligned with business objectives. With extensive experience, award-winning design, and a commitment to innovation, Polish UX/UI and development agencies continue to set new standards in digital product excellence."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1758012658/Frame-3_qfvti0.png","lead":"Poland has become one of Europe’s leading hubs for **digital product design and development.** With a strong pool of creative talent and engineering expertise, Polish studios are delivering world-class solutions for startups and enterprises worldwide. **Here are seven standout UX/UI design and development companies you should know.**","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-09-16T08:49:53.960Z","slug":"top-7-ux-ui-design-and-development-companies-in-poland","type":"blog","slugType":null,"category":null,"additionalCategories":["Digital Product","Ideas","News"],"url":null},"author":"Roksana Kaczmarska","authorAdditional":"","box":{"content":{"title":"Top 7 UX/UI design and development companies in Poland","tileDescription":"Discover the top 7 UX/UI design and development companies in Poland. Explore award-winning Polish agencies like Boldare, Widelab, Momentum, and more, delivering innovative, scalable, and user-centered digital products for startups and enterprises worldwide.","coverImage":""},"coverImage":null}},"id":"62689318-8573-53f9-b4d4-69d96ffeda33"}},{"node":{"excerpt":"","fields":{"slug":"/blog/top-8-agile-software-development-companies-in-2025/"},"frontmatter":{"title":"Top 8 Agile software development companies in 2025","order":null,"content":[{"body":"## BOLDARE\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1754312768/41_aamthd.png)\n\n### Company Overview\n\n* Size: Nearly 100 employees\n* Founded: 2004\n* Website: [boldare.co](https://boldare.co/?utm_source=chatgpt.com)\n* Location: Gliwice, Poland\n\n### Services\n\n* Software Development: Supports all stages of product creation, from MVPs to full-scale platforms, including market expansion.\n* Generative AI: Speeds up development by 20–40%, improving quality, testing, and delivery without compromising craftsmanship.\n* Digital Design: Designers work alongside developers to craft user-friendly, visually appealing products or enhance existing client design systems.\n* Product Innovation & Strategy: Offers guidance on market fit, growth strategies, and long-term product planning.\n* DevOps & Infrastructure: Builds reliable, scalable systems with efficient deployment pipelines.\n* Consulting & Scaling: Helps with tech modernization, system integration, and business growth strategies.\n* Testing & Quality Assurance: Ensures high performance and reliability across all platforms.\n\n### Notable Clients\n\n* Sonnen (Germany): Developed a full digital ecosystem, including an EV charging app and customer portal.\n* Maxeon Solar Technologies: Delivered custom APIs, integrations, and UX improvements for a modern app experience.\n* Decathlon: Implemented digital innovations to enhance customer engagement and operations.\n* Bosch: Advanced product initiatives aligned with global innovation strategies.\n* BlaBlaCar: Scaled backend systems to support rapid user growth.\n* TeamAlert (USA): Transitioned from MVP to product-market fit, tripling user numbers.\n* Matic Services (UAE): Optimized platform, resulting in $3M funding and 10× B2B engagement.\n* Slimpay (France) & Takamol (Saudi Arabia): Built secure, scalable systems supporting fintech and public sector projects.\n\n### Why Choose Boldare?\n\nBoldare has over 20 years of experience delivering digital products that truly meet user needs - and Agile is at the core of how they work. Agile isn’t just a methodology here, it’s a company-wide culture that shapes everything from development to marketing to recruitment.\n\nWhat makes Boldare stand out:\n\n* Agile as a culture - Every team at Boldare works in an Agile way. Knowledge-sharing happens naturally within self-organizing “chapters,” ensuring that best practices flow across projects.\n* A partner, not just a vendor - Boldare doesn’t just build software — they help clients succeed with digital transformation and long-term growth, treating each project as if it were their own.\n* Flexibility in the face of change -Agile at Boldare means embracing change. Shifting goals or new market conditions are seen as opportunities to improve, not obstacles.\n* Strategy first - Before writing a single line of code, Boldare analyzes the business, competitors, and stakeholders. Their long-term strategies are designed to balance user needs with business goals, making them realistic, impactful, and actionable.\n* Expert teams - Boldare’s people are both technical and business experts, passionate about tackling complex, innovative projects.\n* User-centered solutions - Every product is designed around real user needs. The team avoids wasting resources on features that don’t add value, testing and iterating continuously in line with Agile principles.\n\n## SIMFORM \n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1756733401/53_fgo7b4.png)\n\n### Company Overview\n\n* Size: 1,000–9,999 employees\n* Founded: 2010 \n* Website: [simform.com](https://www.simform.com)\n* Location: Headquarters in Ahmedabad (India), with U.S. presence (Orlando, FL) and offices across multiple continents \n\n### Services\n\n* Product Engineering & Platforms: End-to-end digital solutions—from MVPs to full-scale enterprise products.\n* Cloud & DevOps Engineering: Scalable, secure cloud-native architectures. \n* Data Engineering: Intelligent data platforms and analytics. \n* AI/ML Engineering: Generative AI, machine learning models, and MLOps pipelines. \n* Digital & Experience Engineering: UX-led design and development for seamless user experiences. \n\n### Notable Clients\n\nSimform’s global partnerships include renowned brands such as Sony Music, PepsiCo, Hilton, Red Bull, Cisco, Santander, Boy Scouts of America, and more. \n\n### Why Choose Simform?\n\nSimform stands out for these reasons:\n\n* Agile co-Engineering Pods: Self-organizing, multi-skilled teams that collaborate like internal engineering squads, enabling rapid adaptation to evolving requirements. \n* Top Clutch Recognition: Ranked #3 globally in Clutch’s 2025 Spring Global Rankings for Custom Software Development (out of 41,856 firms) and holds #1 in AI and #5 in ML among thousands of competitors.\n* TrustRadius Top Rated: Highest-rated provider in Custom Software Development Services with a 9.1/10 score (2025). \n* Highly Reviewed & Trusted: Receives consistent praise for responsiveness, reliability, technical proficiency, and transparent communication. Ranked 10th globally by G2 among development firms in 2025\n\n## Apzumi\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1756733401/54_vdrn0v.png)\n\n### Company Overview\n\n* Size: 50–100 employees\n* Founded: 2013\n* Website: [apzumi.com](https://www.apzumi.com)\n* Location: Poznań, Poland \n\n### Services\n\n* Custom Software Development: Focused on delivering impactful solutions for startups and scale-ups.\n* HealthTech Solutions: Proven expertise in building regulatory-compliant applications like HIPAA-focused systems.\n* Agile Delivery: Emphasis on iterative, feedback-driven workflows.\n\n### Notable Recognition\n\n* Earned multiple Clutch 2024 Awards, including:\n* Clutch 100 Fastest Growth Companies\n* Top Health & Wellness App Developers\n* Top Software Developers\n* AR/VR Development Leader \n\n### Why Choose Apzumi?\n\nApzumi combines sector-specific know-how with Agile delivery principles. Clients value their transparency, responsiveness, and the ability to evolve products quickly—especially in sensitive domains like healthcare.\n\n## Accedia\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1756733402/55_r7mwvt.png)\n\n### **Company Overview**\n\n* **Size:** 50–249 employees\n* **Founded:** 2012\n* **Website:** [accedia.com](<>)\n* **Location:** Sofia, Bulgaria\n\n### **Services**\n\n* **Custom Software Development:** End-to-end software engineering and custom software creation\n* **Cloud Solutions:** Scalable and secure cloud-native architectures\n* **AI Adoption:** Implementing AI technologies to enhance business processes\n* **IT Consultation:** Strategic IT consulting to optimize operations\n\n### **Notable Clients**\n\n* Indeed\n* BigCommerce\n* Kardex Remstar\n* Unicepta\n* TrustRadius\n* Modulsystem\n\n### **Why Choose Accedia?**\n\n* Premier European IT services company specializing in technology consulting and custom software development\n* Recognized among Europe’s fastest-growing tech firms by Financial Times and Deloitte\n* Expertise in full-stack application development, cloud solutions, and AI adoption\n\n## Edvantis\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1756733404/56_xeqfmo.png)\n\n**Company Overview**\n\n* **Size:** 400+ employees\n* **Founded:** N/A\n* **Website:** [edvantis.com](<>)\n* **Location:** Lviv, Ukraine\n\n### **Services**\n\n* **Custom Software Development:** Tailored software solutions to meet unique business needs\n* **Managed Software Development Services:** End-to-end development and maintenance\n* **Full-Stack Web Development:** Expertise in both frontend and backend development\n* **Agile Project Management:** Implementing Agile methodologies for efficient project delivery\n\n### **Notable Clients**\n\n* Indeed\n* BigCommerce\n* Kardex Remstar\n* Unicepta\n* TrustRadius\n* Modulsystem\n\n### **Why Choose Edvantis?**\n\n* Global software engineering company with 400+ professionals in Central & Eastern Europe and the USA\n* Trusted by top-tier companies for delivering high-quality software solutions on time\n* Strong project management, technical expertise, and seamless integration with client teams\n\n## SoftServe \n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1756733405/57_kvrfho.png)\n\n### Company Overview\n\n* Size: 10,000+ employees\n* Founded: 1993\n* Website: [softserveinc.com](https://www.softserveinc.com/en-us)\n* Locations: Austin (TX), Wrocław (PL), Sofia (BG), Lviv (UA), Singapore, Fort Myers (FL), Westborough (MA)\n\n### Services\n\n* Custom Software Development: Tailored enterprise-grade software solutions.\n* Healthcare Technology Applications: Building applications for the healthcare sector.\n* Innovative Consulting: Technology and strategy consulting services.\n* AWS Consulting Services: Cloud solutions and architecture consulting on AWS.\n\n### Why Choose SoftServe?\n\nSoftServe is a global IT firm combining technological excellence with deep industry knowledge. With distributed teams worldwide, they provide flexible Agile software development adapted to clients’ specific business needs.\n\n## Fulcrum\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1756733407/58_bg1xpm.png)\n\n### Company Overview\n\n* Size: 10–49 employees\n* Founded: N/A\n* [Website: fulcrum.rocks](https://fulcrum.rocks/?utm_source=clutch.co&utm_medium=referral_profile)\n* Locations: New York (NY), Mandaluyong (Philippines)\n\n### Services\n\n* Fractional CTO & Software Development Services: Strategic and technical guidance alongside development.\n* AI, Data Science, and Machine Learning Expertise: Advanced analytics and AI solutions.\n* Certified Developers, Project Managers, and QA Specialists: Highly skilled teams ensuring quality.\n* Startup-Focused Experience: Expertise in fast-paced, dynamic startup environments.\n* Transparent & Partner-First Approach: Clear communication and collaboration with clients.\n\n### Why Choose Fulcrum?\n\nFulcrum supports startups and growing businesses not just with development, but also with strategic guidance. Their Agile and iterative workflows ensure solutions stay aligned with evolving business needs.\n\n## Nefter\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1756733410/59_pszbi9.png)\n\n### Company Overview\n\n* Size: 50–249 employees\n* Founded: 2014\n* Website: [nefter.com](https://nefter.com)\n* Locations: San Diego (CA), Guadalajara (Mexico), La Joya (Mexico)\n\n### Services\n\n* Custom Software Development: Tailored solutions for web and mobile platforms.\n* Staff Augmentation: Flexible team scaling to meet project demands.\n* AWS Consulting Services: Cloud strategy and implementation expertise.\n\n### Why Choose Nefter?\n\nNefter provides highly skilled engineers from Latin America, delivering quality solutions with cost efficiency. Their collaborative Agile approach ensures fast adaptation and reliable delivery for clients.\n\n## Summary\n\nAgile isn’t just a methodology — it’s a mindset. Unlike the traditional Waterfall model, which follows rigid, linear stages and delivers a product only at the end, Agile works in short, iterative cycles. Teams release small, functional pieces of software, gather real user feedback, and adapt quickly to changes.\n\nThe companies featured here excel in Agile because they embrace flexibility, collaboration, and continuous improvement:\n\n* They prioritize user needs over fixed plans.\n* They adjust to shifting market demands rather than sticking to a rigid roadmap.\n* They encourage cross-functional collaboration, where designers, developers, and business experts work as one unit.\n\nFrom startups racing to achieve product-market fit to enterprises transforming digitally, these top 8 firms deliver faster, smarter, and more resilient solutions — proving that Agile isn’t just faster than Waterfall; it’s future-ready by design."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1756732905/Frame-2_lhpaon.png","lead":"In today’s fast-moving digital world, businesses can’t afford to spend years building software that’s outdated by the time it launches. This is where **Agile** comes in. Agile isn’t just a buzzword,  it’s a way of working that values **speed, flexibility, and continuous improvement**. Instead of long, rigid development cycles, Agile teams work in short, focused iterations. They deliver small but valuable pieces of software, gather real user feedback, and adapt quickly to changes.\n\nThe result? **Better products, faster delivery, and happier users**. That’s why Agile has become the gold standard for software development in 2025. From startups racing to find product-market fit to global enterprises undergoing digital transformation, Agile helps teams stay innovative, responsive, and customer-centric. In this article, we highlight the **Top 8 Agile Software Development Companies in 2025**, firms that not only build great software but also embody the true Agile spirit.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-09-01T12:34:46.632Z","slug":"top-8-agile-software-development-companies-in-2025","type":"blog","slugType":null,"category":null,"additionalCategories":["Digital Product"],"url":null},"author":"Roksana Kaczmarska","authorAdditional":"","box":{"content":{"title":"Top 8 Agile software development companies in 2025","tileDescription":"Discover the top 8 Agile software development companies in 2025, offering innovative solutions, expert teams, and efficient project delivery.","coverImage":""},"coverImage":null}},"id":"a27fc04f-cbc4-5022-8290-94bc368444fa"}},{"node":{"excerpt":"","fields":{"slug":"/blog/how-a-global-beauty-brand-overcame-scalability-and-user-engagement-challenges-during-peak-traffic/"},"frontmatter":{"title":"How a global beauty brand overcame scalability and user engagement challenges during peak traffic?","order":null,"content":[{"body":"## Client profile\n\n* **Overview:** Global leader in affordable, vegan, and cruelty-free beauty products\n* **Industry:** Beauty & Cosmetics\n* **Size:** ~1,500 employees\n* **Reach:** Global (Headquarters in the United States)\n* **Mission:** To make beauty accessible to everyone with vegan and cruelty-free products at affordable prices.\n\n\n\n### What key challenges did the beauty brand face in optimizing its e-commerce platform for growth and security?\n\nE-commerce in the beauty industry is becoming increasingly important — according to a [Forbes ](https://www.forbes.com/sites/claraludmir/2025/03/28/social-and-e-commerce-now-drive-more-than-50-of-beauty-sales-globally/)report, e-commerce now accounts for over 50% of global beauty sales. This significant shift highlights the critical need for beauty brands to optimize their digital platforms.\n\nAs the beauty brand sought to strengthen its market position, two key challenges emerged:\n\n1. **Improving the frontend experience:** Their existing e-commerce storefront was functional but lacked the interactivity and modern aesthetic expected by today’s online shoppers. They wanted to implement a more engaging and intuitive user experience that would enhance customer satisfaction and drive conversions.\n2. **Scaling and securing the API:** With frequent high-traffic sales events such as Black Friday, the company needed a more resilient and scalable API to ensure seamless performance under peak loads while maintaining security and data integrity.\n\n\n\n## What was Boldare's approach?\n\n* ### Frontend redesign — implementing a more interactive experience\n\nBoldare’s team of frontend experts worked closely with the client to **implement a new design that would engage customers through modern UI patterns and dynamic interactivity.**\n\nThis shift to a more interactive storefront was powered by Salesforce Commerce Cloud, a headless e-commerce platform, using Salesforce's PWA Kit template. The goal was clear: **no compromise on performance while ensuring the new interface was intuitive and responsive across all devices.**\n\nOur team’s role wasn’t to design the visual look (which had already been defined), but **to take this new design and turn it into a fully functional storefront** that integrated seamlessly with the backend and provided customers with an optimized, frictionless shopping experience.\n\nThis transformation allowed the client to **improve the user journey while maintaining a robust and flexible frontend architecture** that was easy to scale and iterate on as business needs evolved.\n\n* ### API architecture — optimizing for scalability and security\n\nParallel to the frontend work**, Boldare’s Solution Architect** evaluated the client’s existing API infrastructure and pinpointed areas for improvement.\n\nThe team’s efforts focused on developing new, secure API endpoints that would better withstand the high volumes of traffic expected during peak sales events like Black Friday.\n\nOne of the key goals was to **optimize the API architecture to scale without compromising on security**, ensuring that sensitive customer data remained protected even under pressure. By refining the existing middleware, we were able to enhance the overall system’s performance, making it more reliable during traffic spikes, which significantly mitigated the risk of downtime or security breaches.\n\nWith these improvements in place, the brand could confidently handle larger, more complex transactions while ensuring that the platform was ready for future growth.\n\n## Effective time zone collaboration: Boldare’s expertise in managing global projects\n\nBoldare’s experience working with clients across different time zones, including the U.S., enabled a seamless collaboration with this global beauty brand. **Our clients consistently praise our ability to deliver projects smoothly despite geographic distances.**\n\nFor more insights on managing cross-time zone work with Boldare, [check out our YouTube channel, where Allan Willson from Team Alert ](https://youtu.be/LRyBohtWFdo?feature=shared)shares his experience of cross-ocean cooperation with Boldare.\n\n\n\n## Introduction: Setting the scene\n\n**Anna (Host):** Welcome everyone to our Agile Product Builders Community webinar. Today's session is part of our product series where we explore the complexities of building digital products in real-life contexts. And I'm Anna, and I will be your host today. Our topic is how to design digital services in multi-stakeholder environments—a space where designing digital services means aligning diverse stakeholders, often with competing mandates, while still keeping the user in focus. And I'm really happy today because I'm joined by Aleksandra Maslon, product designer and UX strategist at Boldare, a digital product company with deep experience in building products across complex multi-stakeholder environments.\n\nFor the past two years, Aleksandra has played a key role in designing and optimizing the user experience of a Saudi government platform that brings together investors, service providers, and public institutions. She also brings prior experience in research, user testing, and UX design for medical products. Her work gives her a broad perspective on how organizational, legal, and political factors influence product development. Aleksandra, it's great to have you with us today.\n\n**Aleksandra (Guest):** Thank you very much. I'm also really happy to be here with you speaking about this topic, and hopefully, everyone joining us today will get really important information that we can focus on.\n\n## The biggest challenges in multi-stakeholder environments\n\n**Anna:** Let’s start with a fundamental question: What are the biggest challenges in designing digital services and products in environments where multiple government bodies and private stakeholders are involved?\n\n**Aleksandra:** It’s maybe the fundamental question, but I think it’s the most important one. Why? Because the biggest challenge, in my opinion, is definitely cost coordination—both for technical reasons and cultural reasons, but still, it’s the communication and coordination in it.\n\nService design plays a crucial role in bridging the gaps between different points in the product design process. For example, one common challenge is the lack of alignment among stakeholders. Different institutions often have a totally different view of the product, different priorities, and sometimes even different processes they want to work with.\n\n> Different stakeholders and institutions have totally different views of the product, different priorities, and sometimes even different processes they want to work with\n\nAnother challenge we often face is fragmented systems, which lead to data silos. When thinking about different institutions, each one has its own system for storing and providing data. Bringing all of this together for a smooth flow of data is really difficult. So, we always need to consider this when starting to work on such a project.\n\n## Coordination and decision-making challenges\n\n**Anna:** That sounds complicated, especially when dealing with multiple systems and priorities.\n\n**Aleksandra:** Yes, it is. Another challenge I’d mention is decision-making. When we think about multi-stakeholder environments, getting easy approvals can be difficult. Multiple stakeholders have different layers of approvals, different responsibilities, and sometimes their approach to risk management is entirely different, which we need to take into account in the process.\n\nThis complexity can slow down not only design but also the entire delivery process.\n\n**Anna:** So, it seems like coordinating various levels of approval can be a real bottleneck in these environments.\n\n**Aleksandra:** Exactly. And also, when we talk about ownership, who takes responsibility for each step of the process is another challenge. When we work in a multi-stakeholder environment, we need to define who is responsible for what part of the process, especially when it comes to implementation and moving forward. In addition to all these, we also face political and policy barriers that can be specific to the industry or institution involved.\n\n## How consulting and tech partners can help\n\n**Anna:** Many companies seek help from consultants or tech partners. How can a consulting or tech partner effectively support a client navigating legal, political, and organizational constraints?\n\n**Aleksandra:** The key thing here is to understand that we’re not just delivering solutions. We’re guiding our clients through the entire process. Our role is not just to provide the product but to be a trusted guide in navigating the complexities of these environments.\n\nOne of the ways we support our clients is by mapping the constraints they face and creating a strategy around them. We need to help our clients understand the political landscape, the policies involved, and how these influence product development.\n\nWe also facilitate cross-stakeholder collaboration by organizing workshops or strategy sessions to bring everyone on the same page and align them with the product goals.\n\n> We need to act not just as a vendor, but as a trusted guide, demonstrating our ability to understand the political and policy context\n\n## Aligning stakeholders with artifacts\n\n**Anna:** What kinds of tools or artifacts have you found most useful for creating alignment among stakeholders with conflicting priorities?\n\n**Aleksandra:** When working in multi-stakeholder environments, it's not just about the documents. The tools should shape the communication and align teams. Starting with a vision statement for each stakeholder group helps clarify their expectations. We then bring these visions together into a product roadmap. A stakeholder map can also be helpful for understanding roles and relationships among the various stakeholders, especially in large groups. User personas are also critical when dealing with complex products serving different user groups.\n\n## The role of a product owner\n\n**Anna:** A proper product owner seems essential for managing all of this complexity. How important is the role of the product owner?\n\n**Aleksandra:** Yes, a strong product owner is crucial. Even if they need to seek approvals from others, they must take responsibility for decision-making. Having one person overseeing the entire process ensures that things move forward, even when complications arise.\n\n## The design system as an alignment tool\n\n**Anna:** Is a design system helpful for alignment between stakeholders?\n\n**Aleksandra:** Yes, a well-prepared design system is essential. It provides options and bridges the gap between different priorities. A good design system allows stakeholders to focus on the shared product strategy and common goals.\n\n## Final advice for product leaders\n\n**Anna:** Finally, what piece of advice would you give to someone stepping into product leadership in a multi-stakeholder environment for the first time?\n\n**Aleksandra:** Don’t rush. It’s not possible to have all the answers at the start. The most important thing is to focus on building trust and establishing a shared understanding of the process. The answers will come as you go through the process. Focus on the most critical aspects first, and everything else will fall into place.\n\n> Focus on the most important things first, and everything else will fall into place\n\n## Conclusion: Key takeaways for designing in multi-stakeholder environments\n\nDesigning digital services in environments where multiple stakeholders are involved requires a balance of communication, coordination, and careful planning. Key insights from Aleksandra Maslon's experience emphasize the importance of alignment, trust, and a strong product ownership model to navigate the complexities of multi-stakeholder projects. Some key takeaways include:\n\n* Stakeholder alignment is crucial for successful project execution.\n* Data silos and fragmented systems must be addressed early on to ensure smooth information flow.\n* Decision-making can be a bottleneck, but clear ownership and responsibility can help speed up the process.\n* A design system and artifacts like stakeholder maps and user personas help align goals and expectations.\n* A product owner who can guide the process and make critical decisions is vital.\n\nBy focusing on these core principles, product teams can better navigate the challenges of working in multi-stakeholder environments and create digital services that meet both user needs and stakeholder priorities."}],"job":null,"photo":null,"slug":null,"cover":"","lead":"Designing digital services in environments with multiple stakeholders, each with competing interests, is a complex challenge. **Aleksandra Maslon, a product designer and UX strategist** at Boldare, shares her experiences navigating this complexity, particularly in her work on a Saudi government platform. This conversation offers insights on overcoming these challenges while maintaining a user-centered focus.\n\nIf you’re interested in understanding how to manage such complexity and design digital products that meet diverse stakeholder needs, **we invite you to watch the episode or read the full transcript below.**","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-07-31T08:02:16.980Z","slug":"designing-digital-services-in-multi-stakeholder-environments-insights-from-aleksandra-maslon","type":"blog","slugType":null,"category":null,"additionalCategories":["Ideas","Digital Product","How to"],"url":null},"author":"Roksana Kaczmarska","authorAdditional":"","box":{"content":{"title":"Designing digital services in multi-stakeholder environments: Insights from Aleksandra Maslon","tileDescription":"Explore the complexities of designing digital services in multi-stakeholder environments. Aleksandra Maslon offers practical insights on overcoming challenges and aligning competing interests, with a focus on user-centered design. Watch the episode or read the full transcript.","coverImage":"https://res.cloudinary.com/de4rvmslk/image/upload/v1754036508/JJKLN_hsg4id.png"},"coverImage":null}},"id":"9889fc7e-4b3a-5038-b4b9-e94d86bd7ac3"}},{"node":{"excerpt":"","fields":{"slug":"/blog/utilizing-storybook-in-digital-product-development/"},"frontmatter":{"title":"Utilizing storybook in digital product development","order":null,"content":[{"body":"## What is storybook? \n\nAt its core, Storybook is a development environment that allows developers and designers to create and test UI components without needing to integrate them into the main application immediately. This means that UI elements like buttons, input fields, and navigation bars can be worked on and viewed in isolation, making it easier to spot issues, iterate on designs, and collaborate with others.\n\nStorybook was introduced to simplify the process of building and testing UI components across a variety of use cases. Since its launch, the tool has evolved to support popular front-end frameworks such as **[React,](https://react.dev/) [Vue](https://vuejs.org/), [Angular](https://angular.dev/), and [Svelte](https://svelte.dev/),** making it a versatile solution for teams working with different technologies. Its ability to display all components in one central place means that it serves as both a documentation tool and a testing ground, offering immediate feedback and reducing the need for complex integration setups.\n\n## Getting started with storybook\n\nSetting up **Storybook** in a project is fairly simple and involves a few basic steps. After installing it through npm or yarn, **the first step** is to configure Storybook’s settings, typically by defining the framework being used (such as React or Vue) and adding any necessary plugins or add-ons. This step helps ensure that the tool is tailored to the needs of the project.\n\nOnce the configuration is complete, **the next task** is to create \"stories\" for the UI components. Stories are essentially different states or variations of a component (for example, a button in its default, hover, and disabled states). These stories are written in JavaScript and serve as the foundation for testing and interacting with the component.\n\n**After the stories are set up**, you can run Storybook locally and view all the components in your browser. This interactive environment allows both developers and designers to work in tandem, providing an easy way to see how each component behaves before it’s integrated into the larger application.\n\n## When to use storybook\n\nStorybook is particularly useful in scenarios where UI components need to be developed independently or iterated on quickly. For example, when building a design system—a set of reusable components that follow a specific design language—Storybook provides a visual representation of the system, making it easier for teams to maintain consistency across the project.\n\nAnother scenario where Storybook shines is in large-scale applications with many components that need frequent updates or tweaks. The tool helps developers and designers work on individual components without needing to navigate through the entire application, saving time and reducing the risk of introducing bugs.\n\nFurthermore, Storybook is incredibly valuable for teams that need to test components across different states, such as loading, empty, or error states. By working in isolation, teams can ensure that every component functions as expected before they are brought into the full application context.\n\n## Best practices for using storybook\n\nWhile Storybook offers a lot of power and flexibility, there are a few best practices that can help teams get the most out of it.\n\n**One key practice is to write meaningful and comprehensive stories**. Instead of creating a single, default state for each component, aim to include a variety of states that the component might encounter in a real-world scenario. For example, a form input might have stories for different error messages, loading indicators, or successful submissions. This approach not only ensures thorough testing but also provides clear examples for designers and developers to reference.\n\n**Another important practice is to organize components into logical categories within Storybook**. This could involve grouping related components, like buttons, forms, and modals, into separate sections. This organization makes it easier to navigate the component library and ensures that team members can quickly find what they need.\n\n**Additionally, using Storybook’s vast array of add-ons can greatly enhance its functionality.** For instance, accessibility add-ons can help teams identify and fix accessibility issues early, while testing add-ons allow for visual regression testing, ensuring that components maintain their look and feel across updates.\n\nFinally, **simplicity is key**. The goal of Storybook is to provide a clear and isolated view of each component. Keeping stories simple and focused on the behavior of a single component helps avoid confusion and ensures that teams can quickly identify and fix issues.\n\n## Collaboration between designers and developers\n\nOne of the most significant advantages of using Storybook is the way it fosters collaboration between designers and developers. Traditionally, designers would create static mockups, and developers would implement them in code, often leading to miscommunication and delays. Storybook bridges this gap by allowing both teams to work with the same set of live components.\n\nFor designers, Storybook provides a way to view components in various states, ensuring that the design vision is being accurately translated into code. It also allows them to test different interactions and states without needing to rely on developers to build out the entire application first. On the developer side, Storybook gives immediate feedback on how a component behaves, which reduces the risk of errors and helps developers make quick adjustments when necessary.\n\nThis seamless collaboration is particularly valuable in agile workflows, where components are continuously iterated upon and released. With Storybook, both designers and developers can see and interact with the same components, making the development process smoother and more efficient.\n\n## The benefits of using storybook\n\nStorybook offers several benefits that can significantly improve the process of creating digital products.\n\nFirst and foremost, it accelerates the development cycle. By enabling teams to work on components independently, developers can focus on building the code without worrying about the complexities of the entire application. Designers, in turn, can iterate on their designs faster, knowing that they can see their work implemented in real-time.\n\nSecond, Storybook helps maintain consistency across large projects. Since all components are stored in a centralized location, it becomes easier to enforce design standards and ensure that the user interface remains consistent, even as different developers work on different parts of the app.\n\nAdditionally, Storybook provides valuable testing and documentation features. It allows teams to identify bugs and inconsistencies early in the development process, saving time in the long run. As a living document, Storybook can also serve as a reference for new team members, helping them understand how components are supposed to behave and interact.\n\n## Case study: Netlify's rebranding with storybook\n\nOne notable example of Storybook's impact is Netlify's rebranding project. Faced with the challenge of updating their platform's user interface to reflect a new brand identity, Netlify's design and development teams turned to Storybook. By utilizing Storybook's isolated component development environment, they were able to rapidly prototype and test UI elements without the need to integrate them into the full application immediately. This approach not only accelerated the development process but also ensured that the new design elements were consistent and aligned with the brand's vision. The use of Storybook played a crucial role in enabling Netlify to complete their rebranding in just six weeks, demonstrating the tool's effectiveness in streamlining UI development and fostering collaboration between design and development teams.\n\n## Potential drawbacks, alternatives, and when to use storybook\n\nWhile Storybook offers numerous advantages, it’s not without its challenges. For smaller projects or teams that don’t rely on reusable components, setting up and maintaining Storybook might feel like an unnecessary overhead. Additionally, integrating Storybook into an existing project can be time-consuming, especially if the project wasn’t initially built with componentization in mind.\n\nIn terms of alternatives, tools like Figma and Sketch provide design-focused solutions that allow teams to prototype and share UI elements. These tools may not offer the same level of interaction and testing as Storybook, but they can still serve as valuable resources for smaller projects or teams without the need for a full-fledged design system.\n\n## When to Use Storybook:\n\n* For large-scale applications with many reusable components.\n* When building or maintaining a design system.\n* For teams that require close collaboration between designers and developers.\n* When UI components need to be tested across various scenarios in isolation.\n\n## When Not to Use Storybook:\n\n* For small projects or teams that don’t rely on reusable components.\n* If there’s no need for extensive UI testing.\n* When the overhead of integrating Storybook outweighs the benefits.\n\n## Conclusion\n\nStorybook has transformed the way teams develop, test, and document UI components. By isolating components and providing a collaborative environment for designers and developers, it accelerates development cycles, improves consistency, and fosters better communication. While it may not be the right fit for every project, when used in the right context, Storybook can significantly improve the efficiency and quality of digital product development. Whether you're building a complex design system or simply need a better way to test components, Storybook offers a reliable and effective solution."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1753783503/Cover_photo_template_belbeu.png","lead":"Within the constantly changing landscape of digital product development, the collaboration between designers and developers plays a crucial role in ensuring high-quality and consistent user interfaces. **One tool that has revolutionized this process is Storybook**, an open-source tool designed to help teams build, test, and document UI components in isolation. First introduced in 2016, Storybook has quickly become a favorite in the front-end development community for its ability to facilitate smoother workflows and better communication between team members. In this article, we’ll explore **how Storybook is used**, its benefits, and how it can improve the development of digital products.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-07-29T09:51:35.438Z","slug":"what-is-storybook","type":"blog","slugType":null,"category":null,"additionalCategories":["Digital Product"],"url":null},"author":"Kamil Zieliński","authorAdditional":"","box":{"content":{"title":"What is storybook? UI component development & testing guide","tileDescription":"Learn what Storybook is and how it helps developers and designers build, test, and document UI components efficiently. Explore its setup, best practices, and key benefits, along with real-world use cases like Netlify's rebranding project. Discover when Storybook is ideal for your projects.","coverImage":""},"coverImage":null}},"id":"7b862755-e579-586d-895f-9b987915fabc"}},{"node":{"excerpt":"","fields":{"slug":"/blog/design-meets-automation-how-we-reimagined-product-workflows-at-boldare/"},"frontmatter":{"title":"Design meets automation: How we reimagined product workflows at Boldare","order":null,"content":[{"body":"## Automation as a Design Tool\n\nWhile automation might sound like a tool reserved for developers or operations teams, in practice, it has become an essential part of our design toolkit. Tools like **[Make](https://www.make.com/en)** and **[Zapier](https://zapier.com)** allow designers to create workflows that reduce repetition, improve team communication, and even handle localisation at scale.\n\nAt first, I started small. A notification here, a doc update there. But as I dug deeper, I began to realise that automation could stretch across the entire design-delivery chain. From wireframes to localisation, from usability testing to final delivery - the potential was huge.\n\n\n\n## The real Problem: Lost time and fractured communication\n\nLet me give you an example from one of our recent projects. We were building a digital service targeted at both English and Arabic-speaking users. The designs were prepared in Figma and looked solid. But once we reached the localisation phase, we hit a bottleneck. Translating every piece of UI text manually was not only time-consuming, it was error-prone and highly dependent on whether the right person was available at the right moment.\n\nThere were endless Slack messages. Google Docs with missing strings. Product Owners asking what was still pending. It was clear that we weren’t just dealing with a content issue - we were facing a workflow challenge. So we decided to break the pattern.\n\n## Building the Automation pipeline\n\nHere's what we put in place - and it's one of the workflows I’m most proud of. After the design was finalised in **[Figma](https://www.figma.com/files/team/1303111509734114113/recents-and-sharing?fuid=923522201565311366)**, I connected the frames to **[Amazon](https://aws.amazon.com/textract/) AWS Textract**, which scanned the screens and automatically extracted all visible text. That content was then sent to a structured **[Google Doc](https://workspace.google.com/products/docs/)**, acting as a centralised translation file.\n\nFrom there, I used Make to send each English string through **[DeepL](https://www.deepl.com/en/translator)**, which generated an initial Arabic translation suggestion. This wasn't a final copy, of course, but it was a massive help - it gave our Product Owner a first draft to work from, instead of starting with a blank page.\n\nOnce the draft was ready, Make triggered a **Slack notification** to our Product Owner, prompting them to review and approve the Arabic copy. When the copy was approved and updated in the Google Doc, another automated notification was sent - this time back to the design and dev team - letting them know that the final content was ready to implement.\n\nWhat once took days of chasing and context-switching now happens in hours, with everyone informed, and everything tracked.\n\n\n\n## The bigger picture: Automation in testing and validation\n\nTranslation workflows were just the beginning. We also began applying automation to our **UX testing cycles**.\n\nFor example, when preparing usability tests, we often had to manually collect test candidates, schedule calls, send reminders, and follow up with participants afterwards. Each of these steps sounds simple, but taken together, they create a burden  especially for small teams juggling multiple priorities.\n\nBy integrating tools like Zapier and Google Calendar with our participant database, we set up an automated sequence: once a test candidate was selected, they received a personalised email with available testing slots. After booking, reminders were sent automatically 24 hours before the session. Post-session, the system followed up with a feedback form and stored the results in a [Notion](https://www.notion.com) database for easy team access.\n\nAgain, not a single line of code. Just smart connections.\n\nhttps://www.boldare.com/work/case-study-practitest/\n\n## Impact that Speaks for Itself\n\nThe value of this kind of automation can’t be overstated. We saved **hours of manual work each week**. But more importantly, **we reduced room for error**, created a more predictable workflow, and freed up time for what really matters: the thinking, the designing, the refining.\n\nFor our team, it meant fewer Slack threads and status meetings. For our Product Owner, it meant having one clear place to approve translations. For our clients, it meant faster delivery and higher quality.\n\nAutomation didn’t replace collaboration - it **enabled** it.\n\n## A Culture of Experimentation\n\nAt Boldare, we thrive on experimentation. And automation became just another way to experiment - not with the product interface, but with the way we work. As a product designer, I used to see automation as something adjacent to my role. Now, I see it as central.\n\nIt’s a mindset shift. When you start asking yourself “how can this be done faster?” or “is there a smarter way to get this into the hands of the right person?”, you begin to spot opportunities for automation everywhere.\n\nI won’t pretend every workflow was perfect from the start. Some setups failed. Some notifications got lost. But each time, we learned. We iterated on our automation pipelines the same way we iterate on products. And slowly, we built systems that truly supported our process - not added to it.\n\n## Final thoughts\n\nAutomation isn't a magic fix. It won’t replace creativity, strategy, or empathy - the core pillars of great design. But it will give you more time and space to invest in those pillars. It will reduce noise and let the signal come through.\n\nAnd in a team like Boldare, where we’re always aiming for smarter, faster, more collaborative delivery - that’s a game-changer.\n\nWhether you're a designer, a client, or a stakeholder wondering how design operations can scale - I’d say this: automation is no longer optional. It's a strategic advantage.\n\nStart small. Experiment. Build your own workflows. And never underestimate the power of a good Zap."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1753174396/jhjhhj_qewm30.png","lead":"When we talk about **innovation in digital product design**, it’s easy to picture cutting-edge interfaces, refined user journeys, and data-driven iteration cycles. But there’s a quieter, often overlooked frontier that’s just as critical. **As Andrii Nozdrin explains in this article**,  the operational layer behind great design. The mechanics that make the design process seamless, timely, and truly cross-functional. At Boldare, **we build digital products with speed and purpose**. That means rapid feedback loops, high stakeholder involvement, and a continuous flow of deliverables. But for all the agile rituals and UX best practices we have in place, one challenge kept surfacing in our projects: **manual overhead**.\n\nManual coordination between teams. Manual tracking of translations. Manual setup for usability tests. All of it added friction - especially when working with distributed teams or managing products across different time zones and languages. **So, like any designer fed up with repetitive tasks, I asked myself: what can we automate? It turns out, quite a lot.**","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-07-22T07:48:55.273Z","slug":"design-meets-automation-how-we-reimagined-product-workflows-at-boldare","type":"blog","slugType":null,"category":null,"additionalCategories":["Ideas","Digital Product","How to"],"url":null},"author":"Roksana Kaczmarska","authorAdditional":"","box":{"content":{"title":"Design meets automation: How we reimagined product workflows at Boldare","tileDescription":"Discover how Boldare reimagined product workflows with automation to speed up delivery, improve efficiency, and enhance collaboration in digital product design.","coverImage":""},"coverImage":null}},"id":"4db91e52-80af-5bd7-9e1a-22935e79c42f"}},{"node":{"excerpt":"","fields":{"slug":"/blog/top-5-renewable-energy-software-development-companies-in-2025/"},"frontmatter":{"title":"Top 5 renewable energy software development companies in 2025","order":null,"content":[{"body":"## 1. Boldare\n\n[boldare.com](https://www.boldare.com/)\n\nBoldare specializes in end-to-end digital product development - from discovery workshops to MVPs to full-scale platforms. What makes them stand out is their proven experience in renewable energy and their cross-functional teams combining UX, software engineering, and the latest technologies like AI, IoT, and cloud. For over a decade, they’ve supported green energy companies at every stage of digital transformation.\n\nKey strengths in renewables:\n\n* Worked with global leaders like SunPower and Sonnen, building apps for energy production monitoring, battery management, and user engagement.\n* Full product ownership - from design to development to QA and post-launch support.\n* Advanced AI integration for real-time monitoring and predictive maintenance.\n* Strong focus on ESG compliance and sustainability-driven design.\n* Agile product teams that build fast prototypes and scale them effectively.\n\nExplore Boldare’s energy case studies: [boldare.com/work/#industry-energ](https://www.boldare.com/work/#industry-energy)\n\n### Sonnen – Customer Portal (MVP)\n\nBoldare partnered with Sonnen to deliver a fully functional MVP of a B2C customer web portal in under two months. The project included responsive UX and UI design, tailored to real user needs identified through discovery workshops and user story analysis.\n\nKey highlights:\n\n* Dedicated Agile team: frontend developers, DevOps, UX/UI designers, and a Scrum Master.\n* Close collaboration with Sonnen’s internal teams, including daily standups, monthly on-site meetings, and shared retrospectives.\n* The result was a stable, fast, and brand-consistent portal for home battery monitoring and management.\n\n\n\n### SonnenCharger – EV Charging App\n\nAs part of their long-term collaboration, Boldare developed a mobile application supporting green energy-based EV charging. The MVP was delivered in less than three months.\n\nKey features:\n\n* Prediction and control of the EV charging process.\n* Versions of the app tailored for both end users and installation partners.\n* Seamless integration with Sonnen’s existing infrastructure and consistent UX/UI with the customer portal.\n\n\n\n### SunPower One – Energy Management Platform (Web + Mobile)\n\nFor SunPower, a global leader in solar technology, Boldare designed and developed SunPower One—a comprehensive application for energy monitoring, smart device integration, and installer tools.\n\nFunctionality:\n\n* Serves both residential customers and solar installers.\n* Real-time data display: energy production, consumption, savings, and EV charging.\n* Cross-platform technology (Android/iOS/web), with a JS/React-based backend hosted in the cloud.\n\nOutcome:\n\n* Supported SunPower’s digital transformation across international markets (Europe, Australia, and more).\n* Enhanced user experience through personalization and a modern interface.\n\n\n\n### Hack the Wind - AI-Powered Turbine Failure Prediction\n\nBoldare’s Machine Learning team took part in the “Hack the Wind” hackathon organized by InnoEnergy and Wind Europe (2018), securing a top-three spot.\n\nProject scope:\n\n* Built a predictive maintenance tool using machine learning models to forecast wind turbine component failures up to 60 days in advance—developed in just 48 hours.\n* The application visualized risk levels, identified specific parts for replacement, and allowed integration into the service process.\n* Validated through real-time testing during the event, leading to adjustments based on market needs.\n\n\n\n## 2. Intelliarts\n\n[intelliarts.com](https://www.intelliarts.com/)\n\nIntelliarts focuses on tailored AI and IoT solutions for agriculture and renewable energy. They excel in real-time data analytics and hardware-software integration, particularly in smart irrigation systems and predictive maintenance for wind farms.\n\nBest for:\n\n* AI/ML-based data prediction,\n* IoT sensor integration,\n* Automation across distributed energy systems.\n\nGreat for companies working at the intersection of agri-tech and energy.\n\n## 3. ELEKS\n\n[eleks.com](https://www.eleks.com/)\n\nELEKS offers broad expertise in digital transformation—modernizing legacy systems, building mobile/web apps, and integrating analytics with enterprise IT. While not renewables-exclusive, their technical depth and consulting capabilities make them a strong tech foundation partner.\n\nStrengths:\n\n* Robust capabilities in big data, UX, cloud,\n* Transformation-focused consulting,\n* Ideal for industrial and large enterprise clients.\n\n## 4. Envision Digital\n\n[envision-digital.com](https://envision-digital.com/)\n\nEnvision Digital is a global leader specializing in large-scale energy and infrastructure solutions, including smart grids, climate technology, ESG platforms, and national-scale systems.\n\nKey offerings:\n\n* Energy management at city, state, and national levels\n* Real-time ESG tracking and emissions analytics\n* Grid optimization and long-term sustainability planning\n\nIdeal for governments and major utilities seeking to accelerate their net-zero strategies and digital transformation in the energy sector.\n\n## 5. UL Solutions\n\n[ul.com](https://www.ul.com/)\n\nUL Solutions supports project planning and optimization—not by building software from scratch, but through best-in-class analytical tools. Their software helps identify optimal wind/solar sites, ensure regulatory compliance, and assess operational risk.\n\nKey features:\n\n* Wind/solar siting tools,\n* Renewable project monitoring dashboards,\n* Technical due diligence and standards compliance.\n\nIdeal for developers and investors seeking data precision and independent validation.\n\nIn 2025, software is the silent engine powering the renewable energy revolution. Whether it’s optimizing battery storage, integrating smart grids, or enabling real-time data from solar and wind farms, the right tech partner makes all the difference. From Boldare’s agile product development for industry leaders like SunPower, to the large-scale infrastructure platforms of GE Vernova and Envision Digital—this ranking proves one thing: innovation in clean energy doesn’t just come from hardware. It’s built in code.\n\nWant to build the future of energy? Start with the right software partner."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1753086674/Group_26086303_rr1tzr.png","lead":"The renewable energy revolution is no longer just about building wind farms or installing solar panels. It’s about transforming how we manage energy—through smart, flexible, and scalable software systems. In a world where every kilowatt counts, energy providers must adapt to fast-moving regulatory, market, and technological shifts. Software must now be real-time, predictive, interoperable, and easy to use.\n\n**This is where experienced technology partners step in—teams that don’t just understand code, but the business and operational challenges of the energy sector. Among these, Boldare clearly stands out.**","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-07-21T07:51:12.422Z","slug":"Top-5-renewable-energy-software-development-companies-in-2025","type":"blog","slugType":null,"category":null,"additionalCategories":["Digital Product"],"url":null},"author":"Roksana Kaczmarska","authorAdditional":"","box":{"content":{"title":"Top 5 renewable energy software development companies in 2025","tileDescription":"Discover the top 5 renewable energy software development companies in 2025, from smart grid platforms to AI-powered solar and battery solutions.","coverImage":""},"coverImage":null}},"id":"7ce0e8ab-9667-5b3c-a91f-3c46e824b706"}},{"node":{"excerpt":"","fields":{"slug":"/blog/top-7-python-companies-poland-2026/"},"frontmatter":{"title":"Top 7 Python Development Companies in Poland 2026","order":null,"content":[{"body":"## Why Poland for Python Development?\n\nPoland has established itself as one of Europe's premier destinations for software development outsourcing. The country boasts a thriving tech ecosystem, highly skilled developers, and competitive pricing compared to Western European markets. Polish developers consistently rank among the best in international programming competitions and bring strong technical expertise to complex projects.\n\nThe Python development landscape in Poland is particularly robust, with numerous agencies specializing in diverse areas from web applications to data engineering. These companies serve clients worldwide, delivering quality solutions across various industries.\n\n## Quick Comparison: Top Python Development Agencies\n\nHere's an overview of the leading Python development companies in Poland, ranked by their ratings and expertise:\n\n
# Agency Specialization Rating Location
1 Boldare Product-first Python development ⭐ 4.9 /5 Poland (EU)
2 Selleo Custom web apps & SaaS platforms ⭐ 4.9 /5 Poland
3 CodiLime Backend & infrastructure projects ⭐ 4.8 /5 Poland
4 Vavatech Data engineering & analytics ⭐ 4.7 /5 Poland
5 Bright Inventions Python backends for web & mobile ⭐ 4.9 /5 Poland
6 Advox Studio Custom backend systems & APIs ⭐ 4.8 /5 Poland
7 CodeQuest Startup-focused Python apps ⭐ 4.7 /5 Poland
\n\n## Detailed Analysis of Top Python Development Companies\n\n### 1. Boldare - Product-First Python Development\n\n![Boldare](https://res.cloudinary.com/de4rvmslk/image/upload/v1769771386/1_c9Fpfg5MdiB8IQthP0L-9g_b2iov5.webp)\n\n**Rating: 4.9/5**\n\nBoldare stands out with its product-centric approach to Python development. Rather than simply writing code, this agency focuses on building products that solve real business problems. Their team combines technical expertise with product thinking, making them ideal for companies looking to create market-ready solutions.\n\nTheir Python development services encompass full-stack web applications, API development, and integration with modern frontend frameworks. Boldare's methodology emphasizes user research, iterative development, and continuous validation, ensuring the final product meets actual market needs.\n\n### 2. Selleo - Custom Web Applications and SaaS Platforms\n\n![Selleo](https://res.cloudinary.com/de4rvmslk/image/upload/v1769771415/1_Xe_OF7H4ArmuFgAn70iFug_cqgopw.webp)\n\n**Rating: 4.9/5**\n\nSelleo specializes in building custom web applications and Software-as-a-Service platforms using Python. With extensive experience in creating scalable solutions, they excel at developing complex business applications that handle high user loads and large data volumes.\n\nThe company's portfolio includes enterprise management systems, e-commerce platforms, and cloud-based SaaS products. Their development process incorporates modern DevOps practices, ensuring reliable deployments and system stability.\n\n### 3. CodiLime - Backend and Infrastructure Excellence\n\n![CodiLime](https://res.cloudinary.com/de4rvmslk/image/upload/v1769771432/1_YDf6uVcmQ93LuDsFWHTZmw_nhqp5k.webp)\n\n**Rating: 4.8/5**\n\nCodiLime focuses on backend development and infrastructure projects, making them particularly strong in building robust, scalable server-side solutions. Their expertise extends to network programming, distributed systems, and cloud infrastructure.\n\nThis agency is well-suited for projects requiring deep technical knowledge of networking protocols, system architecture, and performance optimization. They've worked with telecommunications companies and tech firms requiring sophisticated backend solutions.\n\n### 4. Vavatech - Data Engineering and Analytics\n\n![Vavatech](https://res.cloudinary.com/de4rvmslk/image/upload/v1769771442/1_Ts44at1bOB0OmoLhHYUPCA_ylgkjm.webp)\n\n**Rating: 4.7/5**\n\nVavatech brings specialized capabilities in data engineering and analytics. Python's strength in data processing makes it ideal for big data applications, and Vavatech leverages frameworks like Pandas, NumPy, and Apache Spark to build powerful data solutions.\n\nTheir services include data pipeline development, ETL processes, business intelligence dashboards, and machine learning implementations. Companies with significant data processing needs will find Vavatech's expertise particularly valuable.\n\n### 5. Bright Inventions - Python Backends for Web and Mobile\n\n![Bright Inventions](https://res.cloudinary.com/de4rvmslk/image/upload/v1769771453/1_xnxpFHaa62JbQdRHf1thcg_dimpay.webp)\n\n**Rating: 4.9/5**\n\nBright Inventions specializes in creating Python backends that power both web and mobile applications. Their full-stack capabilities mean they can handle the entire development process, from server-side logic to mobile app integration.\n\nThe company excels at building RESTful APIs, implementing real-time features with WebSockets, and creating secure authentication systems. Their experience spans fintech, healthcare, and consumer applications.\n\n### 6. Advox Studio - Custom Backend Systems and APIs\n\n![Advox Studio](https://res.cloudinary.com/de4rvmslk/image/upload/v1769771464/1_ZKIcr-kYsZbwz8jAI2r6dw_eo1zr9.webp)\n\n**Rating: 4.8/5**\n\nAdvox Studio concentrates on developing custom backend systems and API solutions. Their technical approach emphasizes clean code architecture, comprehensive testing, and thorough documentation.\n\nThey're particularly skilled at creating microservices architectures, implementing GraphQL APIs, and integrating third-party services. Advox Studio works well with companies needing to modernize legacy systems or build new API-first applications.\n\n### 7. CodeQuest - Startup-Focused Python Development\n\n![CodeQuest](https://res.cloudinary.com/de4rvmslk/image/upload/v1769771470/1_aJ3dQ0OkyN0ySviI-eLpDg_a2lis9.webp)\n\n**Rating: 4.7/5**\n\nCodeQuest tailors its services specifically for startups and early-stage companies. They understand the unique challenges of building products with limited resources and tight timelines, offering flexible engagement models and MVP development expertise.\n\nTheir startup-oriented approach includes rapid prototyping, iterative development cycles, and scalability planning. CodeQuest helps founders validate their ideas quickly while building a solid technical foundation for future growth.\n\n## Key Factors When Choosing a Python Development Partner\n\n### Technical Expertise\n\nEvaluate the agency's proficiency with Python frameworks (Django, Flask, FastAPI), database technologies, cloud platforms, and modern development practices. Review their portfolio for projects similar to yours in complexity and scope.\n\n### Industry Experience\n\nConsider whether the company has worked in your industry. Domain knowledge can significantly accelerate development and lead to better product decisions.\n\n### Communication and Culture\n\nEffective collaboration requires clear communication. Polish development agencies generally offer excellent English proficiency and work well with international teams. Consider time zone compatibility and communication tools.\n\n### Project Management Approach\n\nUnderstand their development methodology (Agile, Scrum, Kanban) and how they handle project management, reporting, and stakeholder communication.\n\n### Pricing and Contract Terms\n\nPolish development rates typically range from $40-90 per hour depending on seniority and specialization. Clarify pricing models (fixed price vs. time and materials) and contract flexibility.\n\n## The Polish Tech Advantage\n\nPoland's position as a Python development hub stems from several factors:\n\n**Education System**: Polish universities produce thousands of computer science graduates annually, with strong foundations in mathematics and algorithms.\n\n**Tech Community**: Active local communities, conferences, and meetups foster knowledge sharing and professional development.\n\n**Business Environment**: Favorable business conditions, EU membership, and strong IP protection make Poland attractive for outsourcing.\n\n**Cultural Compatibility**: Polish developers work well with Western European and American teams, sharing similar work ethics and business practices.\n\n## Making Your Decision\n\nThe agencies listed above represent the cream of Poland's Python development landscape. Each brings unique strengths to the table:\n\n* Choose **Boldare** for product-focused development with strong UX emphasis - Select **Selleo** for complex SaaS platforms requiring scalability - Pick **CodiLime** for infrastructure-heavy or network-related projects - Opt for **Vavatech** when data engineering is central to your project - Consider **Bright Inventions** for mobile-backend integration - Work with **Advox Studio** for API-first or microservices architectures - Partner with **CodeQuest** if you're a startup seeking rapid MVP development\n\nAll seven companies maintain high quality standards, as reflected in their ratings. Your choice should align with your specific project requirements, budget, and timeline.\n\n## Conclusion\n\nPoland offers exceptional value for Python development, combining technical excellence with competitive pricing and cultural compatibility. The companies featured in this guide have proven track records and can handle projects of varying complexity.\n\nWhen selecting your development partner, look beyond ratings to consider technical fit, communication style, and alignment with your project goals. Request case studies, check references, and conduct technical interviews to ensure you find the right match.\n\nThe Polish Python development ecosystem continues to mature, with these agencies at its forefront. Whether you're building a startup MVP or enterprise-grade system, you'll find capable partners among Poland's top development companies.\n\n\n\n## FAQ\n\n1. **Is Python suitable for complex, large-scale products?**\\\n Yes. With proper architecture, Python is widely used in scalable platforms, data systems, and AI-driven products.\n2. **What types of projects benefit most from Python?**\\\n Backend systems, APIs, SaaS platforms, data engineering, AI/ML, automation, and internal business tools.\n3. **Why choose a Polish Python development company?**\\\n Poland offers strong engineering talent, excellent communication, EU-based collaboration, and competitive costs."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1759137948/Group_26086317_bcejuf.png","lead":"Poland has emerged as a leading hub for Python development in Europe, offering world-class expertise in software engineering. This comprehensive guide examines the top Python development agencies in Poland for 2026, helping you find the perfect partner for your next project.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-07-15T10:30:29.919Z","slug":"top-7-python-companies-poland-2026","type":"blog","slugType":null,"category":null,"additionalCategories":["Digital Product","GenAI","Tech"],"url":null},"author":"Roksana Kaczmarska","authorAdditional":null,"box":{"content":{"title":"Top 7 Python Development Companies in Poland 2026","tileDescription":"Discover the leading Python development companies in Poland. Compare ratings, specializations, and expertise to find your ideal development partner.","coverImage":null},"coverImage":null}},"id":"1a420b51-ff99-5b8c-bbf0-1a4d48ffc013"}},{"node":{"excerpt":"","fields":{"slug":"/blog/why-nearshoring-works-the-best-european-software-partners-for-your-business/"},"frontmatter":{"title":"Why nearshoring works: The best European software partners for your business [UPDATED 2026]","order":null,"content":[{"body":"## When to consider nearshoring\n\nYou might consider a nearshore software partner if:\n\n1. **You want more real-time collaboration**\n\nOne of the main benefits of nearshoring over offshoring is the ability to have real-time communication with your development team. With nearshore teams located in similar or overlapping time zones, you can work together more closely, with frequent check-ins, quick feedback loops, and more dynamic collaboration.\n\n2. **You need to scale a development team without hiring locally**\n\nWhen you need to scale your development capacity quickly but don’t have the time or resources to hire locally, nearshoring provides a solution. It allows you to quickly augment your in-house team with skilled developers from nearby countries, without the need for lengthy recruitment processes.\n\n3. **You want cost savings without significant timezone differences or communication barriers**\n\nNearshoring offers a good balance of cost savings and high-quality talent without the significant communication issues that come with offshoring. By working with teams in nearby countries, you can achieve the financial benefits of outsourcing without sacrificing effective collaboration due to time zone differences or language barriers.\n\n4. **You seek a long-term strategic partner for development, maintenance, or innovation**\n\nIf you're looking for a partner to help you with the ongoing development of your product, as well as maintenance and future innovation, nearshoring can provide the stability and continuity you need. The proximity of nearshore teams means that you can build long-term relationships with your outsourcing partner.\n\n\n\n## Top nearshore destinations in Europe for software development\n\nWhen considering nearshoring in Europe, it’s important to choose a destination that offers the right mix of skilled talent, time zone compatibility, and cost-effectiveness. Here are some of the best countries in Europe for nearshoring software development:\n\n1. **Poland**\n\nPoland is one of the top nearshore destinations for businesses in the UK and Western Europe. The country offers a highly skilled talent pool, competitive pricing, and a strong culture of collaboration. With a well-developed IT sector, Poland is home to many software development companies that specialize in agile methodologies, custom software development, and UX/UI design.\n\n**Top companies:**\n\n* **[Boldare](https://clutch.co/profile/boldare)** – Leading product design and development company specializing in agile methodologies and custom software development.\n* **[STX Next](https://clutch.co/profile/stx-next)** – A leading Python development company specializing in custom software development and digital transformation.\n* **[Intive](https://clutch.co/profile/intive)** – Known for agile development and providing innovative software solutions across various industries.\n\n2. **Ukraine**\n\nUkraine is another strong nearshoring option, particularly for businesses looking for high-quality development at competitive prices. Despite the challenges the country has faced, it remains a hub for skilled IT professionals, particularly in software engineering, mobile development, and web development. Ukrainian developers are known for their problem-solving abilities and expertise in complex projects.\n\n**Top companies:**\n\n* **[Intellias](https://clutch.co/profile/intellias)** – A major player in IT consulting, providing services in software development and system integration.\n* **[SoftServe](https://clutch.co/profile/softserve)** – A global IT and consulting firm specializing in product development, cloud computing, and AI.\n* **[Miratech](https://clutch.co/profile/miratech)** – Offers IT services in areas such as software development, testing, and cloud solutions.\n\n3. **Romania**\n\nRomania is an increasingly popular nearshore destination, known for its strong educational system, especially in STEM fields. The country offers a highly skilled workforce at competitive rates, particularly in areas such as software engineering, IT consulting, and cloud development. Romania’s location in Eastern Europe also means it shares a similar cultural affinity with Western Europe.\n\n**Top companies:**\n\n* **[Endava](https://clutch.co/profile/endava)** – A global IT services company that offers software development, automation, and data analytics.\n* **[Luxoft](https://clutch.co/profile/luxoft)** – Provides IT services and consulting, specializing in custom software development and business consulting.\n* **[Zitec](https://clutch.co/profile/luxoft)** – A Romanian leader in digital transformation services and software development.\n\n4. **Portugal**\n\nPortugal has become a prime nearshoring destination, especially for companies based in the UK or Ireland. Lisbon and Porto are home to a growing tech talent pool, and Portugal offers the added benefit of a strong English-speaking population. With its proximity to Western Europe, Portugal allows for seamless collaboration with minimal time zone differences.\n\n**Top companies:**\n\n* **Unilabs** – Known for providing software solutions, cloud services, and IT consulting to businesses across various industries.\n* **[Altar.io](https://clutch.co/profile/altario)** – Specializes in software development, digital transformation, and design.\n* **[Vortexa](https://www.vortexa.com)** – An energy analytics company that relies on Portugal-based software developers for its platform.\n\n5. **Czech Republic**\n\nThe Czech Republic has established itself as a growing hub for nearshore software development, particularly in cities like Prague and Brno. The country offers a highly educated workforce with expertise in areas like software engineering, cybersecurity, and cloud computing. The Czech Republic’s strong tech ecosystem makes it an excellent choice for businesses looking for skilled developers in Central Europe.\n\n**Top companies:**\n\n* **[Accenture](https://www.accenture.com/cz-en)** – With offices in Prague, it provides software development and consulting for businesses across various sectors.\n* **Ciklum** – A software development and IT services company offering custom solutions and consulting.\n* **GoodData** – A leading business intelligence platform offering cloud-based analytics.\n\n## Boldare – your trusted nearshore software partner with a UK presence\n\nIf you're exploring nearshoring, Boldare stands out as an ideal partner for businesses seeking a trusted and skilled team. With more than 20 years of expertise, [Boldare](https://www.boldare.com) is a top-tier product design and development company headquartered in Poland. Our team of 70+ professionals has successfully delivered over 300 digital products to more than 111 clients from a range of industries. We specialize in software development, digital design, generative AI, and product innovation, focusing on providing high-quality, user-centric solutions.\n\n**David Cook, one of our UK clients from Xinfu, shared:**\n\n> The speed with which they grasped the challenge was impressive.\n\nTo better cater to our UK clients, we’ve also formed a partnership with a UK-based company. This collaboration brings the benefits of local legal compliance, cultural alignment, and access to UK talent, while still offering the cost benefits of working with a nearshore team in Central Europe.\n\nIf you want to delve into case studies of our work for UK clients, check out the article about an open bank-based, real-time payment solution for a UK fintech startup.\\\n\n\n## Final Thoughts: Is Nearshoring Right for You?\n\nNearshoring offers a compelling option for companies that need to scale quickly, maintain real-time collaboration, and save on costs without sacrificing quality. By partnering with companies in countries like Poland, Ukraine, Romania, Portugal, and the Czech Republic, you can tap into a skilled workforce and establish a long-term relationship with a strategic partner.\n\nIf you’re ready to explore nearshoring and want to find the right partner, make sure to assess not just technical capabilities but also cultural fit and time zone compatibility. The right nearshore partner can help you achieve your goals and drive innovation in your software development process."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1752563940/Group_1000005037_ryhhxp.png","lead":"Nearshoring has become an increasingly popular option for businesses across Europe seeking to outsource software development. As the demand for skilled talent continues to grow, nearshoring presents an effective way to scale development teams, reduce costs, and enhance collaboration. **But when is the right time to consider nearshoring, and which European countries offer the best outsourcing opportunities?**","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-07-15T06:51:41.405Z","slug":"why-nearshoring-works-the-best-european-software-partners-for-your-business","type":"blog","slugType":null,"category":null,"additionalCategories":["Digital Product","Strategy"],"url":null},"author":"Roksana Kaczmarska","authorAdditional":"","box":{"content":{"title":"Why nearshoring works: The best European software partners for your business","tileDescription":"Explore why nearshoring is the ideal solution for scaling your business. Discover top European software development partners that offer cost-effective, high-quality solutions for your company's growth and innovation.","coverImage":""},"coverImage":null}},"id":"2271fe88-f590-5fbc-ada4-2f746009be2b"}},{"node":{"excerpt":"","fields":{"slug":"/blog/boldare-awards-achievements-and-digital-transformation/"},"frontmatter":{"title":"Boldare: awards, achievements and digital transformation","order":null,"content":[{"body":"Boldare’s core strengths and capabilities\n\nAt Boldare, we take pride in our approach to product development and our diverse, skilled team. Here's a quick overview of the key factors that define our work:\n\n* **1 Facilitator in every team** - Each team benefits from a dedicated facilitator, ensuring smooth communication and effective collaboration.\n* **1 Scrum master in every product development team** - We implement Scrum practices to ensure agility, efficiency, and focus on delivering high-quality results.\n* **300+ Released products** - Over 300 digital products successfully launched, each tailored to meet the unique needs of our clients.\n* **50% of Leaders are women** - We are committed to promoting gender equality in leadership, with women making up 50% of our leadership roles.\n* **1 Human-first, AI-augmented engineering approach** - We combine cutting-edge AI with a human-first mindset, driving innovation while maintaining a strong focus on user experience.\n* **62 Frameworks and tools in use, depending on the product's needs** - A flexible approach allows us to choose the right frameworks and tools to ensure optimal performance and scalability for each product.\n* **10+ Different workshop types for product and business development** - We offer a variety of workshops designed to support product and business growth, ensuring alignment and clarity at every stage.\n* **10+ Self-organized chapters** - These communities within our company focus on problem-solving and continuous learning, ensuring we stay ahead of the curve in delivering cutting-edge solutions.\n\n## Boldare’s client portfolio: strategic partnerships across industries\n\nAt Boldare, we have had the privilege of working with a wide range of clients across various sectors, from startups to global corporations, each requiring unique, tailored digital solutions. Our expertise in software development, digital design, generative AI, and product innovation has led to lasting partnerships with:\n\n* Global Leaders: Companies like **Decathlon, BlaBlaCar**, and **BOSCH** trust us for ongoing strategic collaborations, enhancing their digital ecosystems and driving innovation.\n* Innovative Startups: We partner with **Sonnen** and **Maxeon** to bring forward-thinking products to life, such as AI-powered energy storage solutions and solar innovations.\n* Tech and Security: Clients like **Corel**, **Blu5 Labs**, and **Leaseweb** rely on us to develop high-performance products, ensuring security and efficiency in sectors such as graphic design, telecom, and cloud infrastructure.\n* E-commerce & Retail: **TUI Musement**, **SpaMonkey**, and **e.l.f. Cosmetics** have worked with us to improve their online platforms, boost customer experiences, and scale their businesses globally.\n* Finance and Government: Our work with organizations like **UNDP, PRISMA**, and **Takamol Holding** demonstrates our ability to support government agencies and financial institutions in digitizing operations and improving efficiency.\n* Healthcare and Food Safety: We've collaborated with **Novolyze** and **Caidio** to improve safety and sustainability in food production, and we work with **Agnitio** to develop engagement solutions for the pharma sector.\n\nThis diverse range of projects shows how we partner with businesses across industries, solving complex challenges and driving digital transformation. From global giants to emerging innovators, we are proud to be the trusted digital partner for our clients.\n\n## Boldare’s award-winning excellence: recognized for innovation and design\n\nAt Boldare, we pride ourselves on delivering innovative digital products and solutions that not only meet but exceed expectations. Our dedication to quality and innovation has earned us recognition across the industry, with several prestigious awards that highlight our commitment to excellence.\n\n### Honorable mentions: recognizing our creativity and craftsmanship\n\n**Awwwards - Honorable Mention for Plantarium** \n\nAcknowledging our unique design work, Plantarium received an Honorable Mention for its creativity and usability\n\n\n\nHonorable Mention for Page About Akzidenz Grotesk This project highlighted our ability to bring design and typography to life, earning an Honorable Mention\n\n\n\n**Very Peri Award** \n\nA nod to our innovative approach to design, Boldare won the Very Peri Award for excellence in product aesthetics.\n\n\n\n### Prestigious awards: celebrating Boldare’s innovation\n\n**Lovie award - gold**\n\nWe won the Gold Lovie Award for excellence in digital product development, showcasing our expertise in creating exceptional user experiences.\n\n\n\n**Indigo award - silver**\n\nBoldare received the Silver Indigo Award, further solidifying our position as leaders in the design and development industry.\n\n\n\n**Webby award - honoree**\n\nBoldare earned a Webby Honoree Award, a prestigious recognition for excellence in digital design and innovation.\n\n\n\n### Excellence in digital design\n\n**CSS design award for remote work**\n\nA CSS Design Award was presented to Boldare for our exceptional design work in the Remote Work project.\n\n**Nextgen enterprise award**\n\nThe Nextgen Enterprise Award celebrates Boldare’s strategic approach in utilizing innovative technology to drive growth and success.\n\n\n\n**German design award 2021**\n\nBoldare was awarded the German Design Award for our impactful and elegant design solutions, reflecting our dedication to high-quality digital products.\n\n\n\n## Boldare: transforming the future\n\nAt Boldare, we believe that technology is just a tool for achieving greater goals. For over 20 years, we have combined our passion for digital innovation with a deep understanding of user needs, delivering solutions that drive business growth. With more than 300 completed projects, an international client portfolio, and numerous industry awards, we’re ready for the next challenge.\n\nEvery project is an opportunity for us to create something exceptional – products that not only meet expectations but exceed them. Boldare is more than just a tech company; it’s a team of people who are committed to shaping a better future through innovation.\n\nAre you ready to take your ideas to the next level with us? Get in touch, and together we’ll create something that will transform your industry and inspire future generations.\n\n"}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1752235208/Group_1000005035_qgddnf.png","lead":"With over 20 years of experience in crafting innovative digital solutions, Boldare is a company that combines a passion for technology with a deep understanding of user needs. **Our team of over 70 experts** has successfully delivered more than **300 digital products for 111+ international clients**. We specialize in software development, digital design, generative AI, product innovation, and product support and maintenance, providing solutions that support business growth and transformation.\n\n**Why Our Achievements Matter** - Our success reflects the hard work of a team that not only delivers high-quality technology solutions but also focuses on creating products that truly transform how businesses operate. By blending modern technology with thoughtful design and integrating AI into our processes, we can deliver innovative solutions faster and with greater precision – **improving efficiency by 20-40% without compromising on quality.**\n\nWe’re proud to see that our efforts have been recognized by industry awards, and our clients consistently commend our approach and dedication. With an average rating of **[4.8/5 on Clutch](https://clutch.co/profile/boldare)**, our completed projects speak for themselves.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-07-11T11:50:44.304Z","slug":"boldare-awards-achievements-and-digital-transformation","type":"blog","slugType":null,"category":null,"additionalCategories":["Digital Product"],"url":null},"author":"Roksana Kaczmarska","authorAdditional":"","box":{"content":{"title":"Boldare: awards, achievements and digital transformation","tileDescription":"Discover Boldare’s journey of innovation through industry awards, outstanding achievements, and transforming digital products. Explore how we drive business growth and digital success.","coverImage":""},"coverImage":null}},"id":"d9abb64c-ebc6-593b-8947-69760dcabca9"}},{"node":{"excerpt":"","fields":{"slug":"/blog/ai-tool-of-possibilities-from-simple-rhymes-to-a-revolution-in-daily-work/"},"frontmatter":{"title":"AI - Tool of possibilities: From simple rhymes to a revolution in daily work","order":null,"content":[{"body":"## The beginning – birthday wishes for my sister and the first disappointments\n\nMy AI adventure began prosaically – writing birthday wishes for my sister. In 2023, I asked ChatGPT: \"Write a funny and rhyming poem for Aleksandra's birthday, who lives with her dog Pawełek and her handsome boyfriend Szymon in the city of Łódź, where there are many holes in the roads.\"\n\nThe result? Chat turned out to be terrible at creating rhymes, making up words and their contexts. The first conclusion was clear: AI does not replace creativity, but supports it. It is a helpful sparring partner that, however, lacks skills in some areas. So why, since the first attempts were poor, did I decide to delve deeper into the topic? Because I did not want to stay behind, and I wanted to learn how to support my daily work with AI tools as quickly as possible.\n\nTypical concerns I had to face with:\n\n* \"AI is just ChatGPT\" – lack of awareness of other tools\n* \"I will stay behind\" – fear of technology development pace\n* \"First results were poor\" – giving up after failed attempts\n* \"It is only for programmers\" – myth about its complexity\n\n## Discovering tools – more than ChatGPT\n\nThe course made me realize the variety of available tools. In the chat category, I discovered **Claude by Anthropic** – my favorite one with a more natural \"vibe\" and better at long text handling. **Perplexity** proved to be an excellent researcher with Internet access, providing the latest data with the references. **Gemini** surprised me with Google services integration.\n\nIn the visual AI area, I discovered **Midjourney** – the king of image generation with the best artistic results, **DALL-E 3** integrated with ChatGPT, or **Sora** by OpenAI for video generation with Remix and Storyboard options.\n\nThe real revelation were the tools for creating websites: **Relume** for building websites based on sitemaps, **Figma** **AI** generating landing pages from scratch, **v0.dev** creating React components in minutes, or **Uizard** transforming hand-drawn sketches into clickable prototypes.\n\nTogether with another course participant - Andy, we explored the possibilities of using artificial intelligence for website generation and created a presentation called: \"**Web development in the AI era: from Figma to a ready website**\" We focused on combining tools to achieve the best results, such as Relume + Figma + Cursor (RCP), or Figma + Framer.\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1751886667/Zrzut_ekranu_2025-07-7_o_13.10.40_upcugd.png)\n\n\n\n## Practical use of the mentioned tools\n\nDuring DevCamp, we created together a video in 10 minutes:\n\n1. Together we came up with a topic - Forest game\n2. Claude generated a prompt for Midjourney about the outdoor game\n3. Midjourney created an image of a group of people running in the forest\n4. Claude prepared a prompt for KillingAI based on the image\n5. KillingAI generated the final video\n\nTo prepare this demo, we used three tools: **Claude, Midjourney, and KillingAI**. As Midjourney introduced animation generation functionality recently, we could limit tools just to Claude and Midjourney.\n\n## Automation - only for programmers?\n\nAnother myth is: It is only for programmers. I will raise the bar and prove AI is also useful in the automation field.\n\nThe below flow was also prepared as a part of the course. In everyday work I provide services for an Arabic client - the portal needs to be in the Arabic language. We have been struggling with long delays in receiving Arabic translations for many years. A chatbot that encourages our product owner twice a week to check the file and add translations was introduced some time ago. However, notifications are sent regardless whether we have added any new texts or not. My proposal was to create a new automated task that checks daily whether any new records have been added. If yes, OpenAI generates Arabic translations and sends an email to the Product Owner informing them that the proposed texts and translations need to be verified. The file contains a check column that the PO marks if everything is correct. If corrections are needed, they make them by themselves or add a comment. Automation was prepared using Make app.\n\nAnother example: Imagine a situation where we need to publish some materials on social media every two weeks. While we can barely find any people willing to write content, we have absolutely no space for generating consistent graphics. My proposal includes automation using **Airtable** and **Make**.\n\nProcess designed in Make:\n\n1. Triggering condition: a new row is added in the Airtable spreadsheet\n2. AI generates post description based on the entered title\n3. AI creates branding graphics\n4. Automatic publication on platforms\n\n## My personal assistants\n\nThe last topic covered at the course was creating our own assistants using tools like **Alice** or **GPT**. Each assistant is like a very good intern: they do repetitive tasks, need clear instructions, sometimes make mistakes, but 90% is done correctly and work 24/7 almost for free.\n\n**Ava** – Assistant specializing in creating assistants, equipped with instructions for building system prompts.\n\n**Leo** – Critic with knowledge in product strategy and design. Not afraid to question ideas and propose other solutions.\n\n**Samir** – Helps in building system messages for Arabic services, equipped with glossaries of nomenclatures known to our users.\n\n**WanderWise** – Specialist in planning budget travels focused on hiking and mountains.\n\nBasic principles of building assistants:\n\n* Specify the assistant's role and specialization area\n* Define the assistant's goal\n* Determine the response form and structure\n* You can refer to additional materials (e.g., glossary of terms)\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1751886890/Zrzut_ekranu_2025-07-7_o_13.14.28_sjjzek.png)\n\n## Key Conclusions\n\n1. **AI is a tool, not a threat**. Small steps lead to big changes – at first, you will use chat more consciously, then you will start generating images. Finally, you will enter the world of automation and creating your own assistants.\n2. **Experimentation is key**. Anyone can start now. You do not need to know all the tools – find 3-4 that fit your work. Do not be afraid to test and explore different approaches and solutions.\n3. **Remember:** AI will not replace you, but a person using AI might replace someone who does not use it!\n\n> If this article sparked your curiosity, I recommend following Design Practice and Grzegorz Rog – the organizers of the AI Designer course, which gave me solid foundations for further AI adventures."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1751891033/gghj_tjfomq.png","lead":"**Hello, my name is Joanna Sanetra and I am a Product Designer at Boldare.** I would like to share with you what I have learned during a four-week AI Designer course organized by Design Practice and led by Grzegorz Rog. The results of my work were presented at DevCamp (an event bringing together all Boldare employees) in June 2025. **Although the course’s name may suggest it is dedicated only to designers, its scope was so broad that I recommend it to anyone working on computers on a daily basis.**","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-07-07T10:56:09.668Z","slug":"ai-tool-of-possibilities-from-simple-rhymes-to-a-revolution-in-daily-work","type":"blog","slugType":null,"category":null,"additionalCategories":["Ideas","Digital Product"],"url":null},"author":"Roksana Kaczmarska","authorAdditional":"","box":{"content":{"title":"AI - Tool of possibilities: From simple rhymes to a revolution in daily work","tileDescription":"Discover how AI is reshaping creative and professional tasks, featuring real insights and experiences shared by Joanna Sanetra.","coverImage":""},"coverImage":null}},"id":"2f4347e0-c0d0-5cfd-8777-675a2382e62e"}},{"node":{"excerpt":"","fields":{"slug":"/blog/top-5-software-development-companies-in-poland-2025/"},"frontmatter":{"title":"Top 5 Software Development Companies in Poland [2025]","order":null,"content":[{"body":"## 1. BOLDARE\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1748895024/29_bepece.png)\n\nFounded in 2004, [Boldare](https://www.boldare.com) is a trusted partner in building and scaling digital products through every market shift. The company delivers solutions across all development stages — from MVP and product-market fit to platform scaling and market expansion.\n\nBoldare specializes in addressing complex technical challenges such as system migrations, legacy modernization, architectural optimization, and large-scale integrations. By leveraging AI tools daily, they accelerate delivery by 20–40% while maintaining high standards of engineering craftsmanship, quality, and transparency.\n\nA core part of Boldare’s approach is close collaboration between designers and developers, creating user-friendly, elegant products. When design is already established, they efficiently utilize existing design systems to ensure speed and consistency.\n\nTrusted by global brands like BlaBlaCar, Bosch, and Decathlon, Boldare particularly supports mid-sized companies such as Sonnen, Prisma, and e.l.f. Cosmetics in scaling smarter, modernizing technology, and confidently growing their business.\n\n**Why Boldare is in the Top 5:**\n\n* Nearly two decades of experience navigating market shifts and delivering digital solutions\n* Expertise in complex engineering tasks combined with AI-augmented delivery\n* Strong focus on design collaboration ensuring high usability and product quality\n* Proven track record with global brands and growing mid-market companies\n\n## 2. Future Processing \n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1751550654/39_q3uav6.png)\n\nFuture Processing is one of the largest IT companies in Poland, operating continuously for over 20 years. With hundreds of completed projects under its belt, the company serves clients across industries such as finance, automotive, telecommunications, and manufacturing.\n\nWhat sets Future Processing apart is their unwavering focus on code quality, system scalability, and top-tier technical support. Known for a client-centric and flexible approach, they tailor their projects precisely to meet the specific business requirements of each client.\n\n**Why is Future Processing in the Top 5?**\n\n* Extensive experience and a broad portfolio of projects\n* Use of advanced technologies and modern development methodologies\n* High standards of quality and reliability in delivered solutions\n\n## 3. Celadonsoft\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1751550654/40_vqw8ol.png)\n\nEstablished in 2016 and headquartered in Warsaw, Celadonsoft specializes in end-to-end web development, SaaS platforms, and scalable backend systems. With a flawless **5.0/5** rating from 27+ Clutch reviews, clients applaud their communication, adaptability, and project transparency. They excel in industries like education, healthcare, social media, gaming, and logistics, delivering robust, maintainable solutions.\n\n**Why Celadonsoft stands out:**\n\n* Perfect Clutch score for cost efficiency and quality\n* Enterprise-grade web platforms with strong UX focus\n* International presence (Warsaw, Lisbon, Dubai) with global reach\n\n## 4. Momentum\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1751550654/41_cje4aw.png)\n\nMomentum (formerly Applover), based in Wrocław, is a full-stack digital agency founded in 2016. With a team of around 150 professionals and more than 200 successful projects, the company delivers end-to-end mobile and web development, backed by robust UI/UX design capabilities. Momentum supports clients across industries such as healthtech, fintech, e‑commerce, and entertainment.\n\nTheir technology stack includes Swift, Kotlin, Flutter, Vue.js, Node.js, and more, allowing them to build scalable, future-ready solutions. Recognized for their strong Clutch presence with a **4.9/5** rating and ranked 3rd on Clutch’s “100 Fastest-Growing Companies 2023,” Momentum combines technical excellence with a business-focused, agile approach.\n\n**Why Momentum deserves a spot in the Top 5:**\n\n* Consistently high Clutch reviews for quality, transparency, and delivery\n* Expertise in building MVPs and scaling digital products across verticals\n* Strong integration of tech and design, ensuring seamless digital experiences\n\n## 5. Pagepro\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1751550654/42_vhrain.png)\n\nPagepro, based in Białystok since 2017, focuses exclusively on **React.js** and **React Native**, delivering tailored web and mobile apps. With a **4.9/5** rating from 31+ reviews on Clutch, clients praise their professionalism, timely delivery, and deep technical expertise. Their main services include web development, custom software, mobile apps, and API integration, often in fintech, e‑commerce, and healthcare.\n\n**Why Pagepro matters:**\n\n* Deep React/React Native specialization and agile delivery teams\n* Excellent Clutch recognition for communication and quality[](https://celadonsoft.com/news/celadon-is-a-clutch-champion-winner-and-a-clutch-global-awardee?utm_source=chatgpt.com)\n* Polish HQ with global project reach"}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1751548472/grafika_lista_kvikjm.png","lead":"**Poland’s IT scene** is rapidly growing and gaining international recognition. More and more tech companies from the country deliver innovative, scalable, and end-to-end solutions for clients across industries — from startups and mid-sized businesses to global enterprises.\n\nIn this list, we highlight **five standout software development companies** known not only for their experience but also for their commitment to quality, **forward-thinking technologies, and ability to tackle the most complex projects**. Each company brings unique strengths — from automation and cloud solutions to comprehensive tech support and advanced AI-powered mobile and web applications.\n\n**Discover the leaders of Poland’s IT market who are driving business growth and setting new standards in software development in 2025.**","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-07-04T08:52:12.038Z","slug":"top-5-software-development-companies-in-poland-2025","type":"blog","slugType":null,"category":null,"additionalCategories":["Digital Product"],"url":null},"author":"Roksana Kaczmarska","authorAdditional":"","box":{"content":{"title":"Top 5 Software Development Companies in Poland [2025]","tileDescription":"Discover the top 5 software development companies in Poland in 2025 — from AI-powered builds to scalable enterprise solutions and product design excellence.","coverImage":""},"coverImage":null}},"id":"e6b690db-2671-5fd5-b804-716c93d94e14"}},{"node":{"excerpt":"","fields":{"slug":"/blog/three-ways-we-design-products-today-and-why-sometimes-not-designing-is-the-smartest-design-move/"},"frontmatter":{"title":"Three ways we design products today. (And why sometimes, not designing is the smartest design move)","order":null,"content":[{"body":"Back when we were designing in the days before YouTube existed, interface design was everything. Today? **Design is still everything — but it looks completely different.**\n\nI don’t see design today as a matter of polishing screens. It’s about orchestrating ecosystems of experience: between tools, users, delivery channels, and real-world use cases. That’s where the real value lies. (More on that in the [NNg UX Podcast](https://www.nngroup.com/podcast/) episodes on service design.) We co-create digital platforms for renewables, education, customer panels, and internal portals. And often, our strategic design choice is to skip traditional \"design\" altogether. According to IDC and Gartner, by 2027, up to 60% of user interactions will occur via invisible, AI-driven interfaces, making it more important than ever to think beyond screens and UI elements. Because sometimes, no UI is better UX.\n\nAs Erika Flowers noted in The Future of Service Design: \n\n> In the near future, your ‘client panel’ might just be a friendly face that asks, ‘What do you need today?’ — instead of a multi-tabbed dashboard.\n\n## Why do we offer three different ways to approach design?\n\n(And how this benefits your product when working with a European software development company).\n\nNot all products need the same type of design involvement. That’s why we’ve developed a simple framework- three levels of design execution- that lets us match the right approach to your business reality:\n\n### 🟡 1.0 Interface Craft\n\nPixel-perfect UI, components, tidy visuals when you need to make something beautiful and functional. Think polished dashboards, refined components, and WCAG-friendly screens. But let’s face it: this is no longer the future of design.\n\n### 🔵 2.0 Experience Optimisation\n\nUser journey mapping, UX KPIs, A/B testing when you're ready to test and tune how people experience your product. This is where good design starts to show real ROI. UX is a growth tool here. Not just aesthetics.\n\n### 🟣 3.0 Strategic Design Orchestration\n\nAI, no-design, service design, business-driven UX. This is where we often start today. With tools like LLM-assisted flows, zero-interface design, or orchestrating systems instead of screens. Here, we work with you on service logic, customer experience strategy, and design systems that serve your goals, not decorate your app.\n\n## Design execution modes\n\nWhat may be included in our activities\n\n
Capability 1.0
Interface Craft: light, fast, tidy 		2.0
Experience Optimisation: insight-driven growth loops 		3.0
Strategic Orchestration: AI / bespoke / bet-chosen for ROI
Dedicated Design System or tailored UI/UX Starter token set + 10 - 12 core components • 1-to-1 visual refactor to ready screens Full Design System (tokens 20, theming, motion) • Brand-fit research • unique patterns DS governance Decision matrix • Scaffold vs hand-craft • DS linked to product portfolio roadmap
Data-Driven Growth Plug-in analytics (heat-maps, rage-clicks) • Heuristic quick wins to cut friction A/B tests SUS surveys, funnel KPIs feed backlog • \"Experiment → Learn → Ship\" sprints Always-on telemetry auto-spawns growth epics • ROI model = iteration cost vs ∆ metric
Growth Management (User flows & IA + Delivery Plan) Information Architecture hygiene labels, redirects, dead-end fixes • UI/IA bug tickets in Backlog Journey maps, flow redesign, shared IA-backlog map Portfolio roadmap prioritises IA, UX or AI bets by OKRs • Capacity & budget woven into Delivery-Plan template
Design from Scratch (MVP / brand new product) Rapid brand essentials (logo craft, palette, type) • Skeleton screens on off-the-shelf DS • Lean canvas ready in days Full discovery workshops, persona research, prototype validation • Bespoke brand guidelines + MVP UI library End-to-end product & brand strategy Modular DS built alongside business model • AI-assisted iterations for go-to-market speed
\n\n![]()\n\n## What does design mean for us today?\n\nIt’s not about drawing rectangles anymore. It’s about:\n\n* Choosing the right channel: UI, chatbot, form, automation, voice\n* Matching user intent with the business model\n* Understanding when AI can handle the UX faster, cheaper, and better\n* Reducing friction by sometimes removing the interface entirely\n* Listening to actual user behavior — not personal preference\n\nThis is service design in practice. And we’re doing it every day: as a product partner and software development company for clients across sectors.\n\nAs we often say at Boldare, design isn’t something we layer on top of the product. it’s embedded in how the product actually works.\n\n## Design is still critical. But not where you think.\n\nThe question today isn’t \"how should this look?\" It’s: \n\n* \"What role does design play in getting this product to market fast?\"\n* \"Where can AI support or replace manual design?”\n* \"Is this even a design challenge or a delivery/communication one?\"\n* \"How do we make this work across cultures, devices, and expectations?\"\n\nWe explore all of this, especially when working with nearshore clients in the EU and US who want smart design, not bloated sprints.\n\n## So… when is design really needed, and in what form?\n\nIf you're building platforms in sectors like renewables, education, or public services, you might not need a full-time designer. You need clarity:\n\n* What kind of experience are we enabling?\n* What’s the smartest way to get there?\n\nSometimes it’s a UI. Sometimes it’s an invisible flow. Sometimes, it’s no interface at all. It’s not about having a designer, It’s about choosing the design approach that fits.\n\n## Want to go deeper?\n\nWe recommend checking the [NNg UX Podcast](https://www.nngroup.com/podcast/) episodes on \"Invisible Interfaces\" and \"Service Design vs. Product Design\", they echo much of what we’re seeing with clients every week.\n\nLearn more about how we do [UX strateg ](https://boldare.com/services/product-design/)or [schedule a conversation](https://calendly.com/) if you'd like to reflect on your product’s direction: no pitch, just product talk."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1755763545/Frame_ybfjib.png","lead":"I’ve been building digital products for nearly two decades, first as **co-founder and CEO** of [Chilid: a hi-end design agency](https://chilid.com) working with international clients across industries. Later, as Boldare took shape, I continued working **closely with our clients** — facilitating business workshops, observing how their challenges evolved, and helping them translate those into user-centered digital solutions. I witnessed firsthand how the role of design shifted from crafting interfaces to shaping entire service ecosystems. \n\nThis perspective still shapes how we approach design today. **As a nearshore software development company in Europe**, we build digital products from scratch and also join projects at various stages. We work side by side with clients around the world, **helping them turn complex ideas into real-life solutions**. We integrate into their teams, co-create digital tools, and support fast-paced product development with the smartest use of design.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-07-03T08:27:07.532Z","slug":"three-ways-we-design-products-today-and-why-sometimes-not designing-is-the-smartest-design-move","type":"blog","slugType":null,"category":null,"additionalCategories":["How to","Digital Product","Ideas"],"url":null},"author":"Anna Zarudzka","authorAdditional":"","box":{"content":{"title":"Three ways we design products today (and why sometimes, not designing is the smartest design move)","tileDescription":"Discover three modern approaches to product design and learn why sometimes the smartest choice is… not designing at all. This article shows how to use design wisely to create intuitive and effective solutions. See how the role of the designer is evolving in the world of digital products.","coverImage":""},"coverImage":null}},"id":"6f584141-ea3f-5dbc-95d2-919cd02c447b"}},{"node":{"excerpt":"","fields":{"slug":"/blog/top-10-legacy-modernization-companies-for-enterprise-it-transformation-in-2026/"},"frontmatter":{"title":"Top 10 Legacy Modernization Companies for Enterprise IT Transformation in 2026","order":null,"content":[{"body":"## Boldare\n\n![Boldare – #1 legacy modernization company for enterprises in 2026, offering AI-native delivery, cloud migration, and architecture optimization. Based in Gliwice, Poland. ](https://res.cloudinary.com/de4rvmslk/image/upload/v1776163109/01_boldare_1_euu7sh.png \"Boldare – Best Legacy Modernization Company for Enterprises 2026\")\n\n**Clutch:** \n\n**Location:** Gliwice, Poland\n\n### Company Overview\n\nBoldare is a digital product and transformation consultancy with more than 20 years of experience spanning the full project lifecycle – from early-stage discovery and MVP development through to large-scale platform builds and legacy system overhauls. Their self-organizing team model (Holacracy) and product-first culture support rapid, high-quality delivery on technically demanding enterprise engagements.\n\n### Legacy Modernization Approach\n\nBoldare treats modernization as an ongoing discipline rather than a single, high-risk rewrite. Their methodology layers system diagnostics, architectural redesign, UX refinement, and iterative delivery – allowing enterprises to modernize business-critical systems without disrupting live operations. Core competencies include system migrations, architecture optimization, enterprise-grade integrations, and cloud readiness assessments.\n\n### Key Enterprise Services\n\nBoldare offers legacy modernization and architecture services, end-to-end digital product development, AI and technology consulting, and product strategy including discovery workshops and roadmapping.\n\n### AI-Powered Capabilities\n\nBoldare embeds AI throughout delivery – from UX design and code generation (including Claude Code expertise) to API optimization and predictive scaling. Use cases include AI-driven performance profiling for traffic bottlenecks and automated testing within legacy upgrade workflows. Their AI-native delivery model accelerates timelines while improving observability and SLO adherence, positioning them as a standout partner for intelligent modernization at scale.\n\n## Devox Software\n\n![Devox Software – #2 legacy modernization company in 2026, specializing in code refactoring, technical audits, and architectural migration for fintech and SaaS clients. Based in Miami & Kyiv.](https://res.cloudinary.com/de4rvmslk/image/upload/v1776163109/02_devox_software_1_olmhkm.png \"Devox Software – Top Legacy Modernization Company 2026\")\n\n**Clutch:** \n\n**Location:** Miami, USA\n\n### Company Overview\n\nDevox Software provides legacy system modernization alongside technical audits, code refactoring, and architectural migration services. The company incorporates AI tooling to support accelerated delivery and offers full-cycle development services including custom software builds. They primarily serve small and medium-sized enterprises in fintech, fleet management, and SaaS, and maintain a track record of verified project reviews on Clutch.\n\n## Leobit\n\n![Leobit – #3 legacy modernization company in 2026, specializing in .NET development, Azure and AWS cloud migration, and AI/ML integration. Based in Lviv, Ukraine.](https://res.cloudinary.com/de4rvmslk/image/upload/v1776163109/03_leobit_1_x4ub2v.png \"Leobit – Leading .NET Legacy Modernization Company 2026\")\n\n**Clutch:** \n\n**Location:** Lviv, Ukraine\n\n### Company Overview\n\nLeobit is a software development firm specializing in .NET development, legacy system migration, AI/ML integration, and cloud transformation on Azure and AWS. Their modernization engagements involve transitioning systems from older technology stacks – such as WCF and WinForms – to contemporary architectures. Their client base spans sports technology, jewelry supply chain management, CNC manufacturing, dermoscopy, and real estate.\n\n## Inoxoft\n\n![Inoxoft – #4 legacy modernization company in 2026, offering legacy upgrades, data migration, and cloud integration in .NET, React, and Azure for healthcare and fintech enterprises. Based in Lviv & Philadelphia.](https://res.cloudinary.com/de4rvmslk/image/upload/v1776163109/04_inoxoft_1_tlbb9h.png \"Inoxoft – Enterprise Legacy Modernization & Cloud Integration 2026\")\n\n**Clutch:** \n\n**Location:** Lviv, Ukraine / Philadelphia, USA \n\n### Company Overview\n\nInoxoft delivers custom software development with a concentration on legacy upgrades, data migration, and cloud integration using .NET, React, and Azure for clients across the US and EU. They engage with enterprises in healthcare, fintech, and logistics through both team extension and full-project delivery models. Clutch highlights over 74 verified engagements covering scalable enterprise solutions.\n\n## DevCom\n\n![DevCom – #5 legacy modernization company in 2026, delivering B2B legacy system enhancements and new application development for fintech and enterprise software clients. Based in Florida & Lviv.](https://res.cloudinary.com/de4rvmslk/image/upload/v1776163109/05_devcom_1_cguvvl.png \"DevCom – B2B Legacy Modernization & Enterprise Software 2026\")\n\n**Clutch:** \n\n**Location:** Florida, USA / Lviv, Ukraine \n\n### Company Overview\n\nDevCom is a custom software development company focused on legacy system enhancement and new application development for B2B clients in fintech and enterprise software. The company has earned Clutch Global recognition and engages clients in finance and custom tooling through long-term partnership models. Their stated priorities are secure, scalable platform delivery with consistent quality.\n\n## Celerik\n\n![Celerik – #6 legacy modernization company in 2026, providing custom development and legacy upgrades for mid-market businesses with React front-end expertise. Based in Denver, USA.](https://res.cloudinary.com/de4rvmslk/image/upload/v1776163109/06_celerik_1_dmsr7b.png \"Celerik – Mid-Market Legacy Modernization Company 2026\")\n\n**Clutch:** \n\n**Location:** Denver, USA\n\n### Company Overview\n\nCelerik is a software development company with offices in both the United States and Colombia, offering custom development and legacy system upgrades. The firm has received Clutch B2B recognition for work in oil and gas and enterprise application development. Their technical focus encompasses React-based front-end development and legacy modernization projects, typically for mid-sized businesses seeking cost-effective development options.\n\n## MTechZilla\n\n![MTechZilla – #7 legacy modernization company in 2026, transforming monolithic systems into microservice architectures and delivering cloud migration for European scale-ups. Based in Chicago, USA.](https://res.cloudinary.com/de4rvmslk/image/upload/v1776163109/07_mtechzilla_1_anzstv.png \"MTechZilla – Microservices & Cloud Legacy Modernization 2026\")\n\n**Clutch:** \n\n**Location:** Chicago, USA \n\n### Company Overview\n\nMTechZilla is a software development firm offering legacy application modernization and custom software development, with a primary focus on European clients. Their technical engagements include decomposing monolithic applications into microservice architectures and executing cloud migration projects. They serve scale-ups and data-intensive businesses and maintain an active Clutch presence.\n\n## Taazaa\n\n![Taazaa – #8 legacy modernization company in 2026, offering agile legacy application modernization and greenfield development with a strong UI/UX focus for healthcare clients. Based in Hudson, USA.](https://res.cloudinary.com/de4rvmslk/image/upload/v1776163109/08_taazaa_1_hmuo2q.png \"Taazaa – Agile Legacy Modernization for Healthcare 2026\")\n\n**Clutch**: \n\n**Location**: Hudson, USA \n\n### Company Overview\n\nTaazaa is a custom software development company providing legacy application modernization and greenfield development for clients in healthcare and enterprise markets. They apply agile delivery methods with a strong emphasis on UI/UX design. The company primarily serves mid-market businesses and Ohio-based firms, and has accumulated 28 verified reviews on Clutch.\n\n## The Smyth Group\n\n![The Smyth Group – #9 legacy modernization company in 2026, specializing in billing and automotive industry modernization under the Integrity Billing and Xcite brands. Based in San Diego, USA.](https://res.cloudinary.com/de4rvmslk/image/upload/v1776163110/09_the_smyth_group_1_mq2bzh.png \"The Smyth Group – Billing & Automotive Legacy Modernization 2026\")\n\n**Clutch:** \n\n**Location:** San Diego, USA \n\n### Company Overview\n\nThe Smyth Group is a US-based software development firm specializing in legacy modernization for clients in the billing and automotive industries. Operating under the Integrity Billing and Xcite brand names, they provide services including architecture redesign, systems integration, and custom software development. Their typical clients are mid-market enterprises looking for domestically built technology solutions.\n\n## Facile Technolab\n\n![Facile Technolab – #10 legacy modernization company in 2026, providing IT staff augmentation, ASP.NET upgrades, and system integrations for SMEs in HR and enterprise software. Based in Ahmedabad, India.](https://res.cloudinary.com/de4rvmslk/image/upload/v1776163109/10_facile_technolab_1_xueudr.png \"Facile Technolab – SME Legacy Modernization & IT Staff Augmentation 2026\")\n\n**Clutch**: \n\n**Location**: Ahmedabad, India \n\n### CompanyOverview\n\nFacile Technolab is a software development company providing IT staff augmentation, custom software development, and legacy modernization services, including ASP.NET upgrades. They work with small and medium-sized enterprises across a range of industries, with a focus on system integrations and scalable software solutions. The company has 30 verified reviews on Clutch, with delivered projects spanning HR software and enterprise tooling.\n\n## FAQ\n\n**1. What is legacy modernization and why do enterprises need it?** \n\nLegacy modernization is the process of updating or replacing aging IT infrastructure – such as COBOL mainframes, monolithic applications, or on-premise ERP platforms – with modern, scalable architectures. Enterprises pursue it to reduce technical debt, lower ongoing maintenance costs, facilitate cloud adoption, and unlock technologies like AI and real-time data processing.\n\n**2. What services do legacy modernization companies typically offer?** \n\nMost firms in this space offer some combination of system migration, application re-architecture, cloud transformation, code refactoring, API integration, and technical audits. Many also provide UI/UX modernization, AI integration, and managed services to support operations after the migration is complete.\n\n**3. How long does a legacy modernization project typically take?** \n\nTimelines depend heavily on system complexity, project scope, and the modernization strategy selected. Targeted refactoring or migration efforts may be completed in a matter of months, while comprehensive enterprise transformation programs can span anywhere from one to three years.\n\n**4. Which company is the best for legacy modernization in 2026?** \n\nBoldare ranks as the leading legacy modernization company for enterprise transformation in 2026. The assessment evaluated technical capabilities, delivery methodology, sector experience, and proven outcomes across complex, large-scale modernization programs.\n\n**5. What is the difference between legacy modernization and digital transformation?** \n\nLegacy modernization addresses the specific challenge of updating or replacing outdated technology infrastructure. Digital transformation is a broader strategic initiative that encompasses modernization but extends to process redesign, organizational change, and the adoption of new business models made possible by modern technology."}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1776155734/legacy_modernization_fpotgn.png","lead":"Outdated technology infrastructure holds enterprises back. As maintenance expenses climb, development cycles grow longer, and the adoption of modern capabilities – AI, cloud computing, real-time analytics – becomes increasingly difficult. The question most organizations face today is not whether to modernize, but which partner is best equipped to lead the effort.\n\nLegacy modernization vendors help enterprises retire or upgrade aging IT assets: COBOL-based mainframes, on-premise ERP systems, and tightly coupled monolithic applications. These specialists bring expertise in cloud migration, application re-architecture, automated code conversion, and AI enablement – helping businesses shed technical debt and build infrastructure that can scale with future demands.\n\nThis list ranks 10 leading legacy modernization companies suited for enterprise transformation in 2026. Each has been assessed based on technical depth, sector experience, modernization methodology, and demonstrated delivery on complex, large-scale engagements – giving technology leaders a reliable foundation for the vendor evaluation process.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-07-01T09:33:30.497Z","slug":"best-legacy-modernization-companies-2026","type":"blog","slugType":null,"category":null,"additionalCategories":["Digital Product","Strategy","Tech"],"url":null},"author":"Aleksander Dąbrowski","authorAdditional":"","box":{"content":{"title":"Top 10 Legacy Modernization Companies for Enterprise IT Transformation in 2026","tileDescription":"Discover the top 10 legacy modernization companies for enterprise IT transformation in 2026. Ranking created to help you choose the right vendor.","coverImage":""},"coverImage":null}},"id":"24d28c83-4179-557b-a5a0-e7f992bba607"}},{"node":{"excerpt":"","fields":{"slug":"/blog/how-to-ai-augment-your-dev-team-with-cursor-ai-ide-interview-with-maksymilian-mogilski/"},"frontmatter":{"title":"How to AI-augment your dev team with cursor AI IDE? Interview with Maksymilian Mogilski","order":null,"content":[{"body":"## AI augmentation in software development: a new era\n\nAs Max puts it, “Yes, it is a game-changer,” but it’s important to recognize that AI isn’t here to replace developers—it’s here to augment their work. AI tools like Cursor are designed to handle repetitive tasks that would normally take up a developer’s valuable time, allowing them to focus on more creative, complex aspects of development. This shift is what makes AI truly transformative in the software development lifecycle.\n\n## AI in onboarding: reducing the time to get started\n\nWhen joining a new project, developers used to spend a significant amount of time understanding the codebase. Max recounts his experience with a project that had been in development for over seven years. \n\n> A few years ago, joining a project meant finding someone to mentor you and teach you about the codebase, how the system works, and the business logic. It took a lot of time and money, now, AI changes that. If I’m unfamiliar with the code, I can just ask the AI what a specific piece of code does, and it will explain it to me. Sometimes, it can even help me fix bugs.\n\nWith AI, new developers can get up to speed much faster. No more waiting for colleagues to teach you the intricacies of a project. AI helps provide the context quickly and efficiently, enabling developers to dive in without losing time.\n\n## AI: a tool, not a replacement\n\nMax stresses that while AI is incredibly helpful, it’s not replacing colleagues. “Building complex software is still a team sport,” he says. \n\n> AI can assist you with knowledge and context, but you still need a team for deeper problem-solving. It helps speed up the process, but it doesn’t replace collaboration.\n\nAI provides developers with tools to answer questions quickly or suggest solutions to problems, but human collaboration remains essential. AI is the assistant, but humans are the drivers.\n\n## Refactoring code with AI: more than just clean-up\n\nRefactoring code is another area where AI, particularly in Cursor, offers significant benefits. “Refactoring used to be tedious,” Max explains. \n\n> You’d go through the code, making sure everything was clean and well-organized. With AI, I can tell it to refactor a section of code, and it will suggest improvements.\n\nMax adds that, “It's not magic though. AI needs to be given context. You can’t just hand it a messy codebase and ask it to clean it up. Just like how you would explain something to a colleague, you need to be specific with AI about what you want it to do.”\n\nRefactoring is now faster, more efficient, and it ensures that the code is optimized for future development.\n\n## Cursor: an AI-first IDE for developers\n\nWhat sets Cursor apart from other tools is that it’s not a general-purpose AI like ChatGPT—it’s designed specifically for software development. Max compares the traditional way of coding with AI-enhanced tools: “In the past, when using AI tools like ChatGPT, I would have to paste my code into a window and ask questions about it. But Cursor is built to understand the context right from the start. You don’t need to jump between different tools or tabs; everything is integrated into one workspace.”\n\nWith Cursor, developers can directly attach their project files and give the AI full context on the codebase, making it easier for the tool to help them with coding tasks. This leads to a more streamlined and productive development process.\n\n## AI as a tool: the future of development\n\nAlthough there is much hype around AI, Max acknowledges that AI tools are still just tools that need to be used effectively. “It’s not about getting lazy with AI. It's like upgrading from a scythe to a combine harvester. Sure, you’re doing less manual work, but you still need to operate the tool,” he says.\n\nMax points out that, while AI is extremely powerful, it still requires skill and understanding to use properly. “It’s like using a combine harvester—if you don’t know how to use it, you’ll end up making mistakes. But if you do, it saves time and makes the job easier,” he continues.\n\nIn other words, AI doesn't replace developers, but it enhances their efficiency by automating tasks that would otherwise take up too much time and energy.\n\n## AI helps developers focus on what matters\n\nMax reflects on how AI allows developers to focus on higher-value tasks. “Instead of spending time writing tests or handling trivial tasks, AI can handle those. This gives developers more time to work on meaningful challenges that drive the project forward.”\n\nBy automating these repetitive tasks, AI is allowing developers to save time and energy, and focus on solving more complex problems and creating innovative solutions.\n\n## The future of AI in software development\n\nMax also believes AI will only get better over time. “AI will help us develop software faster, with fewer bugs, and better quality overall,” he concludes. “We are still in the early stages, but in a few years, I believe AI will be deeply integrated into every phase of the software development cycle—from planning to testing, to decision-making. It’s a bright future for developers who embrace it.”\n\nThe future of software development will undoubtedly be shaped by AI, but it’s important to note that developers must learn how to use AI effectively and understand the context in which they work. AI is not a one-size-fits-all solution, but when used properly, it can truly be a game-changer.\n\n## Try cursor and share your thoughts\n\nIf you haven’t tried Cursor yet, now is the perfect time. Dive into the world of AI-assisted development and experience how it can enhance your workflow. If you’ve already used it, share your experiences and let us know how AI is shaping your software development journey. We look forward to hearing your thoughts!"}],"job":null,"photo":null,"slug":null,"cover":"","lead":"In this insightful article, we explore the exciting intersection of AI and software development with **Maks Mogilski, a software engineer at Boldare**. We discuss how **Cursor, an AI-first Integrated Development Environment (IDE)**, is enhancing the way developers write code, collaborate, and solve problems in modern workflows. AI is revolutionizing development practices, and Cursor offers a unique way to supercharge your team’s productivity. From onboarding new developers quickly to fixing bugs with ease, Cursor empowers developers to do more, faster. Maks takes us through how AI tools like Cursor can transform the typical developer's daily routine and make even complex tasks, like **refactoring or exploring large codebases, easier.**\n\n**If you want to learn how AI can become a true development partner for your team**, we encourage you to listen to the full conversation and dive into the article for deeper insights. Whether you’re new to AI or looking to take your coding practices to the next level, this article and interview offer valuable tips and examples you won’t want to miss.\n\n","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-06-27T11:10:59.966Z","slug":"how-to-ai-augment-your-dev-team-with-cursor-ai-ide-interview-with-maksymilian-mogilski","type":"blog","slugType":null,"category":null,"additionalCategories":["Future","GenAI","Tech"],"url":null},"author":"Roksana Kaczmarska","authorAdditional":"","box":{"content":{"title":"How to AI-augment your dev team with cursor AI IDE? Interview with Maksymilian Mogilski","tileDescription":"In this insightful interview, Maksymilian Mogilski, a software engineer at Boldare, shares how the Cursor AI IDE is revolutionizing software development. Learn how AI tools like Cursor can streamline onboarding, improve code refactoring, and empower developers to focus on more creative challenges. Discover the future of AI in development and why Cursor is a game-changer for modern teams.","coverImage":"https://res.cloudinary.com/de4rvmslk/image/upload/v1751027714/ssss_konecg.png"},"coverImage":null}},"id":"69c566d3-bdd5-581d-bdc5-e615a0ac7c36"}},{"node":{"excerpt":"","fields":{"slug":"/blog/visit-to-leipzig-meeting-our-long-term-partner-prisma/"},"frontmatter":{"title":"Visit to Leipzig: meeting our long-term partner – PRISMA","order":null,"content":[{"body":"## PRISMA: A Digital transformation leader in the energy sector\n\nPRISMA is an international IT company managing one of the most crucial platforms for gas transmission in Europe. Serving over 20 markets and 3,000 players in the energy industry, PRISMA plays a pivotal role in integrating Europe’s energy infrastructure. The company’s mission goes beyond ensuring energy supply; they actively support the green transition through digital innovation and sustainable development strategies.\n\nOur partnership with PRISMA dates back several years and encompasses various technological projects supporting their mission. For more details about how we’ve built this relationship and the solutions we’ve implemented together, check out our blog post:\n\n\n\nDuring our meeting in Leipzig, we had the chance to exchange experiences, discuss the next steps, and gain a fresh perspective on our joint efforts. Such dialogues are invaluable for strengthening mutual understanding and advancing our shared projects.\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1750945771/IMG_7483_qmhfhc.jpg)\n\n## Gratitude and looking ahead\n\nWe want to extend our heartfelt thanks to the PRISMA team for their warm hospitality, inspiring discussions, and commitment to collaborative work. Partnering with such an innovative and forward-thinking company is both a challenge and a privilege.\n\nLeipzig, as always, charmed us with its vibrant energy, and the meeting with PRISMA was yet another milestone on our journey toward creating a sustainable energy future. We’re already looking forward to the projects ahead.\n\nThank you for your trust, and here’s to many more exciting challenges together! \n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1750945905/IMG_7462_ggvwbw.jpg)"}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1750945495/PRISM_onw11d.png","lead":"In may, we had the pleasure of visiting Leipzig, one of the most vibrant cities in Europe, to **meet with our long-term client, PRISMA**. A leader in the energy sector, [PRISMA](https://www.prisma-capacity.eu) specializes in ensuring stable energy supplies across Europe while driving the transition toward a more sustainable future.\n\nThe purpose of our visit was to deepen our collaboration, review past projects, and explore new opportunities for joint initiatives. Meetings like this allow us to better understand our partner’s goals and challenges while crafting innovative solutions tailored to the rapidly evolving energy market.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-06-26T12:30:11.751Z","slug":"visit-to-leipzig-meeting-our-long-term-partner-prisma","type":"blog","slugType":null,"category":null,"additionalCategories":["People","News"],"url":null},"author":"Roksana Kaczmarska","authorAdditional":"","box":{"content":{"title":"Visit to Leipzig: meeting our long-term partner – PRISMA","tileDescription":"Boldare recently visited Leipzig to meet with our valued long-term partner, PRISMA, a leader in the energy sector. The meeting focused on deepening our collaboration, reviewing past successes, and exploring new opportunities for joint initiatives that drive the transition to a sustainable energy future. Discover how our partnership continues to innovate and shape the future of energy in Europe.","coverImage":""},"coverImage":null}},"id":"8df582f9-fe0f-5892-b47c-27fe0cb7dbd6"}},{"node":{"excerpt":"","fields":{"slug":"/blog/work-anniversaries-with-impact-boldares-forest-is-growing/"},"frontmatter":{"title":"Work anniversaries with impact: Boldare's forest is growing","order":null,"content":[{"body":"## What impact does 224 trees make?\n\nPlanting trees isn’t just symbolic. It has tangible, measurable environmental benefits:\n\n* ✅ **Oxygen for 150 people annually** \n* ✅ **The equivalent of 12,992 kg of paper returned to nature** \n* ✅ **Ongoing CO₂ absorption every single year**\n\nIt’s our way of turning personal celebrations into something that supports the planet — and we couldn’t be prouder of that.\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1750686729/Zrzut_ekranu_2025-06-23_o_14.24.47_cnjycy.png)\n\n## We celebrated together at DevCamp \n\nThis year’s **work anniversary celebrations took place during our annual DevCamp** — a gathering where Bolders connect, learn, and unwind together. It was the perfect setting to recognize the people who’ve shaped our culture and growth over the years.\n\nEach person celebrating their anniversary received a **personal certificate** with the number of trees planted in their name. It was a powerful moment — filled with appreciation, community, and a shared sense of purpose.\n\nAt Boldare, impact matters — whether it's the code we ship, the culture we create, or the trees we plant.\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1750686582/IMG_3101_kjemcp.heic)\n\n## Tree planting with purpose\n\nPartnering with Posadzimy.pl is not just about numbers — it’s about planting trees the right way.\n\nEach tree is planted with care, following best practices to ensure healthy growth. The process begins with preparing the site: the hole must be wide and deep enough to support the root system without damaging it. After planting, the soil is gently packed to eliminate air pockets and give the tree a stable start.\n\nThis way, the trees have the best possible conditions to thrive — and we know we’re truly helping the environment.\n\nOur planting efforts support more than clean air. They help **restore ecosystems, improve soil quality, retain water, and increase biodiversity**. Trees act as natural filters — preventing erosion and enriching the soil, bringing life to the land around them.\n\n## Follow our forest 🌿\n\nWant to see how our company forest is growing? Check it out here:\n\n👉 "}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1750686279/Group_1000005021_xosnb4.png","lead":"At Boldare, we believe that milestones should be meaningful — not only for the people who achieve them but also for the world around us.\n\nThis year, we celebrated **23 work anniversaries** across our teams. And to mark this special occasion, we wanted to do something that reflects both our values and our long-term commitment to positive impact. That’s why, together with [Posadzimy.pl](https://lnkd.in/dDtc99Uz), we planted **224 trees** — one tree for every year of commitment from our amazing Bolders! 🌿","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-06-23T13:34:33.162Z","slug":"work-anniversaries-with-impact-boldares-forest-is-growing","type":"blog","slugType":null,"category":null,"additionalCategories":["Ideas","People","News"],"url":null},"author":"Roksana Kaczmarska","authorAdditional":"","box":{"content":{"title":"Work anniversaries with impact: Boldare's forest is growing","tileDescription":"We celebrated 23 work anniversaries by planting 224 trees with Posadzimy.pl. Discover how we turned team milestones into meaningful action for the planet.","coverImage":""},"coverImage":null}},"id":"9640ee7c-b00c-5366-ba60-f24f4a9ec638"}},{"node":{"excerpt":"","fields":{"slug":"/blog/the-product-lessons-no-one-talks-about-real-insights-from-boldare-at-scrum-summit-2025-by-co-ceo-anna-zarudzka/"},"frontmatter":{"title":"The product lessons no one talks about – real insights from Boldare at Scrum Summit 2025, by co-CEO Anna Zarudzka","order":null,"content":[{"body":"## From features to products: the start of Boldare's journey - discover Anna Zarudzka’s perspective\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1750415692/slajd1_gnqn2g.jpg)\n\nWhen I look back at Boldare’s journey, one thing stands out: how easily it’s possible to lose your strong product DNA, and how, at times, it takes a crisis to bring it back. Our path hasn’t been linear, but it’s been full of discoveries, hard lessons, and the relentless drive to create products that truly matter – to our clients, their users, and their business.\n\nI’m not a product person by education. My background is in film, but over the past 20 years, I’ve worked closely with clients on over 100 products across three different continents. This unique position has allowed me to witness the full product lifecycle, giving me a first-hand view of the market, cycles, and the challenges that come with creating real value. Yes, many of the insights I’m sharing are subjective – shaped by my own experiences in a constantly evolving business landscape.\n\nOver 11 years ago, when software houses were just starting to learn how to speak \"product\" instead of \"feature,\" we at Boldare decided to go beyond the buzzwords. We didn’t just talk about product thinking, we lived it. We introduced the product as a service, moving beyond hourly [development work](https://www.boldare.com/services/product-design-and-development/). This shift came from one simple belief: our role was to help clients discover what is truly worth building.\n\nBy 2013, the product mindset was becoming more prevalent, influenced by thought leaders like Roman Pichler, who introduced the role of the Product Owner in agile environments. Pichler’s teachings on maximizing business value, not just managing backlogs, became the cornerstone of how we approached product development. A great product isn’t just one that your customers love; it’s one that also drives tangible value for your business. \n\n## The reality of the market: when product thinking became a buzzword\n\nFor years, from 2013 to 2019, everything seemed to be on the right track. We were making the right moves, bringing in new roles like product strategists to help transform our software services into real product-building capabilities. But as the market evolved, the true nature of product thinking became diluted. Everyone started claiming to be “product-driven,” even if they had no structure, no experience, or no real understanding of what that meant.\n\nWe had processes, we had roles, but something wasn’t working. Despite all the right components, clients began to question the value we were offering.\n\nThat’s when things started to unravel. We had processes, we had roles, but something wasn’t working. Despite all the right components, clients began to question the value we were offering. “I feel like you’re more focused on lecturing me than listening,” said one client. ​​Another pointed out, “I don’t need workshops, I need solutions.” That was the turning point for us, as the quality of our services and client feedback have always been the ultimate measure of our work.\n\n## The crisis moment: product strategists and the build trap\n\nAs the market caught up, client payment habits started to shift, signaling new challenges. The questions became uncomfortable, and not just for us, for the whole industry. We started to realize that, despite having product strategists and designers, we were still missing the mark. We had locked our product teams in a \"glass bubble,\" separated from developers and the business side, yet they were supposed to deliver value to clients. We were measuring success not by the value delivered to the client or the business, but by metrics focused solely on the product itself—like the number of workshops, experiments, sophisticated canvases, or other internal outputs. \n\nWhen we started reflecting on our situation, we saw the larger pattern: many companies, including ours, had fallen into the trap of focusing on outputs rather than outcomes. We were measuring success by the number of workshops, not by the value those releases brought to the client or business. We were caught in the “build trap” – a term coined by Melissa Perri in her book Escaping the Build Trap, which refers to delivering functionality without considering its real business impact.\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1750421320/popraw2_u10xol.jpg)\n\n## The discovery: a product isn’t just a set of features\n\nWe were missing something critical: value. We had tools, processes, and rituals in place, but we didn’t have people who truly understood value from both a client and business perspective. We realized we needed to move away from a siloed approach, where product, business, and IT teams worked in isolation—and also educate our clients to ensure their environments don’t operate in this way.  If we wanted to create truly valuable products, we needed to blend these perspectives.\n\nWe needed business-minded people embedded in product teams.\\\nIt’s not enough to create a product your customers love; the product must also work for your business. We needed people who could go beyond the confines of the product itself and engage with the broader business environment in which the product operates. That’s when we made a key shift – we started integrating business leaders and financial perspectives directly into product teams. Every decision was now tied to company-wide goals, including revenue objectives and long-term strategy, rather than being limited to individual product outcomes.\n\n\n\n## The importance of responsibility: creating a culture of accountability\n\nWe slowly began to realize that true product ownership doesn’t just reside in the product manager or designer. It’s a shared responsibility, ingrained in every role, from developers to business leaders. At Boldare, we needed to foster an environment where every team member could take ownership of the product, understand its financial implications, and work together to create real business value. A product is not just a department, it’s a way of thinking within the whole company.\n\nWe started integrating business ambassadors into our product teams – individuals who had a deep understanding of business strategy and could help steer product decisions based on market realities. This also meant providing our product teams not only with direct access to sales, client feedback, and financial data, but also with clear goals, meaningful metrics, and the knowledge necessary to drive impactful decisions. If we didn’t know how our products were impacting the business, we couldn’t create value – it was that simple.\n\n\n\n## The shift to interdisciplinary, inter-business teams\n\nThis shift in responsibility extended to how we worked as teams. We broke down the traditional silos between business, IT, and product. Instead of treating the product as a separate department or a \"translator\" between business and IT, we embedded business knowledge into every team. Developers, designers, and product managers now worked side by side with business leaders who understood the market, financials, and client needs.\n\n## Reshaping structure and responsibility\n\nTo drive alignment between product development and business goals, we restructured our approach from a specialization-based model to a purpose-driven, business-oriented one. This transformation went beyond just tweaking processes—it was about redefining roles and responsibilities across the organization.\n\nWe introduced Product Launchers—senior developers and designers who not only executed tasks but also played a crucial role in shaping product strategy. At Boldare, everyone on the team has skin in the game. It’s not just about understanding the purpose behind our work—it’s about taking ownership of the business outcomes. This approach eliminates any \"bench time\" mentality: if there’s no product to develop, team members actively seek roles within the organization or move on.\n\nOur \"no sales team\" philosophy shifted the responsibility for business growth to entire business units. Leaders, teams, and even developers and designers were now directly accountable for results, including sales and client satisfaction. This required cross-functional teams that weren’t just interdisciplinary (development, design, product management) but inter-business, with members who understood the client's business model, revenue streams, and strategic objectives.\n\nTo ensure this alignment, we embedded business-savvy leaders—like Business Unit leaders or myself—into product development processes. These leaders worked alongside Product Designers, UX Strategists, and consultants, bringing a grounded understanding of the market and its impact on careers, earnings, and long-term success.\n\n## Metrics that matter: from outputs to outcomes\n\nOur shift in structure came with a transformation in how we measured success. We moved away from traditional output-focused metrics like the number of features shipped or sprints completed. Instead, we focused on metrics that reflected real business impact, both for us and our clients.\n\nKey metrics now included:\n\n* Revenue growth and segment performance, rather than just feature completion.\n* Strategic goal achievement over sprint completion.\n* Customer retention and the ability to drive long-term value.\n\nThis shift not only aligned our work with client and business goals but also created a culture of accountability and impact.\n\n\n\n## A Two-Way Street of Learning\n\nFinally, we committed to a two-way exchange of knowledge and responsibility. Business Unit leaders and teams became deeply involved in roadmaps, while developers and designers engaged directly with client feedback and business outcomes. This approach provided a holistic understanding of both the technical and business landscapes, enabling everyone to contribute meaningfully to strategic decisions.\n\n## The need for real business knowledge\n\nOne of the key lessons we learned was that product managers and team members must have a solid understanding of business. Without this knowledge, it’s impossible to make the right decisions or properly assess the risks and rewards of a product. This meant hiring people who had experience making tough business decisions – people who knew what it was like to lose money and feel the consequences of their decisions.\n\n![](https://res.cloudinary.com/de4rvmslk/image/upload/v1750421349/podgld3_i151i8.jpg)\n\nWe started to wonder how well our product teams truly understand the real business impact of their work. Do they realize how the features they build affect the company’s financial results? Knowledge about costs, margins, and profits isn’t just the finance team’s responsibility — it’s the foundation for building products that deliver real value, not just meet user expectations.\n\nIn this context, it’s crucial to regularly ask ourselves and the team questions that help connect technical decisions with business consequences:\n\n* Do people in product roles have experience running a business or have they ever faced a real failure?\n* Do the people designing or building the product understand the cost structure and margins of what they are creating?\n* Do they check how much has been sold every month?\n* Does anyone on the team understand the financial risks associated with missing this functionality?\n* Do developers and designers know which part of the product generates the most revenue or cost?\n* Has the team ever participated in an analysis of abandoned customers or declining sales?\n* Has the product/development team ever had the opportunity to ask a client: “Why didn’t you pay?” if you’re providing a product-building service for a client?\n* Do we talk about changes in the market or competition when planning sprints?\n* Can we connect a specific feature to a specific business outcome?\n* If the product doesn’t generate money, does the product management team know that they will be involved in layoffs?\n* Does your job/promotion depend on the financial outcome of the product?\n\nThese questions are not just a checklist – they represent a shift in mindset, a culture of accountability where product teams take responsibility for both the impact their product has on users and the business’s success. If your team isn’t asking these questions and connecting their work to the financial health of the company, it’s worth considering how this might limit your product's real potential.\n\n## Final thoughts: responsibility, growth, and building the right environment\n\nLooking back at the lessons we’ve learned, one thing is clear: we are all responsible for creating a product-driven environment. It’s not just the product managers or designers – it’s everyone in the company, from business leaders to developers. We all need to be involved in the product’s success, and we all need to take responsibility for ensuring that the product delivers real value.\n\nIf we’re not willing to build this environment – one where responsibility, business knowledge, and value creation are at the core – then we need to ask ourselves if we should continue down this path. The product will never thrive if it’s treated as just a department or a set of features. It must live and breathe within the entire organization, with everyone taking ownership of its success.\n\nFor those interested in diving deeper into the principles and strategies that informed our approach, we recommend the following resources:\n\n* [Roman Pichler, Agile Product Management with Scrum: A foundational guide for understanding how to effectively manage products in agile environments.](https://www.romanpichler.com/romans-books/agile-product-management-with-scrum/)\n* [Marty Cagan, Inspired, Empowered, Loved, Transformed: A series of essential works on building products that customers love and aligning teams with business viability.](https://www.amazon.com/Product-Hard-SVPG-Box-Set/dp/1394326262?utm_source=chatgpt.com)\n* [Melissa Perri, Escaping the Build Trap: How Effective Product Management Creates Real Value: A must-read on breaking free from delivering features for the sake of it and focusing on delivering true business value.](https://melissaperri.com/blog/2014/08/05/the-build-trap)\n* [Ben Horowitz, Good Product Manager/Bad Product Manager: A classic essay that highlights the differences between effective and ineffective product management practices.](https://sriramk.com/memos/Ben_Horowitz_Good_Product_Manager_Bad_Product_Manager.pdf)"}],"job":null,"photo":null,"slug":null,"cover":"https://res.cloudinary.com/de4rvmslk/image/upload/v1750413585/IMG_8234_ynnse8.heic","lead":"**The Scrum Summit 2025**, is the leading agile conference in Poland, took place in may 2025 in Warsaw. Among the distinguished speakers who shared their knowledge and experiences was **Anna Zarudzka, co-CEO of Boldare who served as a keynote speaker**. During her presentation titled **\"From promises to value - lessons from the product frontline\",** she shared extremely valuable insights on the challenges of product management, organizational agility, and the necessity of aligning business strategy with the real value delivered to clients. This article takes you on a journey **through the story we shared at the conference** – the story of Boldare. It's not just about ideas; **it's about the real-life experiences of a company that faced a significant crisis, overcame it, and emerged stronger**. Our co-CEO Anna Zarudzka shared how we navigated through **challenges, adapted to changes**, and learned invaluable lessons along the way. This is a personal and transformative narrative filled with reflections, practical insights, and a renewed vision for **leadership and innovation in the IT and product management sectors**.\n\nMay our story inspire you to see challenges not as obstacles, but as opportunities for growth and change. We now turn the floor over to **Anna Zarudzka - co-CEO of Boldare** - to share her unique perspective. **We invite you to read her personal account of Scrum Summit 2025.**.","templateKey":"article-page","specialArticle":false,"isNewWork":null,"isNewNormal":null,"service":null,"settings":{"date":"2025-06-20T09:40:22.466Z","slug":"the-product-lessons-no-one-talks-about-real-insights-from-boldare-at-scrum-summit-2025-by-co-ceo-anna-zarudzka","type":"blog","slugType":null,"category":null,"additionalCategories":["Ideas","How to","Digital Product","Strategy"],"url":null},"author":"Roksana Kaczmarska","authorAdditional":"","box":{"content":{"title":"The product lessons no one talks about – real insights from Boldare at Scrum Summit","tileDescription":"Discover untold product lessons shared by Boldare's co-CEO Anna Zarudzka at Scrum Summit 2025. Insights on aligning strategy, value, and agility for real business impact.","coverImage":""},"coverImage":null}},"id":"4023fa46-d429-5fa1-a540-27f7bbf71de5"}}]},"markdownRemark":{"id":"fe142a64-0080-5bfa-9e95-9104b7aaefb1","html":"","fields":{"slug":"/services/team-augmentation-services/"},"frontmatter":{"title":"Team Augmentation Services","settings":{"metaTitle":"Top Team Augmentation Services","metaDescription":"Need additional expertise or resources for your app? Boldare offers top team augmentation services to meet business demands without hiring full-time staff. Enhance your team with our skilled professionals today!\n\n","slug":"team-augmentation"},"hero":{"title":"Team Augmentation Services: Power Up Your Team","description":"Improve your development capabilities with Boldare's team augmentation services. Whether you need additional expertise or resources, our skilled professionals are ready to integrate seamlessly into your products. 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By choosing our team augmentation services, you can expect:","servicesItems":[{"item":"Experienced Professionals: Access a pool of skilled experts ready to jump into your projects."},{"item":"Cost-Effective Solution: Meet your business needs without the long-term commitment of hiring full-time staff."},{"item":"Flexibility and Scalability: Easily scale your team up or down based on project requirements."},{"item":"Seamless Integration: Our professionals integrate smoothly with your existing team and processes."}]},"benefits":{"title":"Why Should You Turn to Boldare for Team Augmentation Services?","description":"At Boldare, our primary focus is the success of our partners. We build long-term relationships by helping our clients achieve their business goals with their digital products. Our specialists support this by integrating seamlessly with your team to enhance your development capabilities. 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We guide it through market validation and on its journey toward product-market fit. From there, we work together to ensure continued success as you scale up even further."}},{"item":{"title":"Holistic Support","description":"When you join forces with Boldare, you gain access to the expertise of our entire organization. Our team includes software engineers, product designers, GenAI engineers, DevOps specialists, product strategists, and solution architects, all united in vibrant communities we call chapters. Their collective knowledge ensures your product receives the quality service it deserves."}},{"item":{"title":"Business Mindset","description":"Creating products that people love is at the heart of everything we do at Boldare. Our developers strive to build meaningful, not just functional, products. Our designers focus on providing a compelling user experience, and our consultants are dedicated to helping our clients achieve high-value returns. 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