Does AI test automation integrate with an existing CI/CD pipeline?

Yes. The automation layer integrates with your current pipeline – GitHub Actions, GitLab CI, Bitbucket, or others. No architectural rebuild is required before you start seeing results.

Does the current test suite need to be replaced?

The starting point is always what you already have. The audit maps your existing coverage, identifies the highest-value gaps, and the build phase extends from there. Nothing gets discarded without a reason.

What happens to the pipeline when the product changes?

AI-generated test suites are more resilient to UI and code changes than manually written ones, but they still require maintenance. The SCALE retainer covers exactly that: keeping coverage current as your codebase evolves, without the work landing back on your team.

Is AI test automation relevant for a small QA team?

Particularly relevant for constrained QA teams. AI test automation increases the coverage a small team can sustain – the goal is to shift manual effort away from writing and maintaining tests toward higher-value QA work.

How is AI test automation different from using Cypress or Selenium directly?

Testing tools execute tests. AI test automation generates, maintains, and extends them. The tools are part of the stack – the AI layer sits on top and handles what would otherwise be manual QA engineering work.

Can AI be used to test products that are themselves AI-powered?

Yes – and testing AI-powered products requires a different approach. Testing LLM outputs, agentic workflows, and non-deterministic features needs specific strategies that classical test automation wasn't designed for. Teams building AI products can address that scope in the BUILD phase.

What's the difference between QA automation services and just buying a testing tool?

Testing tools handle execution – they run the tests you point them at. QA automation services cover the full picture: assessing where your process loses time, building the pipeline that generates and maintains tests automatically, and keeping coverage current as the codebase changes. The tooling is part of the delivery, not the whole of it. Teams that buy a tool still need someone to write the tests, maintain the suite, and decide what to cover next. That's the work quality assurance automation is designed to remove from the manual backlog.

AI software testing automations to keep your release cycle on track

Traditional test automation solves the problem once – then someone has to maintain a suite that costs as much as it saves. AI changes that equation: generating test cases, detecting anomalies, and maintaining coverage without proportional manual overhead. Boldare delivers AI software testing that scales alongside your codebase, without rebuilding what already works.

Who has benefited from Boldare's expertise?

See the companies that trusted Boldare to get it done.

Why QA becomes a bottleneck as teams scale

As products grow, QA processes built for smaller codebases start to crack. The symptoms are predictable – even if the fix isn't obvious yet.

Every release stretches your automated regression testing cycle a little further
New features mean new test cases. New test cases mean longer regression cycles. At a certain point, the QA phase starts compressing the time available for everything else – and the team absorbs the pressure without a structural way out.
Your test suite is getting harder to maintain than it is to extend
Automated tests that were written six months ago break when the UI changes. Someone has to fix them. That someone is usually the person who was supposed to be writing new tests – so coverage stalls while maintenance grows.
Some parts of your codebase are well covered. Others are not.
Some areas are tested thoroughly. Others have minimal coverage because there was never enough time to get to them. When something breaks in an undertested area, it reaches production – and the post-mortem always points to the same gap.
Your QA capacity doesn't scale at the same pace as your product
Hiring more QA engineers buys time but not leverage. The ratio of test cases to people stays roughly constant, which means coverage growth depends entirely on headcount – and headcount has its own constraints.

EXPLORE YOUR OPTIONS

From audit to full automation – pick your starting point

Three phases, each with a fixed scope and a defined output. Start at the beginning or come in at the stage that matches your current situation.

ASSESS

QA Acceleration Audit

You get a clear picture of where your testing process loses time – and a concrete estimate of how much AI automation can recover. No commitment to build required.

2-3 daysFixed price

WHAT YOU GET:

1
Current process benchmark
A map of your existing test coverage, manual steps, and the points in your pipeline where time is lost most consistently.
2
AI opportunity assessment
An analysis of which parts of your QA process are the highest-value candidates for AI automation – ranked by time savings potential.
3
Time savings estimate
A concrete projection of hours saved per release cycle based on your actual setup, not industry averages.
4
Go/no-go recommendation
A clear recommendation on whether and how to proceed – with a proposed scope before you commit to anything.

Wondering how much time your team loses to QA? – Ask AI to estimate it with you

BUILD

AI Test Automation Setup

We build an AI-powered test automation pipeline integrated into your existing CI/CD. Test case generation, regression coverage, anomaly detection – set up and running in four weeks.

4-6 weeksFixed price per module / phaseMilestone-based

WHAT YOU GET:

1
AI test case generation
Automated generation of test cases from your codebase and user flows – covering scenarios that would take a QA engineer hours to write manually.
2
Regression pipeline setup
A working regression suite integrated into your existing CI/CD environment. No architectural rebuild required.
3
Coverage report
A before/after comparison of test coverage across your codebase – with a gap analysis for areas that remain undertested.
4
Handoff documentation
Full documentation of the pipeline so your team can maintain, extend, and adapt it without external dependency.

Curious what an AI-powered test pipeline would look like for your stack? – Ask AI to walk you through it

SCALE

QA Retainer

Continuous pipeline maintenance and coverage expansion as your product grows. Your test suite stays current with your codebase – without the overhead landing on your team.

ongoingMonthly retainerTiered retainer

WHAT YOU GET:

1
Pipeline maintenance
Regular updates to the test automation pipeline as your codebase evolves – so coverage doesn't degrade after new feature releases.
2
Coverage expansion
Systematic extension of test coverage into areas identified during the Audit or Build phases, prioritised by risk and release frequency.
3
Release-by-release reporting
A structured QA report after each release cycle – execution time, coverage delta, anomalies flagged, and recommended next steps.
4
On-demand QA consultation
Direct access to the Boldare QA team for questions, pipeline reviews, and coverage strategy as your product scales.

Wondering what keeping test coverage current with AI actually involves? – Ask AI to explore it with you

Start with numbers, not assumptions

You don't need to have everything figured out before you reach out. The QA Acceleration Audit shows you exactly where AI in test automation can recover time – execution time, coverage gaps, time savings estimate – before you commit to anything

GET IN TOUCH

AI test automation in practice

The thinking, numbers, and methods behind our QA work – written for engineering teams who want the details.

ARTICLE

Four approaches to generating test cases with AI, what each one solves, and where each one breaks down.

ARTICLE

Classical QA assumes a fixed input produces a fixed output. LLMs don't work that way. This is how to build a testing strategy that catches AI-specific failures, without doubling your QA headcount.

ARTICLE

When the Specification Became the Test: AI as a QA Agent in a Blockchain dApp

ARTICLE

Test coverage grows at the speed of the QA engineer typing. Here's the exact workflow for generating Playwright test cases with Claude, including where it breaks down.

AI software testing tools and stacks

We've built the most production evidence around the tools below. The AI-assisted QA workflow is transferable across environments – what matters is your pipeline, not ours.

Trusted by product teams across industries

Here's what clients say about working with us.

Allan Wilson

President - Team Alert

"I was really impressed with how much they cared about our product."

Jerome Defillon

Chief Technology Officer – Novolyze

"We were impressed with their capacity to embrace an unknown domain and challenge the strong assumptions presented."

Norbert Baumann

VP R&D – Sonnen

"They treat the customer portal as their product and this resulted in the high quality of their work."

Fabio Zecchini

Chief Technology Officer – Musement TUI Group

Boldare delivers results that meet our standards and expectations."

Christian Jennewein

Head of Engineering – BlaBlaCar

"Their customer-focused, Agile approach inspired us, and we discovered that we shared a similar mindset."

Head of Software Development

Prisma

"They had a very short ramp-up time and were dedicated to delivering."

Zvonko Grujic

Director Digital Engineering – Maxeon Solar Technologies

"I feel that my opinions and observations matter and that the team will adjust their actions based on our feedback."

See more on Clutch

4.8 / 5.0
58 verified reviews

Every review independently verified by Clutch.

COMMUNITY

Where product teams figure out the AI era together

Product Builders | AI-Native is a community for practitioners building digital products in the AI era – run by Boldare, powered by 20 years and 350+ products of hands-on experience.

We regularly go live with guests from product, design, and engineering for honest conversations about what building AI-native actually looks like in practice. Written recaps, articles, and show notes from every session live on Substack.

JOIN THE COMMUNITY

What working with Boldare actually looks like

We built our QA tooling for our own production environment before we offered it as a service. The numbers on this page come from that work – not from benchmarks or estimates. That's what software test automation services built on production experience look like.

Tooling built in production
The QA infrastructure behind every engagement was pressure-tested internally before it became a client offering. What you get has already run in a live environment.
Fits into your existing CI/CD – no rebuild required
The automation layer integrates with your current pipeline from day one. The setup works around your infrastructure, not the other way around.
Full ownership after handoff
Every engagement ends with documentation your team can maintain and extend independently. Continuing is an option, not a requirement.
One partner across the full QA cycle
From audit through build and ongoing maintenance – no handoffs between teams, no gaps between phases.

Your release cycle called – it needs help

Tell us what's slowing your releases down. We'll figure out the rest together.

Customer DevelopmentAleksandra Krzyżakbusiness@boldare.com

Frequently asked questions about QA automation

Answers to the questions engineering teams ask most often before starting an AI test automation engagement.

Classical test automation runs tests you've written manually. AI test automation generates the test cases themselves – from your codebase, user flows, and existing coverage gaps – and maintains them as the code changes. The difference is not just speed; it's the ratio of coverage a team can sustain per engineer – which is why AI software testing is increasingly the default for teams scaling past a certain codebase size.

client relationships

verified Clutch reviews

percentage of clients who return for another project

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Join our Team

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Services

Gliwice

Zwycięstwa 52 44-100 Gliwice Poland

Warsaw

Krucza 50 00-025 Warsaw Poland

Wroclaw

Wyspa Słodowa 7 50-266 Wroclaw Poland

Cracow

Kurniki 9 31-156 Cracow Poland

Boldare S.A. z siedzibą w Gliwicach, przy ul. Zwycięstwa 52, zarejestrowana w Sądzie Rejonowym w Gliwicach, X Wydział Gospodarczy Krajowego Rejestru Sądowego pod nr KRS 0000914518, NIP 6312698829, REGON 38958555. Wysokość kapitału zakładowego i wpłaconego 100 000,00 zł.

AI software testing automations to keep your release cycle on track

Who has benefited from Boldare's expertise?

Why QA becomes a bottleneck as teams scale

From audit to full automation – pick your starting point

QA Acceleration Audit

WHAT YOU GET:

AI Test Automation Setup

WHAT YOU GET:

QA Retainer

WHAT YOU GET:

Start with numbers, not assumptions

AI test automation in practice

AI software testing tools and stacks

Trusted by product teams across industries

Allan Wilson

Jerome Defillon

Norbert Baumann

Fabio Zecchini

Christian Jennewein

Head of Software Development

Zvonko Grujic

See more on Clutch

Where product teams figure out the AI era together

What working with Boldare actually looks like

Your release cycle called – it needs help

Frequently asked questions about QA automation

What does AI test automation actually do that classical automation doesn't?

Does AI test automation integrate with an existing CI/CD pipeline?

Does the current test suite need to be replaced?

What happens to the pipeline when the product changes?

Is AI test automation relevant for a small QA team?

How is AI test automation different from using Cypress or Selenium directly?

Can AI be used to test products that are themselves AI-powered?

What's the difference between QA automation services and just buying a testing tool?

Services

Gliwice

Warsaw

Wroclaw

Cracow