Trust in AI interface design is built through specific design decisions: showing confidence levels, distinguishing high-certainty outputs from suggestions, giving users a way to override or correct the AI, and making it clear what the model did versus what a human confirmed. These aren't just visual choices – they're interaction patterns.

A good AI error state has two components traditional UX doesn't account for: a context-specific reason and a concrete next stepGeneric error messages are one of the most common causes of permanent user drop-off after a single failed interaction.

Traditional UX assumes predictable software behaviour. AI outputs vary, fail non-deterministically, and produce results users can't always verify. That requires a different set of patterns – for feedback loops, confidence display, graceful degradation, and human override – that most UX teams haven't had the chance to develop yet.

The ASSESS package takes 3 days and produces a prioritised fix plan your team can act on immediately. It covers every AI interaction in your product, identifies the top friction points, and gives you a concrete recommendation for each – without pausing your development cycle.

Under EU AI Act, transparency and human oversight need to be represented in the interface – not just in model documentation. Users need to know when they're interacting with AI, what it can do autonomously, and how to override it. These are design requirements, and they need to be built into the interaction layer from the start.

Yes – and that's the most common entry point. Most teams come to us after launch, when adoption data makes the interaction problems visible. The audit works on live products, identifies exactly where users are losing trust or dropping off, and produces a fix plan the team can implement without a full redesign.