Service
UX & Interface Design for AI Systems
Most AI products fail on the interface, not the model. The reasoning is sound and the output is good, but people can't tell when to trust it, can't see how it got there, and can't correct it when it's wrong — so they stop using it. We design the layer where that trust is either built or lost.
Why AI products break ordinary UX patterns
Conventional interfaces are built on a promise: the system does what it says. A button either works or it doesn't. A model is different — it is right most of the time, and a confident wrong answer looks exactly like a confident right one. Designing an AI product means designing the gap: how uncertainty is surfaced, how someone checks the work before acting on it, and what it costs them when the model gets it wrong. Skip that and you ship something people try twice and abandon.
Designing for trust, not just for output
Trust is not a tone of voice, it's a set of mechanics. Users need to see why the system reached a conclusion, review it before anything irreversible happens, correct it, and have the correction stick. That is what human-in-the-loop actually means in practice — not a confirmation dialog bolted on at the end, but review points designed into the flow at the moments where being wrong is expensive. The goal is a system that is legible and reversible, not one that hides the model behind a clean surface.
What the work involves
We map the decision points in your product and what each one costs if it goes wrong, then design around that: confidence and uncertainty states, empty and error states that explain rather than apologise, review and approval patterns, and onboarding that sets accurate expectations instead of overselling. Alongside that comes the visual and interaction system — the components, the hierarchy, the motion — so the whole thing ships as one coherent product rather than an AI feature stapled to an existing UI.
Who this is for
Teams putting an AI capability into a product that already has users, where the risk is disrupting workflows people rely on. Deep-tech and enterprise teams whose model is genuinely strong but whose adoption is slow, which is almost always an interface problem rather than a modelling one. And founders at zero-to-one who need the first version to be credible enough that people give it a real try.