AI UI/UX Design

The hard part of AI design

Any competent designer can lay out a chat box. The hard part is everything around it: What does the user see during a twelve-second generation? How does the interface say “I’m 60% sure”? What happens when the model returns confident nonsense? Where does a human check the output before it sends?

That 20% of the interface decides whether users trust the product — and it’s the part most portfolios have never shipped. It’s the part we specialize in.

Patterns we design daily

• Streaming and progress — progressive output with visible structure, so seconds feel like momentum instead of failure
• Confidence and uncertainty — hedges, thresholds and visual signals that keep user trust calibrated to actual model quality
• Citations and sources — answers that show their work, so verification is one click instead of an act of faith
• Failure and recovery — timeouts, refusals and bad outputs designed as first-class states with a next step, not dead ends
• Human-in-the-loop — review queues, approve/edit/reject flows and audit trails for outputs that trigger real consequences
• Input assistance — suggestions, templates and constraints that rescue users from the terror of the blank prompt box

We test the mediocre case, not the demo

An AI prototype that only shows perfect responses is a demo, and demos lie. We build prototypes wired to realistic outputs from your domain — including the slow ones, the hedged ones and the wrong ones — and put them in front of real users. Watching someone encounter their first hallucination tells you more about your trust design than fifty stakeholder reviews.

The result: interfaces where users recover, recalibrate and keep going — instead of quietly closing the tab and never coming back.

Design systems for AI products

We deliver AI-specific component libraries in Figma — streaming containers, citation chips, confidence badges, review queue patterns — with tokens that map to code. Your second AI feature ships faster than your first, and every feature speaks the same trust language.

Because we also build AI products end to end, every pattern we hand off has survived production — model costs, latency spikes, real users. Nothing in the file is speculative. If the AI lives on your marketing site instead, see AI website development; the same trust patterns apply to assistants and search.

Where to start

If you have an AI feature users don’t trust, start with an AI UX audit: we review flows against failure modes and deliver a prioritized fix list in two weeks. If you’re designing something new, book a scoping call — plan, price and date in writing.