The Problems we hear about the most
The tool is impressive in the demo, but abandoned in a week.
AI tools with powerful models and poor interfaces lose users the moment novelty wears off, and the real task begins.
Developing users' trust in the output seems almost impossible.
When a model's reasoning is invisible, its answers feel arbitrary. An interface that shows no workings earns no confidence.
The prompt bar is not a UX strategy.
Defaulting to an open prompt means asking users to already know what to ask. Most don't.
The tool is built for the AI team's mental model, not the user's.
Products designed by people who understand the model often forget that every user isn't one of them.
Every edge case is a failure without a name.
Gen AI fails in unpredictable ways. The interface is unable to hold up through all the hallucinations, refusals, and erratic outputs.
What good Gen AI and chatbot UI design looks like
Interfaces that surface what an AI is doing, so the output feels earned rather than arbitrary.Guided input patterns for users who know what they need but not how to phrase it.Error and uncertainty states that are designed to hold user trust even when the model doesn't.Agentic workflow design where autonomous actions stay visible, legible, and stoppable.Feedback loops that let users correct, refine, and shape outputs over time.Some of the Clients we served
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The output is non-deterministic. In most products, a user does something and the same thing happens. In AI products, the same input can produce a different result every time — and the user has to stay confident through that variability. Designing for that means rethinking feedback, state, error, and trust from the ground up.
Transparency over magic. Users forgive AI errors when they understand why they happened — they abandon products when errors feel arbitrary. We design for explainability at the interface level: what the model was given, what it produced, and where the user can intervene. Trust is earned through legibility, not performance.
Good chatbot UX design starts by asking what the user is actually trying to accomplish — then removing every step between them and that outcome. Starter prompts, guided flows, and contextual suggestions reduce the blank-page problem. Streaming outputs, inline editing, and clear regeneration paths make the experience feel collaborative rather than transactional.
With a strong emphasis on legibility and control. Agentic systems make decisions that users didn't directly trigger — every autonomous action needs to be visible, reversible, and explained in plain language. We design the oversight layer as carefully as the action layer. Users should always know what the agent did, why, and how to undo it.
With a strong emphasis on legibility and control. Agentic systems make decisions that users didn't directly trigger — every autonomous action needs to be visible, reversible, and explained in plain language. We design the oversight layer as carefully as the action layer. Users should always know what the agent did, why, and how to undo it.
Mistaking capability for experience. A model that can do extraordinary things still needs an interface that makes those things findable, learnable, and trustworthy for someone encountering it fresh. The products winning in Gen AI right now aren't the ones with the strongest models. They're the ones where the design makes the model feel inevitable.
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