Delightful product design for AI companies

Yes. Most of the work goes into setting expectations, showing what the AI is doing, and handling answers that are wrong, slow, or uncertain, so people trust the output enough to act on it.

We start from the tasks people come to do rather than the feature list. Then we structure the product so the common path is quick and advanced options are easy to reach, including tables, filters, and permissions.

Yes, and it is often the change that helps most quickly. We look at where new users stall, remove what they do not need on day one, and design a clear path to the point where the product first becomes useful to them.

Yes, sized to what the project needs. That can be a review of your analytics and session recordings, interviews with a few users, or testing a prototype before anything is built. Testing early helps us find confusing flows before launch.

Yes. Product work covers web and mobile apps, including onboarding, empty states, and error states, which have a big effect on whether people keep using an app.

Yes, that is how most product work happens. We join your planning, answer questions as they come up, and review what gets built against the design, so small gaps get fixed before release instead of after.

Build-ready Figma files: organized pages, documented components, every state including the less obvious ones, and notes on behaviour, so your engineers can build with as few open questions as possible.

It depends on how much there is. The core flows of a focused MVP usually come together in the first month, and a full platform runs longer, which is why bigger products work best on a retainer that keeps pace with engineering.

Because products change while you build them. It is hard to know every screen before the first user test, so a fixed quote either needs a buffer or a new quote for each change. A retainer lets the design work follow your roadmap instead.