What we think
Design 4 min read

Designing AI people trust

Trust in an AI product is not won with a clever model. It is won with honesty about what the system knows, what it is doing, and when it is unsure.

Team collaborating around a screen

People do not trust AI because it is accurate. They trust it because it is legible — because they can see what it is doing and predict how it will behave. A slightly less capable system that is honest about its limits beats a brilliant one that surprises you.

Show the seams

Confidence signals, sources, and clear "I am not sure" states do more for adoption than another point of accuracy. Users calibrate quickly when the interface tells the truth.

Make undo effortless

When people know they can reverse an action, they explore. Cheap undo and clear confirmation for the irreversible turn a cautious user into a confident one.

Design for the moment the AI is wrong — because it will be — and you build something people keep using anyway.

Keep reading

AI Engineering

The agentic SaaS playbook

Agents are not a feature you bolt on — they are an architecture. Here is how we design SaaS products where the agent is a first-class citizen, not a demo.

Read more Quality

Evals before demos

A demo proves an AI feature can work once. An eval proves it works the thousandth time. We do not ship AI without one — and neither should you.

Read more Architecture

RAG that survives production

Retrieval-augmented generation is easy to prototype and hard to keep honest. The difference is in the retrieval, not the generation.

Read more

Let there be clarity.

Tell us where you are and where you want to be. We'll come back with a concrete plan — no jargon, no lock-in.