Building AI features your users can actually trust
Demos are easy. Production AI needs evaluations, guardrails and honest UX. Here is how we approach it.
Most MVPs fail for one of two reasons: they take too long, or they are built so quickly that they collapse the moment real users arrive. Over the last seven years we have refined a 12-week process that avoids both.
Weeks 1–2: Discovery that ends in decisions
We start with short, focused workshops. The goal is not a 60-page document — it is a shared understanding of who the users are, what problem we are solving first, and what we are deliberately not building yet.
- One core user journey, written down and agreed
- A ranked feature list with a clear cut-off line
- Technical risks identified and a plan to test them early
Weeks 3–4: Design you can click
Before we write production code, you get a clickable prototype. We put it in front of five real users. It is the cheapest moment to change your mind.
Weeks 5–10: Build in public
Two-week sprints, a demo every week and a staging link from day one. You see progress constantly, so there are no surprises at the end.
The best predictor of a smooth launch is how often the client has used the product before it.
Weeks 11–12: Harden and launch
Load tests, security review, analytics and monitoring. Then a calm, rehearsed release. An MVP should be minimal in scope — never in quality.