Two weeks, real data: how we de-risk every AI project

Ask most companies how an AI project starts and you'll hear the same story: months of requirements gathering, a steering committee, a vendor shootout, and a year later — a pilot that doesn't survive contact with reality.

We do the opposite. In two weeks, on your real data, we build a working prototype. Then you decide.

Why prototypes beat requirement documents

Requirements documents are written in the dark. Nobody knows, before building, which of the "must-haves" actually matter — and which supposed edge case will turn out to be the whole job. A prototype on real data answers the questions that matter:

• Does this actually work on our data? Not the vendor's clean samples.
• Where does it break? You find the 80% cases and the painful 20%.
• What's it worth? Once you see it working, ROI stops being theoretical.

Two weeks is enough time to know whether an idea has legs. If it does, we keep building. If it doesn't, you've spent two weeks — not a year — learning that, and you know exactly what to adjust.

The de-risk method, in four steps

• Pick one painful workflow — the one where admin hurts most.
• Bring real data — yours, messy and all.
• Build the thinnest working thing — one agent, one job, done well.
• Measure and decide — working? scale it. broken? fix or stop.

What you get at the end of two weeks

Not a slide deck. A demo you can touch, run on your own data, with your own people in the loop — and a clear-eyed read on whether to invest further.

Prototype-first fixes both. It's how we build agentic systems that survive the deck — and how you decide with evidence, not PowerPoint.