Shipping practical AI features without a research team

You do not need a research team to ship useful AI. You need a narrow problem, a measurable definition of a good answer, and a fallback for when the model is wrong.

We start by writing twenty real examples by hand — the inputs users actually send and the outputs we would be proud to return. That set becomes the evaluation harness, run on every prompt change, before any of it reaches production.

Latency and cost are product decisions. A summarisation feature that takes nine seconds is a different product from one that takes one. We cache aggressively, stream partial output, and pick the smallest model that clears the bar.

Finally, design for wrongness. Every model-backed surface in our products shows its source, allows a one-click correction, and logs disagreements. Those logs are the cheapest training data a small team will ever get.