Most AI features die in the gap between a working notebook and a reliable product. Closing that gap is a discipline, not a sprint.
Our checklist starts with a frozen eval set and a target metric, then adds observability, guardrails, fallbacks and a rollback plan before a single user sees it.
We stage rollouts behind flags, watch real traffic, and only widen access when the numbers hold. Every step has an owner and an exit criterion.
The result is boring in the best way: features that ship on schedule and behave in production the way they did in the demo.