The gap between a working prototype and a dependable product is wider than most teams expect, and it is where most AI value is won or lost.
Production is mostly the unglamorous parts
Error handling, retries, logging, access control, and graceful failure are what separate a demo from a system people rely on. They are rarely exciting and always decisive.
Plan for the bad day
What happens when the model is unavailable, slow, or wrong? Systems that answer that question before launch keep running when something inevitably breaks.
Ship, measure, improve
The first production version is a starting point. Instrument it, watch real usage, and improve on evidence rather than opinion. The prototype guessed. Production should know.
Budget for the unglamorous work, design for failure, and treat launch as the beginning of learning, not the end.
Working on something like this?
We are happy to share specific, relevant examples privately, no pitch.
