The distance between a demo and a dependable AI product.

A demo proves an interaction

A compelling prototype shows what might be possible. It rarely proves that the experience holds up across messy data, ambiguous questions, unavailable services, or different permissions.

Make quality observable

Keep a versioned evaluation set. Track changes to prompts, retrieval, and model configurations against the same examples. Combine offline evaluation with operational feedback so regressions can be found and explained.

Build for the uncomfortable cases

Validate inputs, set timeouts, bound costs, and handle partial failure. Treat model outputs as untrusted data before passing them to tools. Require explicit approval for consequential actions.

Launch is a controlled transition

Agree rollout criteria, monitoring, ownership, and rollback. Start with limited exposure and review the errors people actually encounter. A useful production system is an ongoing operating responsibility.