A promising prototype with no safe path to ship. We gave it one.
We took the feature to production in six weeks — with the evaluation harness, guardrails, and monitoring that turn a demo into a dependable product. It launched on schedule, cleared its quality bar, and drove real adoption instead of sitting in a backlog.
The challenge
A SaaS team had built an impressive AI prototype but couldn't ship it with confidence. There was no way to measure whether it was good enough, no guardrails against bad outputs, and no monitoring once it hit real users. The gap between “cool demo” and “production feature” was the entire problem — and it's where most AI features stall.
What we built
An end-to-end path to production: model selection and prompt architecture, a retrieval layer over the product's own data, an evaluation harness that scores quality on every change, guardrails for safety and edge cases, and monitoring plus analytics for life after launch. The feature shipped into the existing product held to the same reliability standards as everything around it.
How it works
- Model and architecture chosen against the actual use case — not the hype cycle.
- Retrieval grounds every response in the product's real data.
- An eval harness gates each change, so no quality regression ever ships.
- Guardrails and monitoring keep it safe and observable in production.
The stack
▪ Key takeaways
- Evals are the unlock — you can't ship what you can't measure.
- Guardrails and monitoring are features, not afterthoughts.
- Grounding in real product data beats clever prompting alone.
- “To production” means owned, observable, and improvable — not just live.