Because “it felt fine in testing” isn’t a release process for a probabilistic system.
AI output is probabilistic, so traditional unit tests alone can’t tell a team whether a production AI system is quietly improving or degrading with every prompt, model or RAG change. This platform turns that into a measurable, CI-gated signal instead of a gut feeling.
Full trace capture across prompts, agent steps and tool calls
Deterministic checks plus LLM-judge evaluation blended into one score
CI-integrated regression gates that automatically block a bad release
Experiment comparison across model, prompt and RAG versions
Cost, latency and safety metrics in a single operational dashboard
Designed and reviewed against all ten — see how I evaluate every architecture.
I design and ship systems like this one — from architecture through to production.
