Press run. An automated harness fires probes at a demo AI assistant — prompt injection, hallucination, PII leakage, scope — and a judge scores each response live. This is the exact discipline I wire into your CI so a bad AI change stops at the gate, not in production.
Honest note: the target is a deliberately naive demo assistant, so its failures are real, reproducible model behavior — shown to demonstrate the method, not to test a real company. A real engagement points this same engine at your live feature, runs hundreds of probes, and wires the gate into your CI. A short audit maps your full failure surface — scoped and quoted after a quick conversation.
No fixture, no simulation. Give me a real question your AI answered and its reply — and, optionally, the source it should have relied on — and the same judge scores it against a grounding, hallucination, safety, and answer-quality rubric, right now.
How this works & what it isn’t: one strict LLM-as-judge pass scores your answer against a fixed four-part rubric — a real mini-eval, not a simulation. It’s a taste of the method, not a full audit: a real engagement runs hundreds of cases, adds deterministic checks, and wires the gate into your CI. Your input isn’t stored.