AI Quality Audit & Assurance
Looks good isn’t proven good.
We measure the proof gap: the distance between how good your AI looks and how good it provably is. The numbers are reproducible: you get the same verdict on every prompt and model change.
Maximum EU AI Act penalty (annual global turnover)
EU AI Act enforcement begins for high-risk systems
Typical time to implement credible traceability
Proof Gap Verdict
Four layers. Each one a failure mode.
Data Layer
Is the source material accurate, current, and complete? Gaps propagate directly into answers.
Vector Database
Correct indexing, measured recall, precision against ground truth. A wrong retrieval causes a wrong answer, invisibly.
Retriever
Relevance, precision, and latency at the retrieval step. The model can only answer what the retriever supplies.
Agent Behaviour
Tone, relevance, correctness, speed. Traced back to which layer caused each failure.
How We Work
Three ways to engage.
Reality Check
Start hereA self-serve scan that surfaces where your AI is confidently wrong. Run it yourself, same result every time.
- ✓Proof gap verdict: all four layers
- ✓Reproducible: re-run yourself, same result
- ✓Top-3 failure modes with layer attribution
Deep Audit
Expert-led audit of your full stack: data, vector store, retriever, and agent, with a reproducible KPI scorecard.
- ✓Full four-layer audit
- ✓Reproducible KPI scorecard
- ✓Quality gates for prompt and model changes
- ✓Cost and carbon analysis
Ongoing Assurance
A standing discipline. Quality gates block any prompt or model change that fails your standard.
- ✓Continuous quality gate enforcement
- ✓Prompt and model change gating
- ✓Monthly reproducible scorecard
- ✓Cost and carbon savings reporting