The Missing Verification Layer for Regulated AI
RAG solved retrieval. Guardrails solved safety. What's missing is verification — proving that LLM outputs comply with your domain's rules, with auditable evidence trails that hold up under regulatory scrutiny.
Why Verification Matters
A banking chatbot retrieves the right fee refund policy via RAG. The LLM reads it and responds: "We'll process your full refund immediately." The retrieval was correct. The response is wrong — the policy requires manager approval above $25. Better retrieval can't fix this. You need a verification layer.
How It Works
A structured verification pipeline that sits between LLM generation and response delivery.
Claim Extraction
Extract individual verifiable claims from LLM responses. Each claim is checked independently — no hiding violations behind aggregate scores.
Policy Verification
Check each claim against structured domain rules. Rules are authored by compliance experts as configuration — no engineering sprints to update policies.
Knowledge Graph
Build and query structured knowledge graphs from your domain documentation. Geometric embeddings provide mathematically grounded semantic search.
Audit Trail
Every verification produces a complete decision record: claims extracted, rules matched, scores computed. Audit-native, not a logging afterthought.
Benchmark Results
Knowly's structured verification matches or beats LLM-as-judge across standard NLP benchmarks, while providing reproducibility and auditability.
| Dataset | LLM-as-Judge | Knowly | Delta |
|---|---|---|---|
| FEVER | 77.3% | 86.2% | +8.9pp |
| ContractNLI | 93.1% | 94.0% | +0.9pp |
| FactCC | 91.7% | 90.5% | -1.2pp |
Both pipelines use the same Qwen2 7B model. F1 metric shown. Read the full analysis
Ready to verify your AI outputs?
Talk to us about compliance verification for your regulated AI deployment.