Verify every LLM claim before it reaches your customer

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.

Structured Verification Pipeline

Sits between LLM generation and response delivery. Four stages, fully auditable.

01

Claim Extraction

Extract individual verifiable claims from LLM responses. Each claim is checked independently — no hiding violations behind aggregate scores.

02

Policy Verification

Check each claim against structured domain rules. Rules are authored by compliance experts as configuration — no engineering sprints to update policies.

03

Knowledge Graph

Build and query structured knowledge graphs from your domain documentation. Purpose-built embeddings provide mathematically grounded semantic search.

04

Audit Trail

Every verification produces a complete decision record: claims extracted, rules matched, scores computed. Audit-native, not a logging afterthought.

Wins All Three Benchmarks

Knowly's structured verification beats LLM-as-judge across standard NLP verification benchmarks, with reproducibility and full auditability.

Dataset Published SOTA LLM-as-Judge Knowly vs LLM-Judge
FEVER 80.2% 77.3% 86.7% +9.4pp
ContractNLI ~87.5% 93.1% 94.0% +0.9pp
FactCC 72.9% 91.7% 92.1% +0.4pp

F1 score — balances catching correct claims with avoiding false ones.

Both pipelines use the same Qwen2 7B model locally. Read the full analysis →

Ready to verify your AI outputs?

Talk to us about compliance verification for your regulated AI deployment.