Govern agentic AI deterministically, not probabilistically

RAG solved retrieval. Guardrails solved safety. What's missing is a governed harness — one that mathematically enforces your domain's policies and produces auditable evidence trails that hold up under regulatory scrutiny.

Why Governance 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 governed harness.

A Structured Governance 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

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

Dataset Published SOTA LLM-as-Judge Knowlytix 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 deploy agentic AI you can govern?

Talk to us about a governed harness for your regulated AI deployment.