Open Knowledge Format Cuts AI Hallucination Rate from 18% to 3% in Enterprise RAG

A software team behind Mattrx, a multi-tenant marketing-analytics SaaS platform, replaced conventional document chunking in their AI pipeline with a structured approach called Open Knowledge Format (OKF). Unlike standard retrieval-augmented generation, which splits documents into context-free text fragments, OKF organizes information into typed, governed knowledge units carrying metadata such as ownership, expiry dates, and explicit relationships. After rebuilding their knowledge base into roughly 11,000 OKF units, the team reported the assistant's hallucination rate dropped from 18% to 3% and stale-answer rate fell from 11% to 1.5%. Context tokens consumed per query also shrank from 14,000 to 3,500, as the system could retrieve precisely relevant units rather than broad text chunks. The approach combines vector search with graph-based retrieval, enabling the AI to answer multi-hop questions that previously went unanswerable and to enforce business rules as structured data rather than buried prose.