LLMs are powerful, but retrieval alone only answers what. In real projects, stakeholders need to know why things happened, who was responsible, and how entities connect across years of scattered documents.

This session shares a real estate case study where 15+ years of contracts, PDFs, and project files were transformed into a knowledge graph. By combining vector retrieval with Neo4j’s reasoning, we moved beyond semantic matches to multi-hop logic and explainable insights.

Learn how to build a reasoning layer for your RAG stack that grounds answers, reduces hallucinations, and delivers business intelligence your stakeholders can trust.