Neo4j Live: From Language to Logic - Why Graph AI Succeeds Where LLMs Fail

In this Neo4j Live session, we explore why Graph AI succeeds where LLMs fail. You’ll learn how knowledge graph–powered systems deliver significantly higher accuracy by explicitly modeling relationships, enabling traceable reasoning, and grounding AI in structured intelligence. This is the difference between fluent language and reliable logic - critical for use cases like regulatory compliance, financial analysis, and operational decision-making. If you’re thinking beyond text generation and toward AI that can reason, connect, and scale with confidence, this session will show you the path forward. Guest: Matthias Buchhorn-Roth LinkedIn: https://www.linkedin.com/in/mbuchhorn/ Github: https://github.com/ma3u Overcoming LLM Deficits with Multi-Layered Ontologies: https://www.linkedin.com/pulse/overcoming-llm-deficits-multi-layered-ontologies-buchhorn-roth-meizf/ Building Graph-Aware Agents with Neo4j and Microsoft Agent Framework https://neo4j.com/labs/genai-ecosystem/ms-agent-framework/ NeoDash: https://neo4j.com/docs/neodash-commercial/current/ NeoDash on Aura: https://neo4j.com/docs/aura/dashboards/ 0:00 – Welcome & intro 3:00 – Live quiz: Where vector search falls short 10:25 – The legal challenge: Why LLMs struggle with law & hierarchy 16:30 – Vector search trap: Similarity ≠ authority 21:00 – Introducing symbolic Graph AI for legal reasoning 25:50 – Temporal dimension & state machines in legal workflows 32:10 – Architecture overview: Hybrid Graph + LLM approach 41:00 – From document management to knowledge management 49:50 – Legal AI in practice: Lessons learned & tooling 57:00 – Q&A: Hierarchies, compliance & implementation 1:06:30 – Wrap-up & upcoming Neo4j events #neo4j #graphdatabase #KnowledgeGraph #genai #llm