Neo4J Graph Overview

I recorded this video to explain how a graph‑based governance layer can organize an organization’s knowledge and feed it into large language model prompts, addressing Linus’s request for a concrete graph solution. I define a graph as a collection of entities and their real relationships—not a chart or a vector store—and show how it captures connections, meaning, and precedent across multiple data sources. I demonstrate a Neo4j (or similar) graph for a bank, illustrating members, accounts, transactions, and disputes, then walk through how a Groovy policy calls the Graph Context API to enrich prompts at runtime, providing consistent grounding and an AI firewall. Finally, I emphasize that the approach is reusable across domains, lets agents discover context without blowing token windows, and can be extended as needed.