Graphs move from niche database to enterprise knowledge layer for AI systems
As generative AI matures beyond its early experimentation phase, enterprises are converging on a shared architecture for grounding large language models in trustworthy data: the enterprise knowledge layer.
Four years after the release of ChatGPT, most organizations have moved past haphazard experimentation and settled on a shared vocabulary and set of architectural patterns for production AI systems, according to Philip Rathle (pictured), chief technology officer of Neo4j Inc. That shift is placing the enterprise knowledge layer — the substrate where an organization’s ontology, data and agent memory live outside the model itself — at the center of enterprise AI conversations.
“Enterprise knowledge layer is the big topic,” Rathle said. “GraphRAG describes the pattern of having an LLM call out to a knowledge graph so that you externalize your knowledge in context. It doesn’t live in the model. It lives in a system of knowledge. And that gives you better accuracy, explainability and governance.”
Rathle spoke with theCUBE’s John Furrier at the Neo4j GraphTalk event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed the evolution of GraphRAG, new independent research on graph-based retrieval, and the anatomy of the enterprise knowledge layer. (* Disclosure below.)
