Neo4j blog

Introducing Neo4j GraphAware Financial Crime Intelligence

Move from fragmented data to connected intelligence, for better detection, deeper investigations, and defensible decisions.

Vector RAG vs. GraphRAG: Which retrieval do you need?

Scaling Karpathy’s LLM wiki: Why your knowledge base needs a graph

Independent study: GraphRAG makes AI agents 80% more truthful

Featured voices

Emil Eifrem

CEO & Founder, Neo4j

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Sudhir Hasbe

Chief Product Officer, Neo4j

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Philip Rathle

Chief Technology Officer, Neo4j

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Most recent

Expensive AI models may sound smart, but without context, they don’t actually understand your business. It’s Ferris Bueller’s Ferrari with nowhere to drive. All engine. No roads.

The context gap: Why your smart-sounding AI struggles to reason

From data to intelligence: Why every enterprise needs an AI knowledge layer

Using agents to secure satellites’ supply chain systems

Using agents to secure satellites’ supply chain systems

Why machines need embeddings: Turning graph structure into features

Building an AI agent with memory: Microsoft Agent Framework + Neo4j

Developer

Building an AI agent with memory: Microsoft Agent Framework + Neo4j

Finding hidden bottlenecks in flight networks with Aura graph analytics on Databricks

Find impactful graph-powered insights in Databricks

Graph database

A complete guide to production-grade PrivateLink setup for Neo4j Enterprise Edition

The efficiency of on-demand graph analytics.

The persistence tax: Why it’s time to let your graph analytics sleep

Graph database

Capgemini turns data and AI into connected intelligence with Neo4j and Databricks

iscover the best tool for context engineering and build agentic AI with MCP, knowledge graphs, GraphRAG, and practical evaluation and tracing.

Agentic AI

Best tools for context engineering in agentic AI systems

Why healthcare CIOs can’t afford to scale AI without a knowledge graph foundation