Graph Intelligence Platform
Build the knowledge layer your AI needs.
The Neo4j Graph Intelligence Platform connects your AI to real business context using graph, so every answer is accurate, explainable, and governable.
Trustworthy AI
Model relationships & ontologies to trace every decision & reduce token spend.
Contextual answers
Act on answers grounded in real business context through a graph.
Reliable scalability
Add your next use case without starting a new procurement cycle.
Seamless fit
Keep the stack you’ve already invested in, and skip the rip-and-replace.
Graph database
Answer business questions with deep insights from your data, no matter where it lives. Model it as a knowledge or context graph that gives your AI the insights it needs for more accurate results.
Graph analytics
Improve your decision-making and uncover deeper insights with powerful graph analytics. Run it on any cloud or data platform. No ETL required.
AI tools
Put your data to work with AI tools like GraphRAG, Aura Agents, and MCP—managing context to improve accuracy and enrich agent knowledge over time.
AI enablement
Give your AI models the context they need to reason correctly. Enablement is where the largest AI budgets are moving now. Knowledge graph foundations, combined with Aura Agent, our MCP Server, and Agent to Agent capabilities, give autonomous systems the context they need to work in production instead of stalling in pilot.
Retrieve answers grounded in accurate, real data instead of a best guess. GraphRAG gives your models the retrieval accuracy they need to reason with confidence.
Connect your AI agents to the knowledge they need to act. MCP Server gives agents the connectivity to reach enterprise data and tools directly.
Move from single answers to autonomous action. Aura Agent runs the workflows that let agents act on what they know, without a person in every step.
Give your agents context that persists. Agent Memory carries what an agent has learned into its next interaction, instead of starting over each time.
Let your agents coordinate rather than work in isolation. Agent-to-Agent gives multiple agents a way to do exactly that.
230% ROI.
Connected data. Smarter AI.
IDC validated $4M in annual value and a 7.8‑month payback for enterprises running Neo4j.
Fraud rings hide inside networks that entity link analysis can trace, and flat data cannot. With Neo4j as the knowledge layer for fraud detection, Gilead achieved a 1,000x improvement in identifying fraudulent activity, surfacing criminal networks that were previously invisible in their data.
Link to full story
Supply chains break in ways siloed ERP data can’t foresee—a shortage in one region cascades into shutdowns elsewhere. With Neo4j as the knowledge layer for supply chain, BASF mapped 1.5 billion nodes to turn weeks of value chain analysis into seconds, protecting margins through Europe’s 2022 energy crisis.
Link to full story
AI agents hallucinate when they reason over disconnected configs. A rule enforced in one system gets ignored in another. With Neo4j as the knowledge layer for AI systems, Uber built a Config Knowledge Graph spanning 8 million+ earners and 15,000+ cities, catching cross-domain conflicts before they reach drivers.
Link to full story
Security teams can’t patch what they can’t see. A single unmapped endpoint is all it takes for a vulnerability to go unnoticed for months. With Neo4j as the knowledge layer for security, Intuit maps 500,000+ endpoints to host names in milliseconds, reducing risk-scoring for every asset from hours to four minutes.
Link to full story
Rich customer data existed, but it couldn’t be connected in ways that improved targeting precision. Using Neo4j as the knowledge layer for their AI engine, What If Media Group cut advertising costs by 33 percent through relationship-based targeting that traditional data systems couldn’t reach.
Link to full story
Loved by devs. Deployed worldwide.
1,700+ organizations build on Neo4j for data breakthroughs. Build with a comprehensive platform for modeling, managing, and retrieving context.
