Neo4j Graph Intelligence Platform (AuraDB)

Neo4j traces its origins to 2000, when its founders hit performance limits of relational databases; the Swedish company behind the product was formed in 2007 and moved its headquarters to Silicon Valley in 2011. It develops a native graph database and graph analytics platform that stores entities and their relationships directly, so connected-data questions such as fraud rings, supply networks or customer journeys can be answered quickly.

The platform includes the fully managed AuraDB, Aura Graph Analytics and Aura Agent services, plus self-managed Graph Database, Graph Data Science and Enterprise Studio. It now positions graphs as a knowledge layer for AI, with GraphRAG, vector search, MCP support and agent memory, and in 2026 added GraphAware Hume and Financial Crime Intelligence after acquiring GraphAware. Customers named include Uber, Cisco, Walmart, Santander, BNP Paribas, UBS, Airbus, BMW, Intuit and Transport for London.

• Native graph database: Stores nodes and relationships natively for fast multi-hop queries on connected data.
• AuraDB managed service: Fully managed graph database on AWS, Azure and Google Cloud with a free tier.
• Graph Data Science: Graph algorithms and analytics run on the graph, managed as Aura Graph Analytics or self-managed.
• GraphRAG and agent memory: Knowledge graph, vector search and MCP tools to ground LLMs and agents.
• Financial Crime Intelligence: Signal, alert, investigate and decide workflow for banks and insurers.
• Enterprise controls: Encryption, governance, RBAC and SSO across cloud and self-managed deployments.

A Vietnamese bank can map accounts, devices and transfers in a graph to uncover mule account rings and investigate alerts faster. A retailer can build a product and customer knowledge graph to power recommendations and an AI shopping assistant. A manufacturer can model its supplier network to trace the impact of a disrupted component across products.

Relationship-heavy queries that are slow in relational systems run in milliseconds, which supports fraud detection, recommendations and network analysis. Grounding AI agents in a knowledge graph improves accuracy, with the site citing 44 percent fewer hallucinations and an IDC-validated 230 percent ROI with a 7.8-month payback. Case results include a 20 percent fraud reduction at BNP Paribas Personal Finance across 800,000 applications a year.

Enterprises adopting generative AI need explainable, accurate answers grounded in their own data, which drives interest in knowledge graphs and GraphRAG. At the same time, fast-growing digital payments and e-wallets in Southeast Asia increase pressure on banks and fintechs to detect fraud networks and money mule rings that only appear in connected data.

The company behind Neo4j was formed in Sweden in 2007 and moved its headquarters to Silicon Valley in 2011. It raised a 390 million USD Series F in 2021 led by Eurazeo with GV and Inovia, passed 200 million USD in annual recurring revenue in 2024, counts 84 of the Fortune 100 as customers and acquired GraphAware in 2026.