Neo4j
Native Graph Database for Connected Data
The leading graph database for knowledge graphs, relationship-rich data, and AI memory systems. Index-free adjacency for constant-time traversals.
Core Capabilities
Native Graph Storage
- •Nodes, relationships, properties
- •Index-free adjacency
- •Constant-time traversals
- •Multi-hop path queries
- •No JOIN operations needed
Cypher Query Language
- •Intuitive pattern matching
- •Path expressions
- •Aggregations & projections
- •APOC procedures library
- •400+ utility functions
Graph Data Science
- •In-database ML algorithms
- •Centrality measures
- •Community detection
- •Node embeddings
- •Link prediction
AI & GraphRAG
- •Knowledge graph retrieval
- •Entity relationship context
- •LLM augmentation
- •Graph embeddings for RAG
- •Semantic enrichment
Enterprise Features
- •ACID transactions
- •High availability clustering
- •Read replica scaling
- •Role-based access control
- •Encryption at rest
Ecosystem
- •Python, Java, JS drivers
- •Kafka & Spark connectors
- •Bloom visualization
- •Full-text indexing
- •ETL tooling
Why We Deploy Neo4j
Relationship-First Design
Unlike relational databases, Neo4j stores relationships as first-class citizens. No expensive JOINs — just follow the connections.
Self-Hosted Control
Community Edition is open-source (GPLv3). Run on your own infrastructure with complete data sovereignty for sensitive knowledge graphs.
AI Memory Systems
Perfect for AI agent memory, storing entities, observations, and relationships that persist across conversations and sessions.
Performance at Scale
Predictable latency on billion-scale graphs. Index-free adjacency means query time depends on data touched, not total size.
Common Use Cases
Neo4j excels where relationships between data matter most.
Ready for Graph-Powered Intelligence?
We can help you design, deploy, and integrate Neo4j for knowledge graphs and AI memory systems.