Neo4j

What Neo4j does

Neo4j Graph Intelligence Platform transforms data into knowledge, powering dynamic, personalized, and autonomous AI systems and intelligent applications so organizations can act faster with confidence. It offers fully-managed and self-managed graph database solutions, graph data science capabilities, and AI-specific tools like knowledge layers, AI systems, and GraphRAG. The platform is designed to store and query connected data at scale, uncover hidden patterns, and simplify application development with a native graph model and flexible schema. Neo4j supports diverse workloads, from analytical queries to transactional operations, and provides enterprise-grade security and high availability. It also offers tools for managing deployments, visualizing data, and building context-aware AI agents.

Products

• AuraDBFully-managed graph database to store and query connected data at scale, available on Azure, AWS, and Google Cloud. Offers flexible schema, Cypher queries, graph algorithms, and enterprise-grade security.
• Virtual GraphCreate and query a knowledge graph on existing data without ETL, allowing graph queries on existing datalakes and discovering insights quickly.
• Aura Graph AnalyticsRun graph algorithms on any data, any cloud, with 65+ pre-built algorithms for community detection, pathfinding, and similarity. Supports data from any source and integrates natively with AuraDB.
• Aura AgentBuild and deploy context-aware agents fast, grounded by a knowledge graph using low/no-code tools. Offers ontology-driven agent generation, single-click deployment, and GraphRAG for accuracy.
• Graph DatabaseSelf-managed graph database to store connected data with a graph database.
• Graph Data ScienceRun graph algorithms on connected data for deeper insights and machine learning models at scale.
• Enterprise StudioSecurely query, explore, and visualize data with built-in dashboards and AI-powered dashboard creation.
• Fleet ManagerManage all your Neo4j deployments from one place.
• Knowledge layerAI capability for organizing and managing knowledge for accurate retrieval and memory recall in AI systems.
• AI systemsAI capability for building production-grade AI systems by modeling and managing context.
• GraphRAGAI capability for building breakthrough GenAI apps with highly accurate responses, rich context, and deep explainability by unifying vector search, knowledge graph, and data science.
• Model Context Protocol (MCP)Protocol introduced by Anthropic to ease the integration of LLM Assistants with tools providing access to data, services, APIs and local systems. Neo4j offers integrations and servers for MCP.

Key capabilities

• Graph Database
• NoSQL Database
• Native Graph Technology
• Graph Platform
• Graph Analytics
• Cypher query language
• Knowledge Graph
• Graph visualization
• Graph Algorithms
• Fraud Detection
• Graph Technology
• GenAI capabilities
• Graph data science
• Graph Intelligence
• Context Graphs
• Context Engineering
• Flexible schema
• Index-Free Adjacency
• Analytical and Transactional Workloads
• 99.95% uptime SLA
• Automated upgrades, patches, and maintenance
• Enterprise-grade security
• Role-based access control
• Pay-as-you-go and prepaid consumption billing
• VPC isolation and dedicated infrastructure
• Hourly backups with 60-day retention
• Enhanced 24x7 support and services
• 65+ pre-built graph algorithms
• Work with any data source (e.g., Pandas DataFrames)
• Scale compute and storage separately
• Ontology-driven agent generation
• Single-click deployment for agents
• Robust vector + graph query retrieval tools for agents
• Chain of thought & multi-hop graph reasoning for agents

Use cases

• Customer experiences
• Data privacy, risk, & compliance
• Fraud detection & analytics
• Identify & access management
• Network & IT operations
• Real-time recommendations
• Supply chain management
• AI Systems
• Generative AI applications
• Digital twin creation
• Knowledge graphs for data integration and analysis
• Metadata management
• Pattern matching in data
• Expert domain knowledge & automation
• Improve enterprise search & knowledge assistants
• Scalable long-term memory for agents
• Drug discovery
• Regulatory compliance
• Case preparation in Legal
• Knowledge management in HR
• Portfolio analysis
• Cyber asset security
• Prescription coverage data turnaround
• Combating pharmaceutical fraud
• Safeguarding customer data
• Rebuilding grid as a digital twin
• Identity graph analysis
• Unlocking art and history collections
• Identity resolution
• Building knowledge graph of life on Earth
• Global value chain analysis
• Decoding disease biology
• Driving faster cash flow management
• Reducing fraud in financial services
• Streamlining complex medical supply chain analysis
• Inventory management
• Identifying rare childhood diseases
• Rapid development of IT architecture visibility solutions
• Natural language processing for equipment repair
• Making companies and economic connections transparent
• Real-time graph analysis of documents
• Keeping remote workers safe and productive
• COVID-19 contact tracing
• Building graph-native construction learning experiences
• Creating personalized, smart homes
• Building a digital twin of railroad
• Detailed visibility into complex workflows
• Building GenAI analytics platform
• Tracking infections in healthcare
• Identifying new uses for existing drugs
• Tracking genetic relationships in crops

AI approach

Neo4j provides a Graph Intelligence Platform that transforms data into knowledge, powering AI systems and intelligent applications. They focus on knowledge graphs, GraphRAG, and Model Context Protocol (MCP) to ground AI, improve accuracy, and explainability. They offer tools for building and deploying context-aware AI agents.

Tech named: Knowledge graphs, GraphRAG, Model Context Protocol (MCP), Vector search, LLM summarization, Langchain, Streamlit, Next.js, Google Gemini 2.5 Flash, Google VertexAI, Microsoft Azure OpenAI, gemini-embedding-001, text-embedding-005, text-multi-lingual-embedding-002, OpenAI text-embedding-3-small/large/ada-002, Cypher

Industries served

• Financial services
• Healthcare & life sciences
• Retail
• Telecommunications
• US federal government
• Pharmaceutical companies
• Chemical manufacturers
• Agriculture companies
• Biotech startups
• Healthcare providers
• Ad tech agencies
• Museums
• Manufacturing
• Logistics
• Utilities
• Legal
• HR

What it says sets it apart

• Transforms data into knowledge for AI systems
• Powers dynamic, personalized, and autonomous AI systems
• Offers both fully-managed (AuraDB) and self-managed graph database options
• Provides Virtual Graph for querying existing data without ETL
• Aura Graph Analytics with 65+ pre-built algorithms for any data, any cloud
• Aura Agent for rapid building and deployment of context-aware AI agents
• Native graph model and flexible schema for simplified development
• Cypher query language for uncovering hidden patterns
• Index-Free Adjacency for lightning-fast traversals
• Supports diverse analytical and transactional workloads
• High availability with 99.95% uptime SLA for AuraDB Business Critical
• Comprehensive security features including role-based access control
• Integration with major cloud providers (AWS, Google Cloud, Microsoft Azure)
• Strong focus on AI capabilities including Knowledge layer, AI systems, and GraphRAG
• Model Context Protocol (MCP) integrations for LLM assistants
• Extensive developer resources, including GraphAcademy for free training and certifications
• Preserves valuable context and relationships in data, unlike traditional databases
• AI-powered dashboard creation and GenAI Copilot in Enterprise Studio
• Ability to build expert agents grounded in enterprise knowledge graphs for critical applications
• Offers multi-agent system integration with AI clients like Claude, Microsoft Copilot, ChatGPT
• Proven track record with 1,700+ global organizations, including Fortune 100 and Fortune 500 companies