Semantic Coherence: Neo4j – Signal Evidence & AI Readability

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“Agentic AI without a knowledge graph is like a self-driving car with no GPS map. Kind of brilliant, but dangerously unaware.”
Natalie Romanov, Associate Director, Knowledge Management & Data Strategy, Merck Group

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“Graph databases are a natural way to express interconnected data. The simplicity extends from humans to LLMs — making our configurations more accessible and explainable.”
Vignesh Murugesan, Senior Staff Engineer, Uber

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“Our collaboration with Neo4j helped us develop a successful fraud detection model that met our expectations. It’s a win-win partnership.”
Mehdi Barchouchi, Head of Innovation Data & Tools, Risk Division

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“With Neo4j we can map possible exposures in seconds — something that previously took engineers hours and days to manually figure out.”
Chad Cloes, Senior Staff Software Engineer

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“Congestion costs London £6 billion  a year. We can make a big dent in that through managing this operation in real-time with Neo4j.”
Andy Emmonds, Chief Transport Analyst

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“Deploying on Neo4j AuraDB allows us to focus our efforts on optimizing our models, knowing that the management of our graph database is under control.”
Eric Wespi, Data Scientist

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[H2] Works Seamlessly With Your Tech Stack
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[H1] Customer Success Stories
1,700+
global organizations rely on Neo4j
84
of the Fortune 100 are Neo4j customers
58%
of the Fortune 500 power their business with Neo4j
[H2] Featured Stories

[H3] JupiterOne Accelerates Cyber Asset Security with Neo4j Graph Intelligence Platform

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[H3] Infinitus Silences the Hold Music: How Graph-Powered AI Cut Prescription Coverage Data Turnaround from Days to Seconds

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[H3] Gilead Sciences Combats $431 billion Pharmaceutical Fraud Threat with Neo4j Graph Analytics

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[H3] Intuit Safeguards the Data of 100 Million Customers with Neo4j

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[H3] Enel Rebuilt Its Grid as a Digital Twin — Now It Sees, Plans, and Acts in Real Time with Neo4j

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[H3] Arhasi Delivers Trusted, Defensible AI and a 370% ROI with Neo4j and Google Cloud

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[H2] Enabling Global Companies of All Sizes

Search

[H3] Identity Graph Analysis at Scale Provides Ad Tech Agency Customers with Greater ROI

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[H3] Amsterdam’s Rijksmuseum Unlocks a Global Gateway to Art and History with Neo4j

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[H3] Arhasi Delivers Trusted, Defensible AI and a 370% ROI with Neo4j and Google Cloud

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[H3] Building the Largest Knowledge Graph of Life on Earth

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[H3] BASF SE Transforms Global Value Chain Analysis with Neo4j

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[H3] BenchSci Decodes Disease Biology with Neo4j to Accelerate Drug Discovery

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[H3] Driving Faster Cash Flow Management with Neo4j

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[H3] BNP Paribas Personal Finance Reduces Fraud by 20% with Neo4j

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[H3] Graph Data Science Streamlines Complex Medical Supply Chain Analysis

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[H3] BT Group Keeps Customers Connected with Lightning-Fast Inventory Management

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[H3] Care-for-Rare Identifies Rare Childhood Diseases with Neo4j

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[H3] Graph Technology Fuels Rapid Development of IT Architecture Visibility Solution

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[H3] Neo4j Provides Natural Language Processing at Scale, Making Equipment Repair More Efficient

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[H3] Companies and Their Economic Connections Are More Transparent Thanks to Big Data Intelligence

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[H3] Real-Time Graph Analysis of Documents Saves Company Over 4 Million Employee Hours

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[H3] Neo4j Keeps Millions of Remote Workers on Citrix Safe, Productive, and Secure

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[H3] COVID-19 Contact Tracing with Neo4j

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[H3] Graph Technology Helps Xfinity Create Personalized, Smart Homes

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[H3] Building a Digital Twin of the Largest Railroad in the Eastern U.S. with Neo4j

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[H3] Detailed Visibility into Complex Workflows

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[H3] Data² Builds Leading GenAI Analytics Platform with Neo4j

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[H3] Cloud-Based Graph Technology Drives Efficiencies

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[H3] E-Health Company Creates a Knowledge Graph Solution to Help Patients with Chronic Pain

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[H3] DUCK Transforms Customer Analysis with Neo4j and LLM-Powered Netnography Platform

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AuraDB Product Brief
Overview of AuraDB’s graph database features, benefits, and practical use
cases.
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Neo4j Trust Center
Learn about our security posture and request compliance documentation.
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AuraDB Datasheet
Snapshot of AuraDB's graph database features, benefits, and key use cases.
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Aura Documentation
Guides for data modeling, data management, querying, security, drivers,
visualization, and integration.
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Fundamentals of Property Graphs
Learn the basics and how to evolve your graphs.
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Fundamentals of Writing Cypher
Learn to write our query graph language.
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Graph Data Modeling Fundamentals
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Intro to Neo4j Graph Data Science
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What's New In AuraDB
Explore new AuraDB enhancements for easier enterprise graph deployment.

Securely Access AWS in a Private Cloud
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Combine Structured and Semantic Search
Learn how to use them together to create better context.

What is a Knowledge Graph?
Learn to organize and integrate complex data for effective analysis and decision-making.

Assess the State of Your Neo4j App
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ROI with Property Graphs
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Use Knowledge Graphs to Implement RAG
Enhance the accuracy and relevance of AI-generated responses.

Training Series:Graph Database Intro
Learn about core concepts, such as nodes, relationships, and properties.

Training Series:Enhance Geospatial Analytics With Graphs
Work with Apache Sedona, Spatial SQL, and GeoPandas for various data types.

Going Meta:Migrate From Triple Store to Property Graph
See step by step how to make the migration easy.

Road to NODES:How to Graph Relational Database Models
Learn how to get your app up and running.

Neo4j Live:Querying and Data Importing
Learn about upgraded graph visualization, quick search capabilities, and other new features.

Neo4j:AuraDB Overview and Best Practices
Build smarter apps using connected data and graphs without JOINs.

Neo4j: Build Powerful Fraud Detection Apps
Uncover difficult-to-detect hidden patterns in your data to fight fraud.

Neo4j: Optimize Supply Chain With Knowledge Graphs
Improve visibility, increase agility, and build a more resilient network.

Neo4j: Trends in Graph and Analytics
Learn to handle the rise of GenAI, unstructured, and semi-structured data with knowledge graphs.

[H2] AuraDB Frequently Asked Questions

How does AuraDB differ from traditional relational databases?
AuraDB is a purpose-built, high-performance native graph database engine that stores and navigates
relationships between data elements directly. Relational databases, use JOINs to access related data. In
contrast, AuraDB handles relationships natively using pointers, eliminating the associated performance costs.
Learn more
AuraDB also supports ACID transactional properties, ensuring that all data changes are consistent and
reliable. This guarantees data integrity even in multi-user, highly concurrent environments.
This robust architecture enables Neo4j customers to build reliable transactional and analytical apps with
confidence in their data’s consistency and integrity. Learn more about Neo4j AuraDB's database internals.

What programming languages and drivers does AuraDB support?
AuraDB empowers developers with high query performance, a flexible data model, and broad language support,
making migration easy. AuraDB supports a wide range of programming languages, with official drivers for .NET,
Java, JavaScript, Go, and Python.
Additionally, community contributors provide drivers for many other languages, including PHP, Ruby, R,
Erlang, Clojure, C/C++, and more. Learn more about available drivers.

What security and regulatory certification standards does
Neo4j Aura meet?
Aura complies with key security and regulatory standards, including CCPA, ISO 27001, ISO 20243, SOC2 Type II,
SOC3, HIPAA and GDPR.
Visit Neo4j's Trust Center at https://trust.neo4j.com/ for comprehensive security and compliance
information.

What migration and interoperability tools does Neo4j provide?
Neo4j supports various approaches for migration and interoperability. It offers standard APIs like GraphQL,
which work across different database models. Neo4j also supports the ISO standard graph query language GQL,
with mature implementations like Cypher and OpenCypher, and provides a plugin for Apache Gremlin.
Neo4j includes Sql2Cypher, a tool that converts SQL to Cypher, often simplifying and improving query
readability for those migrating from SQL. Additionally, Neo4j provides SQL access through BI connectors.

How does Neo4j help developers optimize query performance?
Neo4j’s “EXPLAIN and PROFILE” options in the Cypher query language help architects and developers identify
where indexes can improve query performance. Neo4j doesn’t need JOINs to access related/connected data. This
means Neo4j doesn’t need JOIN indexes, materialized views, or other components that relational databases use
to work around the fact that they don’t store relationships directly.
In short, Neo4j uses indexes as a way to find the Node(s) (entities or discrete objects of a domain) that are
the starting point for a graph query. It doesn’t need additional indexes, materialized views, or other
components to speed up queries of related data.

What data integration and quality tools are available in
Neo4j AuraDB?
AuraDB has multiple data integration and data quality tools built into the database, including:
Cypher query language and Graph Data Science algorithm for data transformation,
aggregation, preparation, and feature generation.
Python client for direct integration with Pandas data frames, enabling data scientists to
leverage Python libraries for data preparation and transformation.
APOC library with stored procedures for data aggregation, formatting, cleaning, and
engineering.
Connectors (Spark, Kafka, Data Warehouse) for data movement and transformation.
Data Importer for no-code graph data modeling and loading.

[H1] Build Smarter AI Systems With Context
Unify your data with a knowledge graph to ground AI systems and agents. Manage context to boost accuracy and explainability. Evolve and enrich agent knowledge over time.
Learn More

Overview
Agentic AI
Capabilities
Use Cases
Customers
Resources
[H2] Boost Accuracy
Organize knowledge for accurate retrieval and memory recall.
[H2] Contextual, Smarter AI
Unify data for multi-step retrieval to deliver complete answers
[H2] Increase Explainability
Optimally model data for AI reasoning and explanation 
of its steps
[H2] Future-Proof Your AI
Continuously enrich context as requirements & data evolve.

[H2] Demo: How to Manage Context to Ground Agentic AI
Watch this five-minute demo about how to feed LLMs the right context for smarter AI.
Watch Demo
Capabilities
[H2] Optimize Context Engineering to Build Smarter Agents

Boost Accuracy & Explainability With Agentic GraphRAG

Pair RAG with a knowledge graph. Then use AI agents to retrieve information from the knowledge graph by traversing its interconnected data for richer contextual insights.

Combine Both Structured & Unstructured Data

Add new datasets with ease due to a flexible schema. Feed your LLM new context to keep agents intelligent as users interact, requirements change, and AI evolves.

Manage Context for Smart MCP & AI

Model your data with a knowledge graph that keeps relationships with their valuable context intact. Improve AI’s ability to understand, reason, and reliably execute tasks.

Query Faster for Agent Memory & Tools

Boost query performance with index-free adjacency for memory and tool calling. Scale seamlessly and query 1,000x faster than a traditional database.

Enrich & Evolve AI Agent Knowledge

1. Get real-time data updates.2. Gain deeper insights from graph analytics.3. Connect and update agent memory across sessions.

[H2] Use Cases: Build Smarter AI With Graph Technology
[H3] Expert Domain Knowledge & Automation
Query massive, specialized knowledge graphs using natural language. Combine vector search, automated query generation, and LLM summarization to help with domain-specific tasks and research.
Build Expert AI Systems
[H3] Semantic Knowledge Graph Layer for Deep Context
Give AI deep context across your business domains—an understanding of what data means, how it connects,  and when to use it.
Improve AI Reasoning
[H3] Improve Enterprise Search & Knowledge Assistants
Unify knowledge across data sources for applications in SaaS, Legal & Compliance, Network & Security, and more. Build agents to dynamically reason across all your enterprise knowledge.
Learn More
[H3] Scalable Long-Term Memory 
for Agents
Optimize memory management that grows intelligently over time for immediate decision-making as well as storing and recalling information from across different sessions.
Improve Long-Term Agent Memory
Analyst Report
[H2] Complimentary Gartner Report on Knowledge Graphs for AI
Gartner, How to Build Knowledge Graphs That Enable AI-Driven Enterprise Applications, By Afraz Jaffri, 22 May 2025GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.
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[H2] 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.
[H2] Real World AI Innovations. Powered by Graph.

“Analysts need to be able to dissect exactly how the AI reached a particular conclusion or recommendation. Neo4j enables us to enforce robust information security by applying access controls at the subgraph level.”
Eric Costantini, Chief Business Officer
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“Agentic AI without a knowledge graph is like a self-driving car with no GPS map. Kind of brilliant, but dangerously unaware.”
Natalie Romanov, Associate Director, Knowledge Management & Data Strategy, Merck Group
Link to story

“Graph databases are a natural way to express interconnected data. The simplicity extends from humans to LLMs — making our configurations more accessible and explainable.”
Vignesh Murugesan, Senior Staff Engineer, Uber
Link to story

“Sales teams, collections specialists—they can all query the graph directly through natural language. What once took days of data team effort now happens in minutes.”
Jin Foo, Head of Data & Analytics, Prospa
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[H2] Works Seamlessly With Your Tech Stack
Neo4j integrates with leading technology providers

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[H2] Explore AI Resources
Tools and Guides for Easily Building AI

GraphAcademy

Blog

Videos

Events & Webinars

Technical Resources

[H3] Neo4j & GenerativeAI Fundamentals
Learn the basics of Neo4j and the property graph model.
4 hours

[H3] Importing Data Fundamentals
Learn how to import data into Neo4j and create a graph data model.
2 hours

[H3] Build a Neo4j-Backed Chatbot Using Python
Build a chatbot using Neo4j, Langchain and Streamlit.
2 hours

[H3] Build a Neo4j-Backed Chatbot with Typescript
Build a chatbot using Neo4j, Langchain and Next.js.
6 hours

[H3] Introduction to Vector Indexes and Unstructured Data
Understand and search unstructured data using vector indexes
2 hours

[H3] Using Neo4j With Langchain
Learn how to integrate Neo4j in into Langchain applications for GenAI.
2 hours

[H3] Building Knowledge Graphs with LLMs
Learn how to use Gen AI, LLMs, and Python to turn unstructured data into graphs.
2 hours

[H3] What is GraphRAG?
Discover how GraphRAG combines knowledge graphs to deliver accurate, reliable, and context-rich AI answers.

[H3] GraphRAG Python Package: GenAI With Knowledge Graphs
Turn unstructured data into knowledge graphs to enhance GenAI retrieval

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[H3] The GraphRAG Manifesto: Add Knowledge to GenAI
Boost AI accuracy and explainability by providing context to AI with GraphRAG.

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[H3] Get Started With GraphRAG: Neo4j’s Ecosystem Tools
Develop GenAI applications grounded by knowledge graphs

[H3] Build GraphRAG Apps for Accurate AI on Google Cloud
Learn about the native integrations with Google Cloud and Vertex AI.

[H3] Implement a RAG App With a Knowledge Graph for Langchain
Learn how to improve information retrieval.

[IMG: Learn how LLMs and knowledge graphs work together.]
[H3] Unify LLMs & Knowledge Graphs for AI Accuracy
Learn how to supply context to a LLM for accurate, relevant, and explainable results.

[H3] Implementing RAG: Write a Graph Retrieval Query in LangChain
Learn how to write a retrieval query that supplements or grounds an LLM’s answer

[H3] How an AI Agent Works
Discover how agents use tools, memory, and reasoning to complete complex tasks.

[H3] Expand Your MCP Toolbox
Combine vector search with the Cypher query language to add MCP as another tool for agents.

[H3] Evaluate Graph Retrieval in MCP Agentic Systems
Learn how to measure and improve retrieval quality.

[H3] GraphRAG in Action: A Simple Agent for KYC Investigations
Learn how to equip a KYC agent to uncover fraud.

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[H3] GraphRAG for Accurate, Explainable AI
Get short primer on how to combine knowledge graphs with RAG.

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[H3] Vector-Based vs Graph-Based Semantic Search
Learn the benefits of providing semantic search better context.

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[H3] Accurate RAG Results With Knowledge Graphs
Learn about how to build knowledge graphs to accurate, explainable RAG.

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[H3] Advanced RAG Patterns for Chatbots & Personalized Emails
Learn how to use a knowledge graph go further with RAG.

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[H3] Ontology-Driven RAG Patterns for Knowledge Retrieval
Learn how to get set up and start working.

[H3] Build AI Faster With The GraphRAG Python Package
Learn to build knowledge graphs, implement advanced retrievers, and create GraphRAG workflows.

[H3] Kickstart AI App Dev With Neo4j’s GraphRAG Ecosystem
Learn how to quickly build a knowledge graph from unstructured data.



[H3] AI Real-World Use Cases
Learn how to build AI app for practical use in the real world.

[H3] Build Accurate AI Chatbots
Learn how to ground chatbots in enterprise data with a knowledge graph.

[H3] Improve Vector Search Results Using a Knowledge Graph
Learn the latest data analysis with AI technologies.

[H3] Devs Guide to GraphRAG for Accurate AI
Learn how to prepare a knowledge graph and work with the three main GraphRAG patterns.

[H3] Devs Guide: AI For CX – A Retail Example
Find out how to improve all the touchpoints in a customer journey using GraphRAG.

[H3] Devs Guide: GraphRAG Agent for Customer & Retail Analytics
See how to improve data quality and retrieval with this end-to-end worked example.