Empowering Customer Breakthroughs
Over 1,700 customers, including 75 Fortune 100 companies, use Neo4j to implement breakthrough solutions across diverse use cases like fraud detection, supply chain optimization, real-time recommendations, and identity & network security. We’re changing the rules of the database market by enabling organizations to uncover hidden relationships and patterns in their data. Graph inherently represents business logic better than traditional relational databases. Developers can easily perform multi-hop graph queries to uncover these connections while ensuring speed and continuity. Here’s how we’ve helped customers achieve extraordinary outcomes:-
- Adobe reduced the hardware footprint for their Behance network from 125 servers to just three with Neo4j, reducing maintenance by 3x, storage by 1/1000, and costs by 16x while improving UX and extensibility.
- Dun & Bradstreet, powering 90% of the Fortune 500 with data insights, launched a new service to rapidly interpret complex corporate structures and ownership. Previously, a single query tied up staff for 10-15 days. With Neo4j, they reduced customer risk profile research from days to hours by applying graph algorithms.
- Transport for London built a real-time digital twin of London’s intricate transport network using Neo4j’s graph database with the aim to cut congestion by 10%, and deliver $750 million in annual savings by optimizing incident response times.
-
- Native integration with Amazon Bedrock: Customers using Amazon Bedrock foundational models can reduce hallucinations by grounding their LLM/RAG use cases of virtual agents, real-time search, text, and summarization with an enterprise knowledge graph. With the addition of vector search, Neo4j can capture both explicit and implicit relationships and patterns, enabling AI systems to reason, infer, and retrieve relevant information effectively.
- Native integration with Google Cloud Vertex AI: Customers can now leverage knowledge graph with Google’s large language models to make generative AI outcomes more accurate, transparent, and explainable
- GenAI stack with Docker, LangChain, and Ollama: Out-of-the-box ready-to-code secure stack designed to help developers get a running start with generative AI applications in minutes.
- Vector Search: Integrated approximate nearest neighbor search natively into Neo4j, enabling contextual similarity queries on graph data that drives AI applications.
- Parallel Runtime: Introduced morsel-based parallel processing to run graph analytics queries concurrently across multiple cores, with customers seeing up to 100x faster complex queries.
- Change Data Capture: Added native capability to track data changes in real-time and take instant action across use cases like identity management.
- Pathfinding Algorithms: Released new algorithms for longest path identification, topological sort, and more that optimize workflows in supply chain, logistics, and beyond.
Cloud-First Development
Neo4j has deep ecosystem partnerships, making graph database and analytics available across all the major cloud providers. This allows us to meet customers on their preferred cloud. Our focus is on delivering an enterprise-grade experience optimized for the cloud. Over the past year, we’ve deepened our product integrations with Google Cloud, Amazon Web Services (AWS), and Microsoft Azure:-
- Google Cloud: Integrated with Google Cloud’s Vertex AI platform so customers can now leverage Neo4j knowledge graphs to enhance Vertex AI outcomes with greater accuracy, transparency, and explainability.
- Amazon Web Services: Formed a strategic, multi-year collaboration with AWS to accelerate enterprise AI development. We launched Neo4j AuraDB Pro on AWS Marketplace and integrated with Amazon Bedrock, helping enterprises solve key AI challenges by grounding them in Neo4j knowledge graphs.
- Microsoft Azure: Collaborated closely with Microsoft to bring Neo4j AuraDB Enterprise to Azure customers worldwide. Joint initiatives enhance performance, scalability, and ease of use for AuraDB customers through integration with Azure services like Active Directory, Azure PrivateLink, and Azure Marketplace.
GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, MAGIC QUADRANT and PEER INSIGHTS are registered trademarks of Gartner, Inc. and/or its affiliates and are used herein with permission. All rights reserved. Gartner® Magic Quadrant™ for Cloud Database Management Systems, 18 December 2023, Adam Ronthal et. Gartner Peer Insights content consists of the opinions of individual end users based on their own experiences, and should not be construed as statements of fact, nor do they represent the views of Gartner or its affiliates. Gartner does not endorse any vendor, product or service depicted in this content nor makes any warranties, expressed or implied, with respect to this content, about its accuracy or completeness, including any warranties of merchantability or fitness for a particular purpose. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. The Gartner logo is a trademark and service mark of Gartner, Inc., and/or its affiliates, and is used herein with permission. All rights reserved.