Neo4j Invests $100 Million in GenAI and Launches Key Agentic AI Innovations

This is a pivotal moment in the evolution of artificial intelligence. According to recent research from MIT, 95 percent of organizations get no return from their AI pilot programs. The reason? “Model quality fails without context.” MIT points to a lack of contextual learning and memory as the biggest reasons for the failure of so many AI projects.

Neo4j solves this problem by design. As the world’s leading graph intelligence platform for AI systems, we provide an infrastructure knowledge layer that ensures accurate, explainable, and contextually aware AI outcomes—allowing enterprises to eliminate wasted AI spend and deploy AI applications into production.

Today, we’re announcing a $100 million investment to expand and deepen our critical role in the AI ecosystem and to better help our customers transition from experimental GenAI pilots to intelligent systems that operate reliably at scale. The investment will help us fuel product innovation, create one of the largest dedicated cohorts of AI-native startups, and expand and empower our leadership team to drive innovation. 

“Agentic systems are the future of software, but they need contextual reasoning, persistent memory, and accurate, traceable outputs—all of which graph technology is uniquely designed to deliver,” notes Emil Eifrem, Co-Founder and CEO at Neo4j. “Neo4j transforms disconnected data into actionable knowledge, and this investment allows us to advance that vision faster.”

New Tools to Accelerate the Development of Production-Grade Agents

The investment has already helped us solve a major enterprise AI challenge: building intelligent agents that are fully grounded in organizational data. We’ve developed two new tools—Neo4j Aura Agent and the Model Context Protocol Server for Neo4j—that address critical development obstacles, including siloed data, disconnected tools, and a lack of specialized expertise.

Neo4j Aura Agent

Aura Agent, now in early access, is an agent-creation platform that enables users to rapidly build, test, and deploy AI agents grounded by their own enterprise data in AuraDB. It provides end-to-end automated orchestration and AIOps for graph-based knowledge retrieval. The platform abstracts away the complexity of integrating diverse LLM and agentic frameworks, GraphRAG retrieval patterns, text-to-query generation (via specialized Text2Cypher models), and secure agent-serving infrastructure. It can eliminate months of development time.

Among the key capabilities of Aura Agent:

• Agent creation in the Aura console with a no-/low-code agent builder
• Agent retrieval tools for pre-defined graph query templates, vector similarity search, and text to query
• Agent testing via a playground chat UI 
• Secure agent deployment for consumption by downstream apps via an authenticated endpoint, with MCP support coming soon

Aura Agent will be generally available in Q4 of this year.

Model Context Protocol Server for Neo4j

The Model Context Protocol (MCP) Server for Neo4j integrates graph-based retrieval and reasoning into virtually any AI system, allowing the AI to break down tasks for explainable, multi-hop retrieval on your data. This greatly increases the reliability and trustworthiness of agents and AI applications, readying them for production use.

The initial release will be an open-source server that customers can deploy themselves, written in GO. It will include tools for inferring a schema from a Neo4j instance and executing graph read queries. Later this year, we will offer the MCP Server as a fully supported, hosted server in Neo4j Aura.

A Faster, More Reliable Path to Agentic AI

Together, Aura Agent and the MCP Server offer enterprises a rapid, dependable way to develop agentic AI that is accurate, explainable, and production-ready.

“Neo4j Aura Agent promises to improve healthcare by designing and deploying AI agents that create comprehensive knowledge graphs from our trusted biomedical knowledge,” says Nitin Sood, Senior Vice President and Head of Product Portfolio and Innovation at QIAGEN. “With new ways to interrogate these graphs, researchers can approach drug discovery in ways that were impossible before. That’s what makes this so promising for drug discovery and healthcare.”

An Industry-Leading AI Startup Program

In addition to our latest product investments, we’re excited to create one of the largest dedicated cohorts of AI-native companies to date. Over the next 12 months, the Neo4j Startup Program will select and support more than 1,000 startups worldwide. Participants receive access to cloud credits, technical enablement, and go-to-market support to build and scale agentic systems with graph technology. The program currently includes visionary companies like Firework, Garde-Robe, Hyperlinear, Mem0, OKII, Zep, and Rivio. 

“Rivio builds AI agents that navigate the complex relationships in procurement, spanning suppliers, contracts, pricing, compliance, and market dynamics,” says Hala Jalwan, Co-Founder and CEO at Rivio. “Neo4j enables us to model that complexity with the accuracy our customers require.”

David Klein, Co-Founder and Managing Partner at One Peak and a Neo4j Board Director, reports that eight out of every 10 startups he speaks with are re-platforming on Neo4j. “They tell me that it’s the natural choice when you’re serious about building intelligent systems with context and memory,” he says.

Empowered Product Leaders and an Expanded Leadership Team

Empowering our product leaders and growing our leadership team has been a priority for us as we expand Neo4j into a full graph intelligence platform for GenAI. Sudhir Hasbe was recently named President and Chief Product Officer, recognizing his vision and leadership during a remarkable recent run of technical breakthroughs in GenAI. His leadership ensures continuity of vision and an accelerated pace of AI innovation.

Mark Woodhams, ex-Oracle and enterprise software veteran, recently joined as Chief Revenue Officer to lead our global field and partner operations, services, and customer success organizations. And Ajay Singh joined as Head of Global Field Engineering. Ajay joined us from Databricks, where he helped scale field engineering during the company’s rapid growth.

“These leadership moves, combined with our investment and product launches, set Neo4j up for its next chapter as the graph intelligence platform for intelligent applications and AI systems,” observes Neo4j CEO Emil Eifrem.

Critical Infrastructure for Tomorrow’s Enterprise AI

Neo4j customers include over 80 percent of the Fortune 100 and more than half of the Fortune 500—many use us to power their ambitious AI solutions, including Uber, Walmart, and Klarna. These companies rely on Neo4j to provide the structured memory and relational context that allow their agents to reason, act, and remember—essential capabilities for production-grade agentic systems.  

Over the past year, Neo4j has seen 6x growth in GenAI customers, and more than half of our top 100 customers have expanded their Neo4j footprint. We’ll continue to invest in their AI ambitions. Given our vision, expertise, and leadership, we expect Neo4j graph technology to drive the evolution of AI well into the future.

As Forrester VP Principal Analyst Charles Betz put it in a recent blog post, “Every function in the enterprise can start moving toward graph-driven, agent-enabled learning, and every domain that produces traceable work can benefit. It’s tempting to think that LLMs alone can solve this. But without structure, GenAI alone drifts. The graph is essential. It is the skeleton to the LLM’s flesh.”