Neo4j joins forces with IBM to bring graph intelligence to enterprise AI customers

We’ve recently partnered with IBM to extend Neo4j’s enterprise AI capabilities to IBM customers worldwide. We’ve created a joint offering powered by the Neo4j Graph Intelligence Platform — Neo4j Intelligraph with IBM uses IBM watsonx Orchestrate and the IBM watsonx.data portfolio to give enterprises the critical component their AI stack lacks: a living map of how the business actually operates, so their agentic systems can retrieve, reason, and act with defensible context. The offering is a first-party product licensed and supported by IBM, one contract, one invoice, one support path.

Accelerating enterprise AI without sacrificing AI sovereignty

According to the MIT State of AI 2026 report, over 90% of organizations regularly use AI in at least one business function. But the report also found that only 44% successfully scale AI beyond pilots. Many studies have identified the same roadblocks: poor data quality and high AI costs.

So why aren’t organizations aggressively tackling these issues? The IBM Institute for Business Value found that 91% of execs don’t understand AI interdependencies across vendors, models, and infrastructure. As business leaders look to scale AI, they’re wondering how they can maintain flexibility and control without disrupting operations.

We designed our new offering with AI sovereignty in mind. IBM’s transactional and analytical platforms sit underneath Neo4j’s graph intelligence system, ensuring that all customer transactions—payments, price risk tracking, freight management, etc.—and the relationships between them are connected, stored, and analyzed through a native graph database. 

“Neo4j and IBM watsonx capabilities create a view into how enterprise business transactions connect while running in customers’ own data centers or private clouds, under their own operational and regulatory controls,” said Martin Oberhofer, VP of Data Core Software Products, IBM. “Our customers who are looking for an integrated solution that combines business context and AI sovereignty benefits can use these capabilities to get the most value out of their AI stack.” 

Tackling a central problem facing enterprise AI: Lack of business context ontology

Execs are asking themselves another key question: How do we build AI we can trust with decisions that actually matter? The future of AI depends on their ability to answer that question confidently, and most are still struggling with it.

But it has a clear answer: no AI system can make reliable business decisions without adequate business context and a guiding map of the relationships. For example, if you ask an AI agent the same question twice and get a different answer, that’s a major issue. Without durable memory, context may not carry over between sessions. A customer support agent can approve a refund, lose the evidence behind that decision, and treat the next interaction as a brand-new case. Without context, agents can’t make decisions consistently or learn from past cases. And as enterprises increasingly deploy systems designed to retrieve, reason, and act, the quality of context becomes decisive. 

Neo4j Intelligraph with IBM enables organizations to build a graph-based knowledge layer, which persists relationships between data entities and provides AI agents context that explains how facts, people, transactions, and events connect. Agentic systems that reason over this kind of context become more accurate, explainable, and governable—they scale to production. 

How Neo4j Intelligraph with IBM delivers trustworthy AI

For IBM customers wondering how our platforms interoperate within the new offering, IBM’s agent orchestration solution, watsonx Orchestrate, uses Neo4j to provide a long-term memory and knowledge layer that maps the people, policies, transactions, and prior actions agents need to understand. This enables agents to generate accurate answers, explain their decisions, and learn from past decisions—all while leaving an audit trail. Watsonx Orchestrate coordinates execution across business workflows and with IBM watsonx.data, Neo4j can add relationship context to enterprise data and vector search, allowing teams to understand how evidence is connected, why it matters, and what action should follow.

Key use cases: Where context is critical

Neo4j Intelligraph with IBM provides critical business context for AI systems across a variety of use cases, including:

• Fraud investigation. Fraud rarely trips a single alarm. It hides in the connections between identities, accounts, devices, and transactions. No single system can see it all, and every missed connection becomes a loss booked. Neo4j Intelligraph with IBM follows those connections so the pattern surfaces before the money moves, and every flag comes with an explanation an investigator can act on.
• Customer service resolution. An AI agent that sees only part of the customer will improvise the rest, and it will improvise in front of the customer. Providing all the context—a connected view of customers, products, cases, and policies—grounds every answer and precludes confident guesses.
• Governance and compliance. When a regulator or board asks why an AI system acted, “The model decided” is not a defense. A graph traces every decision back to the data, relationships, and controls behind it, so the answer survives scrutiny.

Get started with Neo4j Intelligraph with IBM

Neo4j Intelligraph with IBM is now available through IBM’s global sales and partner network. IBM will provide first-line support, with Neo4j providing advanced product support, giving enterprises a direct way to purchase, deploy, and support graph capabilities as part of their broader IBM relationship.

A US public-sector-compliant version is also available.