Databricks customers extend the lakehouse with Neo4j graph intelligence, moving data from Delta Tables into knowledge graphs enriched with relationships for governed analytics and AI. With Agent Bricks, they build GraphRAG apps and assistants grounded in connected enterprise data.
Visit us at Booth 604 to see how connected data drives smarter AI outcomes.
Fraud rings do not announce themselves in a single row. They hide two or three hops away from the obvious suspects, woven into transactions that look ordinary until you trace the full network. The analyst tools stay the same, what changes is what the data reveals. Your Databricks Lakehouse already holds the data. What it lacks is a way to traverse it as a connected graph. We walk through a live fraud investigation and show how Neo4j turns days of manual analysis into a query Databricks Genie answers in seconds. The hidden patterns do not stay hidden once they become columns. Neo4j scores every account; those scores land in your Lakehouse as ordinary dimensions. Databricks Genie needs no changes; it queries graph scores the same way it queries region or balance. Before enrichment: a flat list. After: accounts at the center of distinct fraud communities. Attendees leave with the notebook and graph data model on GitHub to replicate this on their Delta Lake data.
Join us for Data After Hours at Oracle Park, an exclusive evening of networking, conversations, and entertainment with leaders across the data and AI community during Databricks AI Summit. Taking place on Wednesday, June 17, 2026, at the iconic Oracle Park in San Francisco, the event will run from 7:30 PM to 10:30 PM, with doors opening at 7:30 PM. Connect with peers, partners, and industry innovators while enjoying one of San Francisco’s most celebrated venues
Retail AI does not earn trust by sounding conversational. It earns trust by remembering context, understanding product relationships, checking live business data, and grounding answers in source documentation. That requires more than a chatbot over a catalog. It requires a connected intelligence layer where product knowledge, customer context, inventory, pricing, documentation, and prior interactions can be reasoned over together.
INTERPOL’s 2026 Global Financial Fraud Threat Assessment puts global fraud losses at $442 billion in 2025, with financial fraud now ranked among the top five global crime threats. INTERPOL describes it as the industrialization of fraud, driven by AI and global criminal coordination. Much of that loss comes from coordinated schemes that existing analytics infrastructure is structurally unable to detect.
Global supply chains have never been more exposed. Tariff shifts, geopolitical disruptions, and pandemic-era shortages have revealed a structural problem that most organizations already suspected but couldn’t see clearly: they know their tier 1 suppliers, but very few know what sits behind them. A component shortage at a tier 3 supplier, two steps removed from direct contact, can halt production before anyone has time to respond.
AI startups are building with graph databases because AI needs well-managed context, not incoherent collections of data and tools. We’re investing in the future of AI with the Neo4j Startup Program, giving you the resources to build explainable, scalable, and production-ready applications.
Apply now for up to $16,000 in free Aura credits to use on our fully-managed cloud offering, technical consultations with graph experts, and go-to-market opportunities.
In this hands-on guide, Neo4j’s Jesús Barrasa and Jim Webber show data scientists and engineers how to build and apply knowledge graphs to solve today’s most complex knowledge management challenges. Through practical examples and common design patterns, readers learn how to create knowledge graphs that grow in value with more data, enhanced by algorithms and machine learning for deeper insight and intelligence.
