We encourage you to join us early to collect your badge and network over a light breakfast. Program starts at 9:30 sharp.

Join us as Neo4j's Emil Eifrem explains how graph reveals the natural connections in your business data and provides the context AI needs to deliver real results. See how leading organizations turn enterprise knowledge into competitive advantage, powering intelligent decisions and driving business transformation.

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Understanding a financial graph network structure is a complex task. We introduce a new AI‑based graph visualization tool designed to make financial network analysis accessible to non‑graph experts. The tool enables users to explore relationships, detect patterns, and identify risks without requiring prior knowledge of graph theory. The approach lowers the barrier to advanced financial network analysis while supporting faster and more informed decision‑making.

Global life sciences supply chains generate vast amounts of data, but the real challenge is modelling it to reveal meaningful relationships. In this session, Capgemini shares how they helped AstraZeneca transform large-scale Snowflake datasets into a traceable graph model spanning millions of nodes. Learn how this approach enables greater transparency, preserves data lineage, and creates a trusted foundation for AI-driven exploration in complex, regulated environments.

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Modern supply chains are complex, multi-layered networks, yet fragmented systems often limit visibility, slow reactions to disruption, and make change hard to assess. In this talk, we share our journey to building a 360-degree, graph-based view of our end-to-end supply chain (SCM 360) using Neo4j, enabling impact analysis, risk propagation, traceability, and scenario exploration. We conclude with key lessons learned and what it takes to move from analytical insight to production-ready supply chain intelligence.

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An insider look at the latest Neo4j innovations enabling relationship-driven intelligence at scale. Learn about new features and enhancements that make Neo4j essential for developers building interconnected data apps and AI.

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Q&A with all morning speakers

1. Hands-On Workshop: Go from Zero to Production with Neo4j, Aura, and AI Agents
   

   In this hands-on workshop, you’ll build practical graph skills from the ground up. Learn how to model and load relational data into Neo4j, turn it into a working knowledge graph, and visualize key relationships and metrics. Then, put it all into action by creating an AI agent on Neo4j Aura—so you can query, explore, and converse with your data to uncover insights and hidden connections.
   
   No prior graph experience required; you’ll leave with a clear path to taking graph-powered applications into production.

   
   
2. Hands-On GraphRAG Workshop: Build Smarter GenAI Apps with Knowledge Graphs
   

   While GenAI offers great potential, it faces challenges with hallucination and limited domain knowledge. Graph-powered retrieval augmented generation (GraphRAG) helps overcome these challenges by integrating vector search with knowledge graphs and data science techniques. This approach improves context, enhances semantic understanding, enables personalization, and facilitates real-time updates.
   
   

   In this workshop, you’ll explore detailed code examples to kickstart your journey with GenAI and graphs. You’ll leave with practical skills you can immediately apply to your own projects.