Only 12% of AI projects make it to production, according to IDC's 2025 survey. Companies are pouring billions into AI with no clear ROI, and spend wastage.
Hear real customer stories, including how Prospa how it used Neo4j to cut loan application analysis from days to hours. You’ll also hear other case studies and how they’ve productised AI. Plus, get the latest updates and announcements from Neo4j, including our recent acquisition and AI roadmaps.
Then, AWS joins us to explore how a Neo4j knowledge graph can keep multiple AI agents aligned on the same business definitions when querying shared data — so answers stay consistent, auditable, and trustworthy across your AI stack.
Why attend:
Case Studies – hear real customer case studies and our vision for productising AI
AI on AWS – how a Neo4j knowledge graph keeps multiple AI agents aligned on the same business definitions when querying shared data, for AI that's accurate, consistent, and explainable
Latest from Neo4j – updates and announcements, including our recent acquisition and updates on Infinigraph GA, Virtual Graph, and more
Connect with peers – build your network, exchange ideas, and collaborate with your fellow peers across the industry
Fraser Sequeira, Startup Solutions Architect AWS
When multiple AI agents share the same data lake, they each bring their own interpretation of your business terminology. "Revenue" means different things to different agents and the result is inconsistent answers, no auditability, and eroded trust in AI-driven analytics.
This session shows how a Neo4j knowledge graph can act as a semantic bridge between AI agents and your data sources, ensuring every agent speaks the same language.
