Why do 95% of GenAI projects fail to reach production? Because an LLM without enterprise context is effectively "brain-dead,” a high-powered reasoning engine with no nervous system to connect it to your actual business data. For technical leaders, the bottleneck isn't the model; it’s a data management architecture that lacks the connectivity to ground AI in reality.
In this session, we examine why "context engineering" has become the critical missing link in the enterprise AI stack. We will explore how Knowledge Graphs act as a digital nervous system, transforming isolated data points into a coherent knowledge layer that drives accuracy and trust. You'll see how shifting from basic RAG to GraphRAG can improve LLM response accuracy by up to 3x compared to traditional data structures. You will also see real-world results, including how Prospa slashed loan approval times from 48 hours to just 2.
Autonomous agents remain limited without context, leading to hallucination and disconnected reasoning. Building dependable agents requires a fundamental shift in data architecture. Learn how Electronic Arts (EA) achieved 10× faster time-to-insight and improved agent reliability through graph-based context engineering - proof that the future lies in Context Graphs, not simple context windows.
Dedicated to the future of AI with graphs, NODES AI is an online conference with three tracks focused on Knowledge Graphs & GraphRAG, Graph Memory & Agents, and Graph + AI in Production.
Join us on April 15, 2026 for live technical sessions and hands-on learning from leading experts and practitioners who are shaping what’s next.
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.
