How can we bring the power of GenAI into graph-based applications? This talk presents proven architecture patterns for integrating Neo4j with Google Cloud’s Gemini models to build intelligent systems. We’ll show how AI agents can use Neo4j as a tool; for example, translating natural language questions into Cypher queries for graph-powered answers or leveraging a Neo4j knowledge base as contextual memory for reasoning.

Real-world scenarios will demonstrate that a graph-backed LLM handles tasks like recommendations or incident analysis more effectively than an LLM alone. We’ll also cover best practices for deploying these solutions on Google Cloud, connecting Neo4j (AuraDB or self-hosted) with Vertex AI, ensuring secure and scalable interactions, and optimizing performance.

Attendees will leave with a reference architecture for graph+AI systems and practical tips to apply these patterns—all without getting lost in theory.

Key takeaways from this session include design blueprints for graph-integrated AI agents and guidance on implementing them in enterprise cloud environments.