Every week, early-stage founders ask us the same question: how do we make our AI accurate, explainable, and ready to scale? Over the past year, we have worked with numerous startups in the Neo4j Startup Program as they moved GenAI prototypes into production on a graph database. We have seen which architecture decisions pay off, which ones quietly create technical debt, and where teams lose months they can't afford.
In this session, we will share the patterns that separate startups that ship from those that stall. You will see how founders use GraphRAG to ground LLM answers in connected data, build agent memory with agentic GraphRAG, and set a sound graph data model early to prevent costly rework. We will walk through the tooling these teams reach for, including Neo4j AuraDB, Cypher, knowledge graphs, and MCP, and the trade-offs behind each choice.
You will leave with a practical model for designing graph-native AI from day one: when to add a knowledge graph, how to structure nodes and relationships for retrieval, and how to validate your approach before you scale. Whether you are building your first agent or hardening an existing product, you will take away concrete lessons from a year of watching AI startups build, break, and rebuild on graphs.
Brian O’Keefe is a Manager, Solutions Engineering for the Startup Program at Neo4j. He works with startups to ensure their solution is technically sound as they develop, launch, and scale on Neo4j. He has over two decades of experience in various software architecture and research roles, many of which involved graph-based applications.
