About this webinar
In the rapidly evolving field of AI, retrieval is only half the battle. The other half? Knowing how to move through your data - deterministically, intelligently, and at scale.
This is a follow-up to our popular "How to Build a Knowledge Graph for AI" blog post. This time, we go deeper: instead of building the graph, we focus on navigating it - using an LLM to generate queries that traverse a rich, structured knowledge graph and return grounded, deterministic answers.
We'll work with an e-commerce graph that combines relational data, embedded product descriptions, and customer reviews - showing exactly how an agentic system can reason across all of it.
Speakers
Martin Schaer
AI Solutions Engineer at SurrealDB
In this session you'll learn
How to structure a knowledge graph that blends relational data with vector embeddings and graph edges
How LLMs generate graph queries to navigate complex, multi-model data
How to produce deterministic, citation-backed answers from a knowledge graph
How agentic memory fits into a full RAG pipeline