Webinar: Full-stack knowledge graph RAG with agentic memory

​Webinar: Full-stack knowledge graph RAG with agentic memory

​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.

​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

​Blog: https://surrealdb.com/blog/how-to-build-a-knowledge-graph-for-ai

​Speaker

​Martin Schaer
(Solutions Engineer at SurrealDB)

​​Martin is a computer science engineer working at SurrealDB and his GenAI startup. He recently worked in lab automation, where he designed and developed a declarative framework for instrument drivers and a 3D visualiser for testing robotic transport solutions. His background also includes founding a successful advertising agency in Costa Rica and extensive work in web development, UX, branding, and digital marketing.

​Joining the webinar

​Once you register, you'll get an email from SurrealDB with your personal join link. Hold onto it - that's your ticket in.