Knowledge graphs are rapidly becoming essential for taking GenAI projects from proof of concept to real-world production. They make GenAI applications more reliable, transparent, and secure, while also providing a human-navigable, queryable view of enterprise knowledge. In this session, we’ll go beyond theory, as we won’t just talk about knowledge graphs, we’ll show you how to build one.

You’ll learn how to connect siloed data, enrich it with semantics, and structure it for use in GenAI applications. We’ll walk through real tools, examples, and simple steps for getting started, even if you’ve never worked with graph technology before.

Whether you're a data scientist, engineer, or technical lead, you'll walk away with a clear understanding of how to prepare your enterprise data for AI use cases and how a graph-based approach can unlock faster, more trustworthy results.