For GenAI to deliver accurate and relevant results, it requires reliable retrieval—and that starts with well-prepared data. This data may come from a combination of unstructured (text, PDF documents), semi-structured (logs, XML, HTML), and structured sources (relational tables  and CSV/JSON flat files).

Knowledge graphs help by representing, connecting, and organizing this data. They create a structured framework that enables meaningful integration, querying, and analysis—making data more accessible and usable for Retrieval-Augmented Generation (RAG) pipelines.

Knowledge-graph-powered RAG, or GraphRAG, grounds GenAI results in true relevance, deeper context, and accuracy rather than just generic similarity measures. The result? Smarter, more reliable, and contextually rich GenAI responses.

Join our 30-minute technical webinar to learn how to make your data usable and accessible for GenAI applications. You’ll discover how to:

• Quickly process unstructured & structured data for RAG
• Expose key logic and domain context to your AI by building a knowledge graph to organize, connect, and resolve entities in data
• Apply knowledge graph retrieval patterns to deliver more accurate and relevant results in GenAI apps

Register today to unlock the power of knowledge graphs for transforming your data and enhancing GenAI applications.