Does your AI tool have a hard time answering complex questions? Maybe you should consider GraphRAG!

During their presentation “Tracing the Thread: Decoding the Decision-Making Process with GraphRAG” at Enterprise Search & Discovery 2025, Enterprise Knowledge’s Urmi Majumder and Kaleb Schultz discussed how GraphRAG extends traditional RAG by grounding retrieval in a semantic knowledge graph to create transparent and explainable AI. They shared a client success story that focused on enabling program officers at a global philanthropic organization to evaluate investment progress and performance more efficiently, specifically by unifying disparate data sources such as reports, memos, and financial data into a single, interconnected network.

Session attendees learned how GraphRAG differs from traditional RAG, including:

• Why traditional vector similarity sometimes fails to capture domain context, and how graphs bridge that gap;
• How to use GraphRAG to produce traceable reasoning paths with provenance, rather than “black-box” answers;
• How GraphRAG can answer complex, cross-documented questions with a real-life example; and
• The architecture and essential components required for a GraphRAG solution, including ontologies, knowledge graph databases, and retrieval layers answers.