GraphRAG and knowledge graphs
GraphRAG is not the answer to everything. We use it when the real problem is navigating complex relationships, auditing sources, and answering questions a normal search engine or vector RAG does not solve well. If the diagnostic warrants it, we connect entities, documents, and business rules into a knowledge graph with traceable answers.
— data.world / Microsoft Research, 2024
Pain
Scattered information, invisible relationships, answers without clear sources, and decisions that are hard to audit.
Response
Connected knowledge, traceable queries, and answers that show where each conclusion comes from.
How it adapts to the case
GraphRAG and knowledge graphs
Use case
We confirm the problem truly needs relationships, traceability, or multi-hop reasoning.
Knowledge model
We define entities, relationships, documents, permissions, and sources of truth.
Construction
We create the graph, connect documents, and integrate it into the query or agent flow.
Evaluation
We measure accuracy, traceability, latency, costs, and usefulness for users.
Outcomes
Only if warranted