AI & Knowledge Engineering•Wholesale Distribution Company•2026
GraphRAG Knowledge Graph for Enterprise Supply Chain Intelligence
Neo4j knowledge graph with 12 million nodes and 89 million relationships powering GraphRAG queries across a wholesale distribution company's supply chain — enabling multi-hop relational reasoning that standard RAG cannot handle.
12MKnowledge Graph Nodes
89MGraph Relationships
96%Query Routing Accuracy
1.8sAvg Complex Query Response
Python · Neo4j · LangChain · Claude · Apache Kafka · FastAPI · PostgreSQL · Cypher · OpenAI Embeddings · Redis
Deep Dive
The GraphRAG system was built as the relational intelligence layer within a broader data platform that also includes Cache-Augmented Generation (CAG) for fast lookups and standard RAG for document search. This case focuses specifically on the knowledge graph construction, entity extraction pipeline, GraphRAG query system, and the intelligent routing that directs relational queries to the graph.
| Component | Focus | Status | Key Deliverables |
|---|---|---|---|
| 1 | Graph Schema Design | Completed | Entity type modeling, relationship mapping, property schema, traversal optimization — reduced from 40M to 12M nodes by converting lookup values to properties |
| 2 | Entity Extraction Pipeline | Completed | CDC pipelines via Kafka, LLM-based extraction from unstructured sources, multi-signal identity resolution (98% → 99.3% automatic matching) |
| 3 | GraphRAG Query Pipeline | Completed | Query understanding, template-based Cypher generation (96% accuracy), graph traversal with depth limits, LLM response synthesis with citation trails |
| 4 | Query Routing Integration | Completed | AI classifier routing queries to CAG, RAG, or GraphRAG based on intent — routing accuracy improved from 84% to 96% over three months |
