Enterprise Knowledge Graph

Give your LLMs a reasoning layer they can trust.

Vector search alone retrieves passages. An Enterprise Knowledge Graph captures the entities, relationships and policies that make those passages mean something — and lets GraphRAG answer multi-hop questions with citations.

What I build

  • Domain ontology — entities (Customer, Asset, Contract, Inspection, Complaint, Location) and the relationships that matter for your decisions.
  • Ingestion pipelines — ETL + LLM-assisted entity & relation extraction from PDFs, ERP, GIS layers, ticketing systems and email.
  • Storage — Neo4j for native graph + pgvector / Milvus / Weaviate for embeddings; hybrid retrieval at query time.
  • GraphRAG retrieval — community summaries, multi-hop traversal, query rewriting, answer grounding with node-level citations.
  • Governance — provenance, lineage, row/edge-level access control, redaction at retrieval time.

Why GraphRAG outperforms plain RAG

  • Answers questions that span multiple documents and require connecting facts.
  • Reduces hallucinations — every claim links back to a node or edge in the graph.
  • Encodes business rules and hierarchies (org, geography, asset trees) that flat chunks lose.
  • Makes the system auditable — critical for public-sector and regulated workloads.

Stack I typically use

Neo4j · pgvector · LangChain · LlamaIndex · Microsoft GraphRAG · spaCy / GLiNER for extraction · LLaMA / DeepSeek / Mistral via vLLM · FastAPI / Node.js for serving · Next.js for the UX.

Typical outcomes

  • Knowledge worker time on document search reduced 30–60%.
  • Single source of truth across siloed CRM / ERP / case management.
  • Foundation for downstream MCP servers and agentic automation.