GraphRAG & Knowledge Graph AI Consulting

Large language models are fluent and forgetful. Give one your documents through a plain vector store and it will answer single-fact questions well and multi-hop questions badly — confidently joining facts that were never connected, or declining to answer when the connection exists but was never retrieved. A knowledge graph fixes the second half of that sentence. GraphGuru builds GraphRAG systems on Neo4j: knowledge graphs constructed from your unstructured data, vector and hybrid retrieval inside the graph, and agent integrations that let your AI applications reason over relationships instead of guessing at them.

Why vector-only RAG falls short

Vector retrieval finds passages that resemble the question. It has no notion of which supplier, which contract, which version — only of similarity. When an answer depends on facts spread across documents that never mention each other, the retriever returns fragments and the model stitches them together, which is where hallucination lives. Graph-plus-vector hybrid retrieval uses the vector index to find entry points and the graph to walk from them to the connected facts, so the model receives the actual chain of evidence. We benchmark this rather than assert it; see GraphRAG vs. Plain RAG for the method we use on client data in week one.

What we build

• Knowledge-graph construction pipelines — entity and relationship extraction from PDFs, tickets, contracts, wikis, and transcripts, with a schema designed for your questions rather than a generic one, and an entity-resolution pass tuned to your data.
• Neo4j vector search — HNSW vector indexes sized to your embedding model, and the native Vector data type (Neo4j 2025.10+, Cypher 25, driver 6.x) for compact, validated storage.
• Hybrid retrieval — vector, fulltext, and Cypher traversal combined with reciprocal-rank fusion, plus graph-aware re-ranking for multi-hop questions.
• Text-to-Cypher with guardrails — schema-constrained generation, read-only execution, EXPLAIN cost gating, and query logging, so analysts and agents can ask the graph questions safely. Our tutorial shows the pattern we harden.
• Evaluation harnesses — a question set with verified answers, an LLM judge with human review, and CI integration so retrieval quality is measured on every change.
• Production hardening — re-runnable ingestion with deduplication, permission-aware retrieval, observability, and cost controls on embedding and extraction.

Tooling we use

• neo4j-graphrag, Neo4j's official Python package for KG construction, retrievers, and generation.
• LangChain and LlamaIndex integrations where your application already lives in one of them.
• Neo4j's official MCP server for agents that use the Model Context Protocol, and the Neo4j GraphRAG context provider in Microsoft's Agent Framework.
• Neo4j Aura Agent, Neo4j's managed agent offering (public early access in 2026, general availability expected later this year), for teams that want the managed path.
• Graph Data Science for node embeddings and community detection when the retrieval problem is structural as well as textual.

We are an independent consultancy and not affiliated with Neo4j, Inc.; we recommend the tools above because they are the ones that work.

Engagement shapes

Two-week GraphRAG proof of value. We take a representative slice of your documents, build both a vector-only and a knowledge-graph retriever, write the question set with your domain experts, and run the benchmark. You get a working prototype, the numbers, and a written recommendation — including "do not proceed" when the data does not justify a graph.

Production build and hardening. From prototype to a system your security and platform teams will sign off: ingestion jobs, schema and resolution rules, access control, evaluation in CI, runbooks. Typically six to twelve weeks with one or two senior engineers embedded in your team.

Eval-driven retrieval tuning. For teams that already have a GraphRAG system and a quality problem: we build or extend the evaluation set, instrument retrieval, and work the failure cases — extraction gaps, resolution misses, retriever weighting — until the metric moves.

Who this is for

Teams with a corpus that is connected — contracts that reference products that reference suppliers, support tickets that reference components that reference releases, research that cites research — and an AI application that has to answer questions across those connections. If your questions are single-hop, we will tell you so in the first week and recommend the simpler system.

All our consultants are senior, located in the United States, and work inside your existing team and process, remote or onsite.

Start with a conversation

Tell us about your documents, your questions, and where your current system falls down. Contact GraphGuru to scope a proof of value.