GraphRAG Implementation for Enterprise

An enterprise RAG deployment reaches production in six weeks when the scope is a single domain and the acceptance criteria are written first. Weeks one and two are ontology and retrieval architecture, three and four are the pipeline and the evaluation harness, five and six are hardening, citations and handover. Programmes that run longer are usually modelling the whole enterprise schema at once rather than the one domain the first agent actually needs.

Vector RAG returns similar text chunks and hopes the answer is inside one. GraphRAG returns structured facts (this patient's active medications, this contract's parties, this transaction's counterparty) plus the source paragraph: and constrains the LLM to answer only from what was retrieved. In our benchmarks, GraphRAG typically produces single digit percentage hallucination rates versus 10 to 20% for vector only baselines.

Not necessarily. For a first PoC we can build a minimal graph over one source system: enough for the retriever to walk two or three hops. The graph grows as new use cases land. Most teams don't start with a 'perfect enterprise graph'; they grow it.

OpenAI GPT 4/4o, Anthropic Claude 3.5 Sonnet, and open weight Llama 3 / Mistral for self hosted deployments. The retrieval and grounding layers are LLM agnostic: swapping providers takes a config change, not a rebuild.

We build a golden test set during discovery (typically 200 to 500 canonical Q/A pairs verified by domain experts) and run it against every candidate release. In production, we sample 1 to 5% of live queries for human review and feed the corrections back into the eval set. The rate is reported weekly.

Fixed scope pilots start at $75K for 8 weeks: includes the graph build, retriever, eval harness, and one production use case. Enterprise engagements with multi source ingestion and compliance sign off run $150 to 350K. Every pilot is backed by our guarantee: hit the hallucination reduction target or you don't pay.