Do you need a full knowledge graph before GraphRAG works?

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.

Which LLMs do you build against?

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.

How do you measure hallucination rate in production?

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.

What's the pricing structure for a GraphRAG pilot?

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.

AI & Machine Learning

GraphRAG Implementation for Enterprise

Reduce hallucinations with grounded AI. Every answer cites the exact graph node and source document it came from.

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What is GraphRAG and why use it over standard RAG?

The Answer

GraphRAG is retrieval-augmented generation that retrieves from a knowledge graph instead of (or alongside) a vector store, so every AI answer is grounded in explicit relationships and carries citations back to source. Compared with vector-only RAG, it reduces hallucinations on complex, multi-hop questions and makes answers auditable — what regulated production workloads require.

What you get

Three outcomes we commit to before we start.

Citations on every answer

The retriever returns structured graph facts plus the source paragraph. The LLM is constrained to answer only from what was retrieved: and every citation is a clickable link back to the source document.

Sub 2% hallucination rate

We build the evaluation harness alongside the retriever. Each release is scored against a golden test set; regressions block deploy. Production hallucination rates typically land single digit percentage after two iteration cycles.

Model agnostic: swap LLMs without rebuilding

GraphRAG separates retrieval from generation. Start with GPT 4 or Claude; swap in Llama or Mistral in your VPC when your Security Officer requires it. The graph, the retriever, and the eval harness carry over unchanged.

The Guaranteed Production Pilot

Fixed scope · Written target

A production GraphRAG Implementation system in your VPC: audited, documented, owned by your team.

Not a slide deck and not a sandbox demo: a working GraphRAG Implementation deployment inside your own cloud boundary, mapped to your compliance controls and handed over with the schema, the eval harness, and the runbook.

Speed

Architecture and success criteria signed off in week one. First working slice running in your environment inside 30 days.

Zero effort

Fully done for you. Our senior squad owns ontology, build, evals, and compliance mapping: your team reviews and signs off, nothing more.

Risk reversal

Fixed scope, fixed price, and a measurable success target agreed in writing before we start. Miss the target and you don't pay for the pilot.

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