How to Choose a GraphRAG Implementation Partner
Published 2026-09-05 · Agentic Giants · 8 min read
What should you look for in a GraphRAG implementation partner?
The Answer
Evaluate a GraphRAG partner on five things: proof of production deployments (not demos), a written evaluation methodology for hallucination rates, a security posture that matches your compliance regime, IP and knowledge-transfer terms in your favor, and senior engineers actually doing the work — not just the partner who sold you the deal. Below is the full framework, including when you shouldn't hire a partner at all.
Who this guide is for
CTOs, CDOs, and heads of data evaluating vendors for a grounded-AI or knowledge-graph project — typically after a first RAG pilot produced hallucinations, failed a compliance review, or stalled in legal.
The 12 evaluation criteria
Production evidence
- Named production deployments. Ask for at least one referenceable client running GraphRAG in production, and ask what broke in the first month. A vendor with no war stories has no production experience.
- Eval methodology in writing. How do they define and measure a hallucination? What’s the eval-set size? A partner who can’t answer this in one page cannot guarantee a reduction.
- A guarantee with teeth. What happens if the accuracy target is missed? (Ours: the pilot is free. Others may differ — but “we’ll work with you” is not an answer.)
Security & compliance
- Deployment boundary. Can they deploy inside your VPC with zero data egress? For regulated data this is non-negotiable.
- Compliance mapping. SOC 2 / HIPAA / GDPR controls documented before work begins, not retrofitted.
- Prompt-injection and data-boundary defenses on any user-facing surface — a hardened surface is what keeps agent access from becoming agent risk.
Delivery model
- Who actually does the work. Ask for the CVs of the engineers assigned — not the partner who sold the deal. (This is the classic big-consultancy failure.)
- Redundancy. Is there a backup engineer who knows the system, or a single point of failure? (The classic freelancer failure.)
- Cadence and visibility. Daily standups, weekly reviews, monthly eval reports — in the contract, not the pitch.
Ownership & exit
- IP terms. You should own 100% of code, schemas, and eval harnesses from day one.
- Knowledge transfer. Documentation and handover built into every stage, so leaving is always possible.
- Model-agnostic architecture. Retrieval separated from generation, so you can swap LLMs without a rebuild.
Red flags
- · Demos on their data, never yours.
- · “Hallucinations eliminated” — nobody can eliminate them; insist on measured reduction.
- · Pricing that requires a long discovery retainer before any deliverable.
- · No answer to “show me your eval harness.”
When NOT to hire a partner
- · Your corpus is homogeneous documents with no meaningful relationships — vector RAG may be enough.
- · You haven’t validated the underlying use case — run a cheap vector-RAG pilot first.
- · You have a senior data team with graph experience and 6+ months of runway — building in-house is legitimate.
Questions to ask in the first call
1. Walk me through your last production GraphRAG incident and what you changed.
Ask this on the first call. A partner with no war stories has no production experience. What you're listening for is a specific incident (data schema mismatch, retriever timeout, eval regression) followed by the concrete change that fixed it and the metric that proves the fix held.
2. Show me a redacted eval report from a real client.
A partner who cannot show even a redacted eval report cannot guarantee an accuracy target. The report should include eval-set size, baseline vs current hallucination rate, and how a hallucination is counted.
3. What does week 3 look like, specifically?
Vague answers ('we scope with you', 'we iterate') mean the plan doesn't exist yet. A serious partner will name what's shipped by week 3 (typically: retriever wired to a first data source, first eval run, initial accuracy baseline) and who signs off on it.
4. What happens contractually if you miss the accuracy target?
The answer is either specific ("the pilot is free", "we refund X", "we ship Y more iterations at no cost") or it's evasion. "We'll work with you" is not an answer — it's the sentence that shows up in dispute emails six months later.
5. Who owns the ontology when we part ways?
You should own the ontology, the code, the schemas, and the eval harness from day one — with documentation adequate for a new team to maintain them. Any answer that involves a proprietary format or a licensed retriever engine is a lock-in trap.
Next step
Ready to evaluate us against this list?
Read our dedicated hiring-intent pages — same criteria, applied to us specifically — and start a confidential briefing when you’re ready.