AI Evaluation Consultant for Teams Shipping LLM & Agent Features
Demo prompts tell you nothing about production quality. I build the evaluation systems — datasets, judge rubrics, and regression gates — that tell you whether your AI product actually works before your customers find out it doesn't.
When you need an evaluation consultant
- You are about to launch an AI feature and the only testing so far is founders typing prompts
- A model upgrade quietly degraded quality and you found out from customers
- Your agent works in demos but fails on multi-step or tool-use tasks in production
- Investors, enterprise buyers, or compliance teams are asking how you measure AI quality
- You need an eval baseline before switching between model providers
If any of those sound familiar, the fix is not more manual prompt-poking. It is a small, systematic evaluation loop: representative scenarios, explicit pass/fail criteria, and a harness that runs on every change. That is exactly what I build — see how it caught real failures in my case studies.
LLM evaluation consulting, specifically
If your product is built on a large language model — GPT, Claude, Gemini, or an open-weights model — evaluation has model-specific dimensions beyond generic QA: accuracy and hallucination rates on your domain, robustness to prompt variation, regression behavior across model versions, safety and refusal behavior on sensitive inputs, and cost/latency trade-offs between model tiers.
My LLM evaluation consulting covers all of it: benchmark suites for model selection, judge rubrics calibrated to your domain, and version-upgrade gates so a provider's silent model update can't quietly degrade your product. The methodology is documented in the AI Agent Evaluation Handbook.
What you get
Failure-mode analysis
A systematic map of where your LLM feature or agent actually breaks: edge cases, adversarial inputs, tool-call errors, and formatting drift — not just the happy path your demo covers.
Evaluation datasets & judge rubrics
Curated eval sets that reflect real usage, with deterministic checks where possible and calibrated LLM-as-a-Judge rubrics where outputs are open-ended.
Pass/fail quality gates
Scoring logic and regression baselines wired into your workflow, so every model swap, prompt change, or dependency bump gets checked before customers see it.
A harness you keep
Everything I build — scenarios, rubrics, scripts, dashboards — is handed over as working code your team owns and can re-run on every release.
Consultant vs. hiring in-house
Full-time evals engineer
- Months to hire in a scarce specialty
- Significant salary before the first eval exists
- Right once evaluation is a daily, full-time need
Evaluation sprint with me
- Working eval system in 10–14 days
- Fixed price from $7,500 — scope agreed up front
- Your team keeps and runs the harness afterwards
Curious how engagements run day to day? See how we work, or compare all three hiring routes in the AI evals hiring guide.
Frequently asked questions
What does an AI evaluation consultant actually do?
I design and build the testing layer for AI products: failure-mode analysis, evaluation datasets, judge rubrics, pass/fail scoring, and regression harnesses. The outcome is that you know — with evidence — whether your LLM feature or agent is good enough to ship, and you keep a repeatable way to check every future release.
How is this different from hiring through a marketplace?
Marketplace listings are generalist AI consultants. Evaluation is my specialty: I lead benchmark and evaluation work at Turing, delivered tasks for a published frontier-lab benchmark, and built a 43-tool agent benchmark harness. You get a productized engagement with fixed scope, price, and deliverables — not an hourly experiment.
How long does an evaluation sprint take?
The AI Evaluation Sprint runs 10–14 days: failure-mode analysis, a first evaluation set with judge rubrics, pass/fail scoring logic, and a repeatable quality scorecard. Larger agent benchmark harnesses take 4–8 weeks.
Do you work with early-stage startups?
Yes — most clients are startup teams shipping their first serious LLM or agent feature. Engagements are fixed-price and scoped so a small team gets a working evaluation system without hiring a full-time evals engineer.