We build the evals.
You ship the AI.
Golden datasets, calibrated LLM judges, regression gates on every PR, and live quality dashboards — built for your stack and maintained for good. Tool-agnostic: we work inside your Datadog, Langfuse, Braintrust, or LangSmith.
Your AI changed last night. Do you know what broke?
Golden datasets go stale. Judges drift. Model upgrades silently shift your baselines. A one-time eval build is a snapshot; quality is a subscription.
You bought the platform. Someone still has to curate the datasets, design the judges, read the traces, and keep it all alive.
Final-answer checks miss the step where it actually went wrong: the wrong tool, the bad argument, the retrieval that never happened.
Audit → Build → Maintain.
Audit
We read 100+ of your real production traces and map every failure mode into a taxonomy specific to your product. Two weeks, fixed price, exec-readable report.
Build
Golden datasets with provenance. Judges calibrated against human labels — with reported true-positive and true-negative rates. Regression gates on every PR. A dashboard your execs actually open.
Maintain
Weekly runs and reports. Monthly dataset refreshes from live traffic. Quarterly re-baselines when models upgrade underneath you. Evals rot; we're the upkeep.
Your evals shouldn't just find problems.
They should fix them — with a human on the merge button.
Not another admin panel.
for the judge's written reasoning
What we do
Eval Audit & Error Analysis
100+ traces, a failure taxonomy, and a prioritized roadmap in two weeks.
Golden Datasets
Mined from production, labeled by experts, stress-tested with synthetic edge cases. Versioned like code.
LLM Judge Design & Calibration
Custom pass/fail judges per failure mode — validated against human labels, not vibes.
Regression Gates on PRs
Score diffs on every pull request. Quality drops block the merge.
Live Monitoring & Dashboards
Async judges on real traffic, alerts where you work, a dashboard worth bookmarking.
Self-Healing Loop
Production failures become pull requests become test cases. Automatically.
I built and ran the evals system for an industrial AI platform serving global energy companies — where a hallucinated number isn't a bad review, it's a safety problem. I bring that bar to your product.
Proof, not promises.
Shipped in production, anonymized because the work is under NDA. The methodology isn't.
RAG, added to your system — with its evals attached
Need retrieval in your product? I build the pipeline end-to-end — chunking, embeddings, reranking, citations — and ship it with retrieval-quality, groundedness, and citation-accuracy judges wired in from day one. The feature and the proof it works arrive together.
Tiered judge architecture, in production
Deterministic gates first — schema checks, PII regex with a domain allowlist, crash short-circuits. Calibrated LLM judges second, including a hallucination judge that splits fabrication into 5 distinct failure types and tells honest deflection from confabulation. Serving an industrial AI platform used by global energy companies.
100+ production traces, audited by hand
A structured audit covering 19 step types and 17 known failure modes, run across Langfuse and Datadog. The findings redirected an engineering roadmap and became a 37-item, priority-ranked eval plan. This program is what the Audit offer productizes.
Tool-agnostic by construction
Extended an eval harness to ingest Langfuse alongside Datadog behind a single normalized record shape — 30 existing code graders ran unchanged. Whatever you're running, the evals live in your stack, not another one.
Production ops, not notebooks
Debugged and recovered customer-visible deployments across the GitOps boundary — K8s readiness probes, Helm, ArgoCD, API gateways. Self-healing loops need someone at home in real infrastructure; that's the day job.
Customer-facing, end to end
Owned customer tools from prototype to containerized services running in the customer's stack — including grounded system prompts written so a live demo couldn't overpromise. Trust is the product when you're selling quality assurance.
Questions
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Where do you stand?
10 questions, instant 0–100 score, stage placement on the needs map. No email required.
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