Production AI Eval Infrastructure
Last verified: October 2026· engagement
Most teams shipped AI features with zero evals. We build eval harnesses, regression suites, online quality monitoring, and A/B infra for prompts and models.
- Outcome
- An eval platform wired into your CI/CD
- Timeline
- 4–8 weeks
- Pricing
- $30–80k build + $3–8k/mo ops
- Buyer
- VP Eng, Head of ML / AI Platform
The problem
You shipped AI features with zero evals. Every prompt or model change is a blind deploy, and the cost of a bad output only shows up after it reaches a customer.
What we do
- Build eval harnesses and regression suites for your prompts and models.
- Add online quality monitoring and alerting for production traffic.
- Stand up A/B infrastructure for prompts and model swaps.
- Wire it all into your CI/CD so quality is a gate, not a guess.
How it fits together
What you get
Built on our open source
openclawOS — An OS-like architecture for AI assistants — a kernel-based design with process-isolated apps.
Building evals that people keep running
The first question is not which framework but what a failure actually looks like for your feature, and it is usually harder to answer than teams expect. Until someone can articulate what a bad output is for this specific product surface — wrong, unhelpful, unsafe, off-brand, too slow to be worth it — there is nothing to score against, and a suite built before that conversation ends up measuring whatever was easy to measure. We start with a short exercise on real outputs: sort a few dozen into acceptable and not, and argue about the boundary cases until the boundary is written down.
Then we build in layers, because the cheap layers catch most of the real defects. Structural checks run on everything and cost nothing: did it parse, does it satisfy the schema, did it come back empty, did it refuse, did it truncate at the token ceiling. Rubric scoring handles open-ended output against the checklist we just wrote. A judge model handles the nuanced cases, and it gets validated against human labels on a real sample before it is allowed to gate anything, because an unvalidated judge is a number with unknown units. Human review is reserved for the highest-stakes slice.
What makes a suite survive is where the cases come from. Hand-written test cases encode the failures you already imagined, which are not the ones that will hurt you. So we wire production back in: traces that errored, truncated, cost far more than the route's median, or that a user retried or corrected go into a review queue, and a reviewer promotes them into the regression suite with one action. A suite built that way has a property hand-written ones never do — every case actually happened to a real user, so a regression on it is a regression that matters.
When this is not the right engagement
Evals are worth building when the surface is stable enough to defend. This work is a poor fit when:
- The feature is still changing shape weekly. Evals built against a moving target get deleted; wait until the surface is stable enough to be worth defending.
- You want a quality number for a report rather than a gate in CI. A metric nobody blocks a deploy on stops being maintained within two months.
- There is no appetite to look at real outputs. Every good suite starts with someone sorting actual responses into acceptable and not.
What “done” means
These are written into the agreement before work starts, so you, your finance team and procurement can see the finish line up front.
Acceptance
- An eval suite built from real traffic covers the failure classes agreed at kickoff.
- The suite runs in CI and blocks a release that regresses past the agreed threshold.
- Online monitoring samples production and alerts on the agreed signals.
- A deliberately injected regression is caught by the gate, end to end.
Not included
- Fixing every failure the evals find — we prioritise them; fixes are scoped separately.
- Labelling large datasets by hand beyond the agreed seed set.
What we need from you
- A sample of production traffic for the feature, redacted as your policy requires.
- CI access to add the gate, and a named owner for the suite.
Common questions about this engagement
Do you work under NDA?
Yes — we sign your mutual NDA before any data or repo access. For audits we prefer read-only access to start; for builds we work in a clean repo under your ownership.
Will we own what you ship?
Always. You own the code, the runbooks, the dashboards. We are explicitly set up to hand off and transition out, not to create dependency.
Vendor-agnostic — what does that mean in practice?
We integrate with what you already run — OpenAI, Anthropic, open-weight models on your cloud, your CI/CD, your observability stack. If a hosted vendor solves it, we will not reinvent it; if a self-hosted tool is the right answer, we will not pretend the hosted one is.
How is this different from a Big Four consulting deck?
We are the engineers doing the work, not analysts handing recommendations to a different team. The deliverable is working software in your repo, not a slide deck.
Can you work with our in-house AI team instead of replacing them?
Yes — most of our engagements pair with an internal owner and ramp them up to run the system after we leave. Many of our best engagements start with "we hired an AI team, help us get them productive."
How long before the suite catches something real?
Usually within the first fortnight, because the structural checks come first and they are cheap: parse failures, schema violations, empty responses, refusals, truncation at the token ceiling. Those catch a surprising share of genuine production defects before any scoring exists. The judge and rubric layers take longer, mostly because validating a judge against human labels is the slow part and skipping it produces a number nobody should trust.
Do we need a human in the loop forever?
Not for volume, but yes for calibration. Human review stays on the highest-stakes slice and on the periodic check that your judge model still agrees with people — judges drift as models update, and an unvalidated judge is a metric with unknown units. In practice that settles into a small recurring review rather than an ongoing cost, and it is the thing that keeps the rest of the suite believable.
Guides for this work
How to build LLM evals
Evals turn 'the model feels better' into a number you can gate a deploy on — they are the test suite that makes every prompt, model, and pipeline change safe to ship.
LLM evalsLLM observability: what to instrument in production
LLM observability is the practice of recording enough about every model call that you can answer, after the fact, what it cost, how long it took, whether the output was any good, and what the model was actually shown. Standard APM answers none of those, because a call that returns HTTP 200 in 900ms can still be a complete failure.
LLM evalsWhat is RAG (retrieval-augmented generation)?
RAG retrieves the passages relevant to a question and puts them in the prompt, so the model answers from your data with citations instead of relying on what it happened to memorise during training.
Let’s scope it on a call
Thirty minutes with an engineer. We’ll tell you straight whether this is the right first move for your team.