Ship AI you can actually measure.
Most teams put language models into production with no way to tell whether yesterday's change made the system better or worse. We build the eval suites that answer that question — grounded in your traffic, not a public benchmark.
Why AI features fail after the demo
The demo works. That is the trap. A language-model feature that performs beautifully on the twelve examples someone tried by hand will fail in ways nobody anticipated on the ten thousand that arrive next week — and without evaluation, the first person to notice is a customer.
The specific failure is not that teams do not test. It is that the tests they have do not transfer. Public benchmarks measure general capability on tasks that are not yours. Thumbs-up/thumbs-down feedback is too sparse and too biased to steer on. And a suite of unit tests over deterministic code says nothing about a component whose output is a probability distribution over sentences.
So changes get shipped on intuition. A prompt gets tweaked, a model version bumps, a retrieval step gets added, and no one can say with evidence whether the system improved. That is not a tooling problem. It is a measurement-design problem, and it is the one we solve.
What an eval suite actually contains
An eval suite is not a dashboard and it is not a score. It is a body of measurement designed against the specific decisions your system makes. What we build typically includes:
Grounded in your traffic
Cases mined from real production logs and real failure reports, stratified by the dimensions that actually vary — intent, difficulty, document type, user segment, edge conditions. Public benchmarks measure someone else's problem.
Criteria, not vibes
Explicit, written criteria for what a good output is on this task, calibrated against human labels on a held-out set. If two reviewers disagree on what passes, the rubric is not finished.
Where determinism is possible
Schema validity, citation grounding, tool-call correctness, refusal behaviour, PII leakage, format compliance, latency and cost budgets. These are cheap, exact, and catch a surprising share of real regressions.
LLM-as-judge, done carefully
Judges are useful and they are also biased — toward longer answers, toward their own outputs, toward whichever candidate is shown first. We control for position, calibrate against human agreement, and re-validate when the judge model changes underneath us.
In CI, not in a notebook
The suite runs on every meaningful change and blocks the ones that make things worse. An eval that lives in someone's local notebook stops being run within a month.
Online, sampled, continuous
Offline suites catch what you thought to test for. Sampled scoring of live traffic catches the rest, and gives you a drift signal when a provider silently changes a model you depend on.
Offline, online, and the difference that matters
These are different instruments and teams routinely buy one while needing the other.
Runs against a fixed set before you ship. Fast, repeatable, and the only thing that can gate a deploy. Its blind spot is that it only measures what you thought to put in the set.
Scores a sample of real production traffic continuously. Catches distribution shift, novel misuse, and silent provider-side model changes. Its blind spot is that it tells you after the fact.
Answers the narrow, critical question: did this change break something that used to work? Cheap to run, and the single highest-value thing most teams are missing.
Prompt injection, jailbreak attempts, harmful-request handling, and the specific abuse patterns your product invites. Sized to the consequence of getting it wrong, per our doctrine.
Signals you need this now
If more than one of these is true, evaluation is the highest-leverage thing you are not doing.
- A model or provider upgrade is coming and nobody can predict what it will break.
- Someone changed a prompt last week and the team is still arguing about whether it helped.
- Quality problems are reaching customers before they reach your dashboards.
- Procurement, legal, or a regulated customer is asking for evidence that the system performs as claimed.
- You are choosing between models or vendors and have no defensible way to compare them on your own workload.
- An agent or multi-step pipeline fails intermittently and no one can localize which step is at fault.
What we deliver
We work with your team to define what good means for your task, build the first test sets from your data, and write rubrics that survive contact with disagreement.
We build the runner, the CI integration, and the reporting — in your stack, so your engineers own it after we leave.
Sampling, online scoring, drift detection, and alerting on the metrics that actually correspond to user harm rather than the ones that are easy to compute.
The deliverable is a suite your team runs and extends, plus the documented reasoning behind every measurement choice. We are not trying to become a dependency.
We are tool-agnostic. If you already run Braintrust, Langfuse, Arize, Phoenix, DeepEval, or a homegrown harness, we work in it. If you have nothing, we will recommend based on your stack and constraints rather than on what we prefer. The suite is the asset; the platform is replaceable — which is the same inference-independent stance we take everywhere else.
Questions we get asked
- ?
What is an AI eval suite?
A structured body of tests that measures whether an AI system produces acceptable output on the tasks it actually performs. It typically combines curated test sets drawn from real usage, explicit grading rubrics, deterministic checks such as schema and citation validity, model-graded scoring calibrated against human labels, and a runner that executes all of it automatically on every change.
- ?
How is evaluating an LLM different from normal software testing?
Conventional tests assert exact outputs. A language model produces a distribution over possible outputs, so the same input can yield different valid answers. Evaluation therefore measures quality against criteria rather than equality against a fixed string, and reports rates and distributions rather than pass or fail on a single run.
- ?
Is LLM-as-judge reliable?
It is useful and it is biased. Judge models favour longer responses, favour outputs from their own model family, and are sensitive to the order candidates are presented in. Those biases are manageable — randomize position, calibrate the judge against human labels on a held-out set, measure agreement, and re-validate whenever the judge model changes. A judge that has never been calibrated against human judgment is a number, not a measurement.
- ?
How long does it take to stand up an eval suite?
For a single well-defined feature, an initial suite with real test sets and CI integration is typically a matter of weeks. Broad multi-surface systems take longer, mostly because defining what good means across several tasks is the slow part. Our doctrine caps this: discovery must never become billable archaeology.
- ?
Do we need evals if we are only using an API?
Especially then. Calling a hosted model means the thing your product depends on can change without notice, without a version bump you control, and without any obligation to preserve behaviour you rely on. Evaluation is how you detect that.
- ?
What if evaluation shows we should not ship the feature?
Then that is the finding, and you have it before your customers do. Our commercial model is arranged so we are never financially punished for reporting an inconvenient result — see our operating doctrine.
Not sure whether you need evaluation or a rebuild?
That is a fair question and the first conversation is free. If the honest answer is that your problem is upstream of evaluation, we will tell you that.