Evaluation and observability
Most teams cannot say how accurate their own AI system is, or whether last week's change helped. We build the evaluation suites, CI gates and production tracing that answer both questions.
What is evaluation & observability?
LLM evaluation is the practice of scoring an AI system against a fixed set of cases with known correct outcomes, so that every prompt, model or retrieval change can be measured rather than guessed at. Paired with production observability — traces, quality sampling and cost attribution — it is what stops slow, invisible quality decay. It is also the single practice most often skipped, and the reason teams cannot say how accurate their own system is.
Why teams call us
Almost every engagement starts from one of these. If none of them sound familiar, you probably do not need us yet.
Someone tried a handful of prompts and formed an impression. There is no baseline, no per-category breakdown, and no way to settle a disagreement about whether the system is getting better or worse.
Every quality decision becomes a debate rather than a measurement.
A prompt is tweaked, a model version changes underneath you, a retrieval parameter is adjusted. Each change looks fine on the two examples someone checked. Cumulatively the system is measurably worse than at launch, and nobody can identify which change did it.
Quality decays for months before a customer complaint surfaces it.
A better or cheaper model ships, but with no way to verify it is safe to switch, the team stays on an ageing version — and eventually gets forced off it by a deprecation notice on the vendor's timeline rather than their own.
Leaves cost savings and capability gains on the table for quarters at a time.
Logs capture errors but not quality. Nobody sees that a specific query category has been failing since a document set changed, because it does not throw an exception.
The failures that matter most are the ones that look like successes.
Scope
We build a golden dataset from your real historical cases, adjudicated with someone who knows the domain, and structured by case category so scores stay diagnostic rather than aggregate.
The right scorer per case type, and where a model acts as judge, we validate its agreement with human labels before trusting it.
Evaluation runs on every pull request against a baseline, and a drop beyond threshold blocks the merge — the same discipline you already apply to unit tests.
End-to-end tracing with quality sampling on live traffic, cost and latency attribution per step, and alerting on quality regression rather than only on exceptions.
How it runs
Fixed checkpoints with a named deliverable at each one, so you can stop the engagement at any stage and still own something useful.
We assemble a first evaluation set from your real traffic and measure what your current system actually does, per category.
Expand coverage, implement scorers, and validate any model-as-judge against human labels so the numbers can be trusted.
Evaluation into your pipeline with regression gates, and tracing plus quality sampling into production.
Your team learns to extend the suite, read the dashboards and interpret a regression — because an evaluation suite nobody maintains stops being true within a quarter.
Results
There is one habit that reliably distinguishes teams whose AI systems keep improving from teams whose systems quietly decay: they have a fixed set of cases with known correct answers, and they score against it before every change.
Everything else follows from that. You can compare two prompts. You can evaluate a new model version in an afternoon. You can give a compliance reviewer a number rather than a reassurance. You can detect that a change made things worse before your customers do.
Teams without it are not merely less rigorous — they are structurally unable to improve, because no change can be shown to be an improvement.
Synthetic evaluation cases test the situations you thought of. Your real traffic contains the situations you did not, which is precisely where systems fail.
Support transcripts, resolved tickets, historical documents, completed applications — these are labelled datasets that already exist inside your company. Turning a few hundred of them into an evaluation set takes roughly a week of adjudication with someone who knows the process. Include the awkward ones. Include the ones a human got wrong, labelled with what the right answer was.
Synthetic cases still have a place: they cover rare paths that history is too thin on. They are the supplement, not the foundation.
For open-ended output, scoring usually means asking a model to grade the answer against a rubric. This works, and it is easy to do badly.
An unvalidated judge produces confident numbers that correlate with nothing. Before trusting one, we have humans label a sample, measure the judge’s agreement with those labels, and iterate on the rubric until agreement is high enough to be useful. If the judge disagrees with your experts, the rubric is wrong — and until that is fixed, the score is one model’s opinion of another model’s output.
Evaluation that runs manually gets skipped under deadline pressure. The version that survives contact with a real team runs automatically on every pull request and blocks the merge when a category score drops beyond threshold.
This is not a novel idea — it is exactly how you already treat unit tests. The only reason AI changes are commonly exempt is that the tooling arrived later.
An evaluation suite reflects the traffic that existed when it was built. Six months later, real usage has moved: new question types, changed documents, different user behaviour.
So live traffic gets sampled and scored continuously, and the drift feeds back into the suite. The alerting that matters is on score drift and refusal-rate change, not on exceptions — because the failures that hurt most are the ones that return a perfectly well-formed wrong answer.
Next Step
A 30-minute technical call with an engineer who has shipped this before — not a sales qualification round. You leave with a feasibility read, a rough shape for the build, and an honest answer about whether it is worth doing at all.