Prove it works. Then keep proving it.
In short
An AI eval is a repeatable test that scores your system's output against known-good answers, the way a test suite scores code. Without one, every model change, prompt tweak and vendor upgrade is an unmeasured risk, and “it seems better” is the only review you can give. We build the labelled dataset, the scoring logic and the CI gate, so you have an accuracy number before launch and an alarm when it drops afterwards.
What you get
What changes when this works.
A number you can take to a risk review
Accuracy on a held-out set, broken down by case type, with the failures listed. That is what turns “the demo was good” into something a governance forum can approve.
Safe model upgrades
When a new model ships, you re-run the suite and see the delta in minutes instead of discovering it in production three weeks later.
Regression gates in CI
Evals run on every change to prompts, retrieval or tools. A change that drops accuracy fails the build like any other broken test.
Failure modes made legible
The harness groups failures by cause, so the next fix is obvious rather than a guess about what to tweak.
Concretely
What we actually hand over.
No deliverable on this list is a document about the work. Each one is the work.
- A labelled evaluation dataset built from your real cases, not synthetic ones
- Scoring: exact-match, rubric-graded, and model-graded where appropriate, each justified
- Regression suite runnable locally and in CI with a pass threshold
- Failure-mode breakdown and a prioritised list of what to fix first
- Observability hooks so production traces feed back into the eval set
- Documentation your team can extend without us
In practice
A team with an AI feature already in production
Our clients’ systems are commercially sensitive, so these stories are anonymised at their request. Metrics are reported as ranges or percentages rather than raw figures.
Problem
An AI feature was live with no measurement. Every prompt change was argued about rather than tested, and nobody could say whether the last model upgrade had helped or hurt.
Action
We built a labelled eval set from real production cases, added rubric scoring for the subjective outputs, and gated the deployment pipeline on it.
Result
Accuracy went from unmeasured to [X]% baseline; a subsequent model upgrade was validated in [X] minutes instead of guessed at.
Questions
AI evals, answered plainly.
What is an AI eval?
An eval is a repeatable test that scores an AI system's outputs against known-good answers on a fixed dataset, producing a number you can track over time. It plays the same role for a probabilistic system that a unit-test suite plays for deterministic code: it tells you whether a change made things better or worse, rather than merely different.
How many labelled examples do we need?
Fewer than teams expect. A well-chosen set of 100-200 real cases, weighted towards the edges where the system actually fails, is usually enough to detect meaningful regressions. Ten thousand easy cases tell you far less than 150 hard ones.
Can a model grade another model’s output?
For subjective outputs like summaries, yes, with care. Model-graded scoring needs its own validation against human judgements, or you have simply moved the uncertainty somewhere less visible. We use exact-match and rubric scoring wherever the task allows it, and reach for a model grader only when the output genuinely has no single right answer.
We already shipped without evals. Is it too late?
No, and this is the most common way teams come to us. Production traces are the best possible source of eval cases, because they are real and they include the failures. We build the harness from what your system has already seen.
Related
The other four.
Most projects touch more than one of these. If you're not sure which yours is, that's what the scope call is for.
Start here
Scope your first workflow.
Tell us the process that eats your team’s afternoons. We’ll come back within one business day with a scope, a fixed price, and an honest answer about whether AI is the right tool for it.
- No sales sequence. One reply, from the person who’d build it.
- If we don’t think it’s worth building, we’ll say so.
Email us what the process is, roughly how often it runs, and who does it today. That is enough for us to come back with a scope.
Email usOr write to admin@vintrexlabs.com directly.
Prefer to talk
Book a 20-minute call.
Bring one workflow. We’ll tell you on the call whether it fits in 10 days, and roughly what it costs.
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