An ai evaluation audit sounds thorough, and it is. But most teams should start with a pilot: a small graded batch that answers the urgent questions fast. Then go deep with an audit when you know where to look.
We grade outputs. We do not train models, run fine-tuning, or sell experts we cannot verify.
An evaluation pilot is a small, scoped grading run: 20 to 50 samples from one use case, graded against your rubric, back in 2 to 3 business days once scope is confirmed. It answers "is this good enough, and where does it fail?"
A full evaluation audit goes deeper and wider: larger samples across multiple use cases, edge cases, guardrail behavior, and a prioritized fix roadmap. It answers "what is the full state of our AI quality, and what do we fix first?"
The pilot is the reconnaissance; the audit is the full survey. Most teams need the first before the second makes sense. Our pilot guide details how a first engagement is scoped.
The wrong scope wastes money in both directions: too small to learn from, or too big to act on.
Commissioning a full audit with no baseline and no rubric is like ordering a survey of land you have never walked. You get a thick report, your team cannot prioritize it, and the findings sit unread. A pilot first gives the audit something to build on.
A pilot with a handful of hand-picked samples proves nothing except that those samples were fine. Pilots need enough real, representative outputs to surface patterns. Twenty to fifty is the range where patterns start to show.
Skipping the walkthrough call turns the report into a document instead of a decision. The call is where your team asks why a sample got its grade, challenges the reasoning, and leaves with a fix list. It is the highest-value hour of the engagement.
A pilot is diagnostic, not a verdict. Its job is to find the failure patterns while they are cheap to fix, not to certify the model. Teams that treat it as an exam hide the interesting samples; teams that treat it as a diagnosis fix things faster.
Same grading method, different depth. The pilot is how most engagements start.
| What to compare | Evaluation pilot | Full evaluation audit |
|---|---|---|
| Sample size | 20 to 50 samples from one use case. | Larger samples across multiple use cases and edge cases. |
| Turnaround | 2 to 3 business days for a standard pilot. | Longer; scoped per engagement based on breadth. |
| Depth | One use case, headline failure patterns, first fixes. | Cross-cutting patterns, guardrail behavior, regression baselines. |
| What you get | Graded batch, issue summary, recommended fixes, walkthrough call. | Full report, prioritized fix roadmap, stakeholder walkthroughs. |
| Cost shape | Small fixed engagement. The cheapest way to get real grades. | Scoped to breadth. Priced on the work, agreed before we start. |
| Best when | First baseline, pre-launch check, validating a model or prompt change. | Mature product, post-incident review, regulated use case, annual quality check. |
Three questions: is the model good enough for this use case, where does it fail, and what should we fix first? A pilot will not map every edge case, but it reliably finds the headline patterns: the hallucination clusters, the instruction-following gaps, the tone problems.
Pilots also produce your baseline. Every future change gets measured against it, which turns "the new model feels worse" into a number. That baseline alone is worth the engagement.
Breadth and prioritization. An audit covers the use cases a pilot skipped, probes the edge cases and guardrails deliberately, and ranks everything into a fix roadmap your team can work through quarter by quarter.
Audits make sense when the product is mature enough that the fixes need sequencing, when an incident demands a full accounting, or when a regulated use case needs documented diligence. Until then, the pilot usually tells you enough.
Start with the pilot. It is fast, it is focused, and it tells you whether you even need the audit yet. About half the time, the pilot's fix list keeps the team busy for months, and the audit can wait until those fixes land.
When the pilot surfaces smoke in areas it was not designed to cover, that is the signal to scope the audit. You will scope it better for having piloted first: you know which use cases hurt and which questions matter. See a sample report to judge the format before you commit.
You send 20 to 50 model outputs plus your rubric, or we help you write one.
Trained reviewers grade every sample against the rubric, backed by automated checks.
Sample-level grades, issue patterns, recommended fixes, and a walkthrough call.
Twenty to fifty real outputs from the use case you care about. Fewer than that and patterns do not show up; more than that and you are doing an audit. The samples should be representative, not hand-picked: include the boring ones, because boring is where the baseline lives.
Rarely, but it happens: you already have a recent baseline from your own evals, you are doing a post-incident accounting, or a regulated use case needs documented diligence across the board. If none of those apply, the pilot first rule holds.
Yes, and it often does. The pilot finds the patterns; the audit maps them across your other use cases and builds the roadmap. Scoping the audit after the pilot means the audit targets the real problem areas instead of guessing.
That is good news, and the pilot still paid for itself. You now have a documented baseline proving the model was in good shape on that date, which is exactly what you want before the next model update. Keep the report; future you will want it.
Gather 20 to 50 real outputs and whatever quality criteria you have, even if they are rough. If you do not have a rubric yet, we help you write one from your criteria. The less prep you need, the faster the grades come back.
One small batch tells you more than months of debate.
Not sure where to start? Talk to us and we will point you at the right evaluation.