"It seems good" is the only quality signal most teams have. It is also the signal that quietly degrades when a provider updates a model, your product changes, or the questions people ask move on. Evaluation replaces the impression with a number you can act on.
From$8,000
Evaluation set from your real cases, scoring harness, measured baseline and CI integration.
Indicative starting price. The fixed fee for your scope is quoted after the two-day diagnosis, before any build begins.
What makes it work
Public benchmark scores tell you nothing about whether a system handles your customers, your terminology and your edge cases. The set we build comes out of your own history, including the awkward examples people remember.
Evaluations run in your pipeline on each release, so a prompt edit or a model change produces a reported delta instead of a hopeful deployment. Quality stops being a matter of opinion in a standup.
Ambiguous inputs, contradictory instructions, out-of-scope requests, adversarial phrasing and the long tail nobody wrote a test for. Better we find the failure than a customer, or a screenshot on social media.
Capabilities
A held-out set drawn from your real cases, labelled with the correct outcome and weighted toward what actually matters.
Automated scoring with model-as-judge where appropriate, calibrated against human ratings so the score means something.
Wired into your pipeline so every change is measured before it ships, not after.
Structured attempts to induce wrong, unsafe or out-of-scope behaviour through input alone. This is behavioural testing, not a security assessment - formal penetration testing is a separate discipline and we will refer you.
Accuracy broken down by language, customer type, channel or region, because a strong average routinely hides a weak segment.
An agreed bar for go and no-go, so launch decisions stop being a judgement call under deadline pressure.
In detail
A category name is not a scope. These are the individual pieces of work inside this practice - take the two that apply to you and ignore the rest.
A held-out set drawn from your real historical cases, labelled with the correct outcome and weighted toward what actually matters commercially.
Defining what "correct" means for your task, which is harder and more valuable than it sounds when outputs are free text rather than a label.
The code that runs the set and produces a score, using model-as-judge where appropriate and calibrated against human ratings so the number is trustworthy.
A workflow for the cases automated scoring cannot settle, with inter-rater agreement measured rather than assumed.
Evaluations wired into your pipeline so every prompt edit, model change and dependency upgrade produces a reported delta before it ships.
Structured attempts to induce wrong, unsafe or out-of-scope behaviour through input alone. Behavioural testing, not a security assessment; formal penetration testing is a separate discipline.
Accuracy broken down by language, customer type, channel or region, because a healthy average routinely conceals one badly served group.
Checking that when the system reports 80% confidence it is right about 80% of the time, without which any routing threshold is arbitrary.
Running the same evaluation across candidate models so a switch is decided on measured quality and cost rather than a launch announcement.
A written bar for go and no-go, agreed in advance, so launch decisions are not made on instinct under deadline pressure.
What you receive
Around three weeks end to end. That comes from scoping tightly to one workflow - not from skipping a phase. Each still ends in evidence you can check.
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Further reading
Let's talk
Point us at a system that is live and we will build an evaluation set from your real cases and tell you how it currently performs. Most teams have never had that number. It is usually the most useful thing they learn all quarter.
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