A test harness that catches a bad model update,
before your customers do
A prompt change that looks like an improvement in one conversation can quietly break ten others. We build an evaluation harness that scores every change against a real test set first, so a regression gets caught before it reaches a customer.
What it is and who needs it
An AI model evaluation product scores your AI agent or model against a real test set every time something changes, a new prompt, a model upgrade, a new tool, catching a regression before it reaches a customer instead of after. It fits any team running an AI agent or model in production seriously enough that a silent quality drop would actually cost something, lost sales, wrong answers, a damaged brand. It is not worth building for an experimental prototype nobody depends on yet; the value shows up once real users are relying on consistent quality.
What is inside
The test set is built from your actual use cases, real conversations, real documents, real edge cases your system has already encountered, not a generic benchmark that may not reflect what your product actually needs to get right. Automated scoring runs the current model or prompt against every test case and flags anything that newly fails, combining rule-based checks where correctness is objective with a second model acting as judge where correctness is more about quality and tone. A dashboard tracks scores across versions over time, so a slow quality drift is visible before it becomes a real problem, not just a sudden regression. Cases automated scoring cannot judge confidently route to a human reviewer, keeping the evaluation honest about its own limits.
How we build it
We build the test set directly from your real usage history and known edge cases, since an evaluation harness is only as useful as the test set reflects your actual product, not a hypothetical one. Scoring logic gets validated against cases with known correct answers before being trusted to catch regressions on cases where the right answer is less obvious. We integrate the harness into your actual deployment process, so running an evaluation is a step before shipping a change, not a separate tool nobody remembers to open. The dashboard and version tracking get built once the core scoring is proven reliable, giving visibility over time rather than just a pass or fail on the latest change.
What to watch
The real risk is a test set that does not actually represent your production traffic, which produces a harness that passes everything while real users encounter cases it never tested, a false sense of safety that is worse than no safety net at all. This is why the test set is built from real usage and expanded as new edge cases surface in production, not treated as finished after the first version. The other risk is over-trusting automated scoring on cases that genuinely need human judgment, tone, nuance, brand fit, which is why those cases are explicitly routed to a person rather than scored automatically just because a number can technically be produced.
Timeline and price
| Option | Price | What it covers | Timeline |
|---|---|---|---|
| MVP | from $1,800 | Core use-case test set, automated scoring, regression alerts | 4 to 5 weeks |
| Production | from $4,500 | Edge-case coverage, version comparison, quality dashboard over time | 6 to 7 weeks |
| Full control (handover-ready) | from $7,650 | Everything in Production, plus a full handover package: architecture docs, test suite, admin access audit, and a walkthrough so your own team or another vendor can run it without us | 7 to 8 weeks |
Running cost on top of the build is usually $15 to $55 a month in model-as-judge calls, depending on test set size and run frequency.
What you own at the end
You own the test set, the scoring logic, the historical results and the full source code, able to run evaluation on every future change without ongoing dependence on us. The handover package documents how to add new test cases as your product’s real usage evolves.
Related
Pairs with AI agent orchestration platform and custom AI product MVP, both of which benefit from catching a regression before it reaches a trust-level increase or a launch. See the AI agents service page for the full range of agent builds this evaluation layer protects. Real builds: the SENET memory engine case study, evaluated with 18 of 18 intelligence tests passed, and the 11-type content agent case study, tested adversarially before launch. Shipped a prompt change that quietly broke something else? Get in touch.
FAQ
How much does an AI evaluation product cost?
From $1,800 for a test harness covering your core use cases with automated scoring. A harness covering edge cases, regression tracking across versions and a quality dashboard runs $4,500 to $7,500.
How long does it take?
Four to five weeks to build an initial test set from your real use cases and get automated scoring running. Expanding coverage to edge cases and setting up version-over-version tracking takes longer, typically six to eight weeks.
What is the stack?
Python for the harness and scoring logic, a mix of rule-based checks and a second model acting as judge for cases that need semantic scoring, and a dashboard tracking results across model and prompt versions.
Who owns the test set and the harness?
You. The test cases, the scoring logic and the code are yours, so you can run evaluation on every future change without depending on us to check it for you.
Can this replace human review entirely?
No, and it is not built to. It catches the regressions automated scoring is reliable at catching and flags the cases that genuinely need human judgment, so your review time goes to the cases that actually need it.