A test system 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 system that scores every change against a real test set first. A regression gets caught before it reaches a customer.
Catching the regression before a customer does
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. It catches a regression before it reaches a customer, not 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.
A real test set, scored automatically, checked by a human when it matters
The test set is built from your actual use cases: real conversations, real documents, real edge cases your system has already hit. It is not a generic benchmark that may miss 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. It combines 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. That keeps the evaluation honest about its own limits.
Built from your real usage, not a hypothetical one
We build the test set directly from your real usage history and known edge cases. An evaluation system is only as useful as its test set reflects your actual product, not a hypothetical one. Scoring logic gets checked against cases with known correct answers first. Only then do we trust it to catch regressions where the right answer is less obvious. We fold the system into your actual deployment process. Running an evaluation becomes 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. That gives visibility over time, not just a pass or fail on the latest change.
The false sense of safety to avoid
The real risk is a test set that does not actually represent your production traffic. That produces a system that passes everything while real users hit cases it never tested. It is a false sense of safety, worse than no safety net at all. That is why we build the test set from real usage. We keep expanding it as new edge cases surface in production, instead of treating it 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. Those cases get routed to a person on purpose, not 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 $5,500 | 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 stays yours
You own the test set, the scoring logic, the historical results and the full source code. You can run evaluation on every future change with no 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 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 an evaluation system covering your core use cases with automated scoring. One 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 evaluation system and scoring logic. A mix of rule-based checks and a second model acting as judge for cases that need semantic scoring. A dashboard tracks results across model and prompt versions.
Who owns the test set and the evaluation system?
You. The test cases, the scoring logic and the code are yours. 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. It flags the cases that genuinely need human judgment, so your review time goes where it is actually needed.