AI MODEL FIT
You are probably paying frontier prices for a job a cheaper model does better.
Almost nobody tests. A model gets chosen because it was in the news, wired into everything, and never revisited while three generations shipped past it. We build a scored test set out of your actual work and run the candidates against it, so the choice comes from measurement.
A benchmark someone else ran is not evidence about your work.
Public leaderboards measure performance on public problems. Your classification task, with your edge cases and your tolerance for a wrong answer, is not on any leaderboard. A model that leads the charts can lose badly on a narrow real job, and a small cheap one can win it outright, which is the finding that usually pays for the engagement several times over.
So we do the boring thing. We take twenty to a hundred and fifty real examples from your workflow, agree with you what a correct answer looks like, and score every candidate on accuracy, latency and cost per task. Then you get a recommendation with the numbers attached.
How a bench runs
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01
We take your real work
Examples come out of your systems, not out of a sample dataset. Where the right answer is a judgement call, we agree the rubric with the person who currently makes that call.
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02
Everything runs against the same set
Frontier models, mid-tier models, and at the larger sizes open-weight models you could host yourself. Same prompts, same set, same scoring.
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03
You get the harness, not just the answer
From the Bench tier up, the eval harness is yours to keep and re-run. When something new ships you can test it in an afternoon instead of buying another opinion.
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04
Routing, where it pays
Most workloads split. Easy cases go to something cheap and fast, hard ones escalate. We design that split and the fallback behaviour when a provider has a bad afternoon.
What it costs
Priced by how many workflows are in scope and how large the test set needs to be. Published, and not negotiated.
Worth saying plainly
If you are running one light workflow at low volume, a bench will not pay for itself and we will tell you that on the call rather than after the invoice. This work earns its cost when a model is doing something repetitive at volume, or when being wrong has a real price attached.