Most of your AI workload doesn't need a frontier model.
Crit Studio builds private workflow automation on small models you own. No per-task tax. No data leaving your walls. Accuracy measured on your evals — both numbers, always.
Frontier APIs, credits, per-seat copilots.
Rented AI bills you per task — and the bill scales against you.
Small models.
Critical hits.
Most of what companies send to frontier models is routine, narrow work — classification, extraction, drafting, scoring. A small model fine-tuned on that exact task does it cheaper, keeps the data at home, and is usually more accurate. Frontier models are for the hard 20%. We build the split.
The same is true of automation: most workflows don't need a per-task subscription — they need to be built once, on infrastructure you own. One pattern — evaluate, fine-tune, deploy privately, monitor. The economics don't change by industry.
lower inference cost after moving routine workloads to fine-tuned small models — Forethought, published on AWS
task accuracy after fine-tuning a small model, at 50–68% lower cost — distil labs × Knowunity
Gartner's forecast: task-specific small models adopted three times more than general-purpose LLMs by 2027
systems built and operated by this studio — the same stack we sell, running on our own bills
The first three figures are published third-party results, linked at the source. They're not our claims — they're the pattern we implement, proven elsewhere. The fourth is ours.
One pattern, three places it pays off.
Start with one workflow. End up owning your AI.
Every engagement starts with The Crit. Book The Crit
The workflows we build.
Every use case opens its page. All use cases →
The pattern doesn't change by industry. The data does.
Run your own workload through it.
Drag to your monthly volume. List-price frontier API against a fine-tuned small model with routing — our cost model, public prices, August 2026.
Estimates from published list prices as of August 2026: API classes at ~$0.6 / $6 / $30 per 1M tokens blended in/out (economy, standard, reasoning-grade); self-hosted 7–8B model — one GPU instance ~$700/mo, flat. Chained and agentic pipelines burn 5–30× more tokens per task, so real bills sit above single-call math. Reference: Forethought reports 66–80% lower inference cost after migration (published on AWS). Your workload will differ — that's what the audit measures.
Four stages. One rule: nothing ships until it beats the benchmark.
One small model. Then a workforce.
Every system we ship shares one core: your model, your router, your eval harness. The first deployment is the expensive one — every workflow after it reuses the same infrastructure, the same monitoring, the same lessons. Owned models compound; rented APIs just meter.
Scale is a property of ownership. Rented APIs meter every workflow you add — an owned core amortizes them.
We run what we sell.
Every system here is built and operated by Crit Studio — on real infrastructure, with real bills. We publish the economics and the eval results as we run them.
View the work
Read case
Read case
We won't sell you a frontier model where a small one wins — and we'll tell you, in writing, where it doesn't.
We won't sell you “AI transformation”. We sell working systems with measured accuracy — both numbers, always.
We won't sell you an automation that costs more to run than it saves. The Crit does that math before you spend real money.
Some processes shouldn't get AI at all. When that's the answer, the report says so — that's what makes it an audit.
We can afford to say no. Our own products run on the same stack we sell you.
Your data never leaves. That’s the design.
- Lives in your private cloud
- No third-party AI processors
- Never trains anyone else’s model
- Audit trail by default
You've read the thesis. Test it on your workload.
The Crit tells you exactly what to move to small models, what to automate, and what to leave alone — with the math behind every call. If the numbers don't justify a project, the report says so. That's what makes it an audit.
You talk to the person who ships the system.