The big AI platforms can’t do your most important task. That doesn’t mean it can’t be done.
When ChatGPT and the big platforms are off-limits, too costly, or just wrong for a task you can’t get wrong, a smaller AI, built for that one job and yours to keep, can beat them. We prove it on your data first.
Free · ~2 min · no email to see it.
A specialist beats a brilliant generalist at the one thing it does all day.
You’d rather have the lawyer who reads this kind of contract all day than a brilliant generalist who also does your taxes. AI works the same way. The big platforms are generalists: they know a little about everything. Train a smaller model on your one task, with your data, and it can know that one thing better, and run for a fraction of the cost, because it’s small. It’s why a focused tool beats a general one, and our public benchmark shows it on real tasks.
You have a task that has to be right, and the big platforms aren’t the way to do it.
You’re not allowed to use it
Your data can’t leave your walls, compliance, customer contracts, residency. The API everyone else reaches for is off the table for you.
It’s too expensive at your scale
Per-call pricing is fine in a demo and brutal in production. Run the task at any real volume and the API bill dwarfs the same work on hardware you own, and it climbs with every call instead of flattening.
It’s just wrong too often
On your task, the general model is wrong too often, and you can’t retrain what you don’t own. Prompting only carries you so far.
For teams with one high-stakes task the big platforms can’t do.
- Getting the task wrong is expensive
- You have representative data, or you can capture it
- You don’t have an ML team and don’t want to build one, you want the result handed over to run
- You’re ready to invest once the win is proven on your data
- Renting an API works fine for this, and you’re happy to keep it that way
- You won’t invest even once the win is proven
We work under NDA; your data and architecture stay confidential.
Prove it first. Build second. Hand over everything.
Prove it on your data
Before you commit to a build, we benchmark an owned model head-to-head against your current option (the big platforms, rules, or whatever runs the task today) on a sample of your real data. You get a go/no-go verdict and a reproducible test calibrated to your task. If an owned model won’t win, we tell you, and you’ve spent next to nothing to find out.
Build the solution
You approve scope, target metric, and cost before any build work starts. We build what the benchmark prescribed (a fine-tuned adapter, a distilled model, a trained classifier, a retrieval stack, a composed pipeline) calibrated to your data, not to whatever’s easiest to bill.
Hand over everything
Weights, adapters, the test suite, training recipes, inference code: all yours, outright. No license, no hosted dependency, no call back to us before you ship a change. Your team runs it, and the test keeps proving it holds as models and data move.
No technique to sell you. No lock-in. Just what wins on your data.
We’ve no technique to sell you
Every AI shop has a house specialty (fine-tuning, RAG, a platform) and recommends the thing it sells. We don’t have one. We pick the approach from what wins on your data, because we’ve nothing else to push.
We get paid either way, so we can tell you no
A platform earns when you train; an integrator earns when you build. Our paid step is the proof itself, so “keep your API, an owned model won’t beat it” is a fine result for us to hand you. Nobody who only makes money on the build can afford to say that.
You own it; a platform would rent it back to you
A fine-tuning platform’s business is keeping your model behind their API, on their pricing. We hand the whole thing over to run on your own infrastructure, change without asking us, or take elsewhere. The asset is yours, and we’re not a dependency.
We’re taking a few founding partners, at founding rates.
You work directly with the person doing the build, help shape it, and you’re first in your vertical, at founding rates while we’re early. We let the proof do the talking instead of a logo wall: the public benchmark, plus a paid Pilot that proves it on your data before you commit to a build. In return, if it works out, we’d ask to share the result, anonymised and only with your sign-off, so the next team has the reference you didn’t.
| Step | What you get | Founding rate |
|---|---|---|
| Scan | A quick read on a sample of your data (we take it on only if it’s a fit): hidden-failure check, self-host feasibility, rough cost break-even. | $1,500, credited toward the Pilot |
| Pilot → | A go/no-go verdict plus a re-runnable test you own that proves it, the public benchmark, run on a sample of your task. Yours to keep whatever you decide. | $6–9k |
| Build | Done-for-you: the owned solution the Pilot prescribed (adapter or weights, classifier, retrieval stack, or pipeline) handed over outright. | Scoped from the Pilot, founding-partner terms |
| Assurance | We keep proving it holds: re-run the test as models and data move, catch it when quality slips, extend coverage as new cases appear. | By arrangement |
Start with the cheap Scan to confirm fit; its fee credits toward the Pilot. Founding partners get direct senior attention and preferential Build terms for going first, we’ll be specific on a call.
Common questions
The fit check is free and takes about two minutes, a few quick questions about your task, an instant verdict, no email required to see it. If you’re a candidate, the next step is a low-cost Scan on a sample of your data, and its fee is credited toward the Pilot. You never commit to a build before the win is proven.
That’s one of the main reasons teams come to us. The whole point of an owned model is that it runs on your infrastructure, nothing has to leave your environment. We work under NDA and can operate inside your walls. For compliance and data-residency cases there’s often no big-platform API that’s even permitted; owning the model is the only viable path.
Then we tell you, early and plainly, and you keep the test that proves it. The proof step exists precisely so nobody spends on a build that won’t pay off.
That’s exactly what the test you keep is for. The Pilot proves the win on a sample, but the test is built from your data and your definition of correct, so you re-run it on fresh, messy production data and see whether it still clears the bar, instead of hoping. New edge cases get added to the test; when a model or your data drifts, the test catches it before a customer does. The sample proves it can work; the test you own is how you keep it working. Assurance is the optional step where we keep that current for you.
No, that’s who this is for. You get a runnable artifact (weights or adapter, a classifier, or a pipeline) plus the test and recipes, handed over so your existing engineers can deploy and re-run it. No ML hires required. If you’d rather we keep it current as models and data move, that’s the optional Assurance step.
Your team, on your infrastructure. That’s the point of owning it. Everything is handed over so any competent engineer can deploy and re-run it: the model, the test that proves it, the recipes, and the inference code, documented. Because you own the lot and nothing is locked behind us, you’re never dependent on one person or vendor staying available; you can run it, fix it, or hand it to another team. And if you’d rather not carry it, the optional Assurance step keeps us on the hook to watch for drift and extend coverage as your data shifts.
Everything. Weights and adapters, the test suite, training recipes, inference code. No ongoing license, no hosted dependency, no permission needed before you ship a change. It’s yours to run, modify, or take elsewhere.
Two ways. First, what a provider hands back is a training loss curve, not proof the result is better on your task, we ship a test calibrated to your data that answers exactly that. Second, you don’t own the result: it stays behind their API, on their pricing, subject to their changes. Here the model and the proof are yours outright.
Those platforms are self-serve: you bring the ML judgment, they run the fine-tune, and the result usually lives on their infrastructure and pricing. Three differences. Proof before build (we benchmark whether an owned model wins on your task at all, before anyone trains anything. Ownership) the weights, adapters, eval, and inference code are yours to run on your own infrastructure, which is the whole point when data can’t leave your walls; a hosted platform is the opposite. The judgment (deciding what “correct” means, picking the approach (a classifier, retrieval, a decoder with adapters, or a pipeline) not just fine-tuning because that’s the product), and building the eval that proves it. A platform plus an eval still needs someone to make those calls; that’s what we supply.
It helps, and for some approaches it’s decisive, but “representative data exists or can be captured” is enough to start. The fit check asks what you have and tells you straight whether it’s sufficient, or what to capture first. If the data isn’t there yet, we’ll say so rather than take the work.
Find out if an owned model wins on your task.
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