Automate your highest-volume task with a specialist model on your servers.
Teams come to us when the big AI platforms cost too much at their volume, make too many mistakes, or can’t touch the data. We automate the task end to end: an open-source model post-trained on your data, the workflow around it, run by your team or by us.
A small open-source model, post-trained for one task, measured against the big-platform API tier a team would deploy:
The same test runs on your task: pull a spec off a datasheet, classify a part or customs line, match a record, check an entry.
See the head-to-head →The narrow, repeatable jobs a specialist model does well.
It takes the routine volume and routes the hard cases to your experts. Whatever else you use the big platforms for stays as it is.
Measure first, then build and run it.
Measure it on your data
Every Build opens with the head-to-head: a specialist model against whatever does the task today (an API, rules, or people), on a sample of your data, inside your environment. You see where it wins and where it fails, backed by a re-runnable test.
Build what measured best
We build the workflow around what won on your data: the post-trained model plus retrieval, rules, guardrails, review routing, and audit logging.
Run it your way
You choose the end state: a full handover (weights, inference code, runbook) run by your team, or we operate it and keep the test current as models and data drift. The weights are yours either way, and you can take the whole thing in-house at any time.
For teams running one high-volume task without an ML bench.
A queue of documents, records, or messages gets processed every day, at a volume where the errors and the bill both add up. The examples to learn from sit in that flow already, or a few weeks of capture would collect them. Nobody on staff post-trains and operates models, and one workflow doesn’t justify the hire. You get the finished workflow, run by your team or by us.
Know a team like this? Send them the head-to-head, or intro us.
First in your vertical, at founding rates.
You work directly with the person doing the build (Philip Stevens, 15 years in applied ML at Agoda and Quantcast). The first customer in each vertical sets the reference case.
| Step | What you get | Founding rate |
|---|---|---|
| Build | One workflow automated end to end: the head-to-head on your data first, then the winning setup deployed with guardrails, review routing, and a runbook. Your team runs it, or we do. | Fixed scope, founding-partner terms |
| Operate | We operate the workflow and keep it above the agreed bar: re-running the test as models and data move, extending coverage as new cases appear. | Monthly retainer |
We’ll be specific about terms on a call.
Common questions
Yes. The model runs on your infrastructure, and so can the head-to-head: in your VPC or on-prem, or on a de-identified sample. For strict residency policies, a self-hosted model is usually what passes review.
No. The specialist model takes over one task; everything else stays where it is. The test scores anything that attempts the task, so unsure items can route to a platform model or a person.
Then the numbers show it, with the reason. The engagement ends before build spending starts, and you keep the model.
The sequence and the aftercare. The build is measured before it starts. After delivery the workflow is kept above the agreed bar, by us or by your team with the runbook. The method is public; your integrator is welcome to check it.
The review artifacts come with the work: Apache 2.0 or MIT base models (usage-capped or revocable licenses are screened out), every file revision-pinned and hash-verified, work under NDA, and for data that can’t leave, inside your environment.
That’s who this is for. One GPU covers most volumes, and your engineers get the runbook and inference code at handover; or we operate it for you. Either way, the test flags the day quality moves.
Weights and adapters, inference code, and the runbook: yours to run, modify, or hand on. Run it yourselves and the recurring cost is your hardware, with the model version pinned until you change it; or we run it for you on a monthly retainer.
“Representative data exists or can be captured” is enough to start. The fit check tells you whether what you have is sufficient; if it isn’t, you get a capture checklist to close the gap.
See what automating your task would take.
Prefer to talk it through? Book a free call →