You bring the use case.
We build the model for you.
Your data stays in your hands,
secured.
A solution that fits.
Not AI slop.
You host it anywhere.
We keep nothing.
Small custom models.
Big capabilities.
One day. One range.
A model that fits every hour of your work.
24 hours over one mountain range, in 24 seconds. The light changes every hour; the range stays the same. Your work is like that. We build the model that fits it.
What each line means in practice.
You bring the use case. We build the model for you.
We start from the job, not from a model. Extract these fields. Sort these tickets. Write it the way we write. At intake we define the input, the exact output and the number that says it works. Then we fine-tune a small open model on your examples, on GPU machines we own. You never touch a training script. You correct a few dozen outputs; each correction teaches the model.
Your data stays in your hands, secured.
Your data trains your model and nothing else. It runs alone on isolated hardware, is never pooled or sent to a third-party training service by default, and is deleted when you accept delivery. The weights are yours. Our data commitments.
A solution that fits. Not AI slop.
No generic chatbot with a long prompt. One trained specialist per workflow, scored before and after on examples it never saw. You see the number and the failures before you accept. Nothing ships below the bar we agreed.
You host it anywhere. We keep nothing.
You receive standard weights, an adapter, a quantized copy, the eval report and a deploy recipe. Run it on AWS Bedrock, SageMaker, Cloudera, your own servers or a laptop. Switch hosts without retraining. We do not run it, and we do not keep a copy.
Small custom models. Big capabilities.
0.6 to 9 billion parameters, trained on your task, run on one GPU or a laptop. On the narrow job they are built for, they match frontier models at a fraction of the cost and latency, and they answer in milliseconds, offline if you need it.
From your examples to a model you own, in weeks.
Intake
We confirm training is the right fix, define the output you want, and agree how success is measured.
Data prep
Your emails, tickets, invoices or sheets become training examples. You correct a few dozen outputs. Each correction teaches the model.
Train
We fine-tune a small open model on our own isolated GPU farm. No shared training pool, no third-party training service by default.
Prove
Base model vs. your model, scored on examples it never saw. Nothing ships below the bar we agreed.
Hand over
Weights, adapter, quantized copy, eval report and a deploy recipe for your host. Then we delete your data.
Pick the workflow. We train the specialist.
Invoice and receipt extraction
Photo or PDF in, clean JSON out, every time in your field layout.
Text · SMB & enterpriseSupport ticket triage
Route, tag and prioritise tickets with your own categories, in milliseconds, on one small GPU.
Text · individuals & SMBQuotes and proposals in your house style
Drafts that sound like you, use your terms and follow your structure.
Intake form classification
Sort incoming forms and documents into your workflow queues.
Email reply drafting
A personal model that answers routine mail the way you would.
Contract clause tagging
Find and label the clauses your team reviews, with your taxonomy.
We train. You host. Your data never becomes our asset.
Training runs on hardware we own, not a shared cloud pool. Your data is used for your model only, then deleted on delivery. The weights are yours. Read our data commitments.
Already using our Work Agents?
Parker, Brie and Amelia keep doing their jobs. A custom model makes them yours: trained on your projects, your reports, your policies. See personal models.