We fine-tune and train LLMs on your proprietary data, prove them on your real tasks, and take them to production as smaller, faster specialists that outperform a general-purpose API.
Illustration: training loss falls steadily over the run, and the tuned model scores well above the base model on the held-out evaluation set.
Works with the models and clouds you already use
Why pilots stall
Prompts can describe your formats, codes, and edge cases, but they can't teach them.
Frontier prices on every call make high-volume tasks uneconomic.
Multi-second responses rule out in-product and on-call workflows.
The answer
A specialist model trained on thousands of your own examples learns the task, instead of being told about it on every call.
It answers in your formats, uses your vocabulary, and handles the cases your team cares about, and it's small enough to run wherever you need it.
What we do
Start with the one you need. Each can run on its own or combine with the others.
Results
A frontier-API prompt pipeline was accurate enough to demo but too slow and too expensive at production volume.
Routing accuracy
Before: 71%After: 93%
Cost per 1K tickets
Before: $4.80After: $0.60
Generic models produced fluent summaries that omitted or invented findings, so clinicians rewrote most of them.
Clinician acceptance
Before: 58%After: 86%
Unsupported findings per 100 notes
Before: 7.1After: 0.9
Ownership
Trained adapters and checkpoints are assigned to you in the contract. There's no licence fee to keep using them.
We never reuse your data for other clients or for our own models.
Train and serve inside your cloud account or data centre, not ours.
All copies we hold are deleted at project close, and we confirm the deletion in writing.
Compliance programme:
Industries
FAQ
Less than most teams expect. A few hundred reviewed examples are enough to start, and a few thousand usually carry a first model. If you have logs, tickets, or documents, we can usually mine the examples from those.
If the model mainly needs facts that change often, retrieval is usually enough. If it needs a consistent format, tone, or domain judgement, or you need lower cost and latency, fine-tuning wins. Many production systems use both.
Most company data is. Cleaning, de-duplicating, and labelling it is part of the work, and you get a data-quality report before any training starts.
No. Mid-size companies with one clear, high-volume task are often the best fit, because the savings show up fastest.
Yes. Many engagements are co-builds: your engineers pair with ours, review every pull request, and own the pipeline once it's handed over.
Getting started
Find out whether a custom model is worth it before you commit to a build. Larger engagements can be paid in monthly installments.
