Patient data stays on premises.
Small models for clinical transcription support, document extraction, and coding assistance — running on local hardware, so no patient record ever reaches a third-party API.
Healthcare AI on premises means a 3–7B model handles transcription cleanup and structured extraction on hardware in your facility. No patient record reaches a third-party API, so the data-flow question disappears instead of being papered over with contracts. Published migrations report 66–80% lower inference costs on routine workloads (Forethought, on AWS).
Healthcare has the strongest privacy constraint of any vertical we work with, and some of the heaviest documentation burden. Visit transcription, referral letters, discharge summaries, prior-authorization paperwork — narrow language tasks, done at volume, on data that has no business leaving the building. Cloud AI vendors answer this with contracts. On-premises small models answer it with architecture: a 3–7B model handles transcription cleanup and structured extraction on hardware in your facility, and the data-flow question disappears instead of being papered over.
We'll be direct about where we stand: healthcare deployments carry validation and integration weight that makes them the most capital-intensive builds we scope, and we haven't shipped one yet. This page exists because the pattern fits and the published economics are strong. If you're a clinic, practice group, or health-tech company with this problem, talk to us — early conversations shape whether this becomes a focus vertical, and early clients get disproportionate attention. That's the honest trade.
Documentation, handled locally.
In the US, sending protected health information to an AI vendor means business associate agreements and a longer list of disclosures to manage; in the UK and EU, health data is special-category data under GDPR, with NHS-connected organizations carrying their own data-security expectations. We're engineers, not your compliance advisers — and healthcare demands more compliance advice than any other vertical on this site. What an on-premises deployment offers is a smaller problem to take to your advisers: no new external data flow, no new vendor in the record's chain of custody.
We haven't shipped this vertical, and this page says so in plain text. Here is the published evidence the economics hold — and here is how we'd prove it on de-identified samples, against a frozen eval set, before any commitment.
| Published result | Source |
|---|---|
| Inference costs fell 66–80% after moving routine workloads to fine-tuned small models | Forethought, published on AWS |
| Task accuracy rose from 81% to 93% while inference costs fell 50–68% | distil labs × Knowunity, vendor-reported |
| Serving costs fell roughly 50% on dedicated small-model infrastructure | distil labs on Cerebrium, vendor-reported |
| Task-specific small models to see 3× the adoption of general-purpose LLMs by 2027 | Gartner forecast |
Have you deployed in a clinical setting?
No — and this page says so plainly. Healthcare deployments carry validation and integration weight that makes them the most capital-intensive builds we scope. What we offer today: the published evidence the economics hold, and a proving path on de-identified samples, against a frozen eval set, before any commitment.
What hardware does an on-premises model need?
A 3–7B model runs on a single GPU server — facility-grade hardware, not a data center. The Crit scopes the exact configuration for your document volume, and the deployment lives behind your existing security controls.
Why list healthcare if you haven't shipped it?
Because the pattern fits and the published economics are strong, and we would rather say where we stand than imply experience we don't have. Early conversations shape whether this becomes a focus vertical, and early clients get disproportionate attention. That's the honest trade.
Nothing leaves the building.
- Audio never reaches a vendor
- Records stay on premises
- Clinicians control the data
- DPO-ready documentation
We’re engineers, not your compliance advisers — the architecture keeps your existing compliance intact instead of adding a vendor to it.
See if the numbers hold in your facility.
The Crit is a teardown of your AI spend and workflows: where a small model wins, where an API is fine, and where AI shouldn't be used at all. You get the numbers either way.
If your workload doesn't clear roughly 50M tokens a month and you have no privacy constraint, a frontier API is probably fine — and we'll tell you so.