For healthcare and health-tech: providers, payers, RCM, medical SaaS
We build a compact AI system for one or two of your paperwork tasks and deploy it inside your environment. No third party in the data path, so no new BAA, no new subprocessor. It runs on an ordinary server, offline, at a flat price.
What it gets done for the business, not just for IT.
Claims, prior-auth and referral paperwork done right the first time, because the model was trained on your payers' forms and rules.
Structuring notes, pulling codes and fields, triaging messages: the parts of documentation nobody went to school for.
On-prem inference means no vendor sees PHI. Security review takes an afternoon, not a quarter.
Each line is a candidate for a first pilot: one task, one metric, three weeks. Each task gets its own compact model.
From clinical notes and discharge summaries into structured fields.
By payer, urgency and department, straight from the scan.
Into the billing system, with a confidence flag per field.
All HIPAA identifiers, before data goes to research, analytics or vendors.
By urgency and department, with escalation rules you define.
With the source cited, and a refusal when it isn't there.
For coder review, never auto-billing.
Consistent structure, your terminology, no invented facts.
And what we do about it.
Beyond the standard delivery.
We start from your task and your success metric, build the data, train your own compact model, prove it on a gold set, and install it on your server. The model, the data and the code stay with you.
One or two narrow tasks with a clear success metric. We write the eval first.
Our engineers collect, clean and label thousands of examples from your real inputs, using the strongest available AI tooling, or fully offline if required.
We take a compact open-weights model and train it for your task, and only your task, on your data. Measured against the gold set until it clears the bar.
Delivered as a container with an OpenAI-compatible API, on your server, offline. We hand over weights, data and pipeline: the model is yours.
Public results, not ours, from teams that replaced a frontier API with a small model built for one job.
Checkr: fine-tuned Llama-3-8B vs GPT-4 on background-check classification. ~$800/mo instead of $7-12K, 0.5 s instead of 15 s.
Predibase "LoRA Land": fine-tuned 7B adapters matched or beat GPT-4 on 25 of 27 tasks, each trained for under $8 of GPU time.
Gartner predicts that by 2027 organizations will use small, task-specific models three times more than general-purpose LLMs.
Sources and details on the FAQ page. Your numbers will differ, that's what the pilot measures.
| Frontier cloud API | Your model on your server | |
|---|---|---|
| Where data goes | Vendor's servers, another jurisdiction | Stays inside your network |
| Cost model | Per token, forever, vendor-priced | One flat fee + a server you already own |
| Latency | Seconds, internet-dependent | Sub-second, on-prem |
| Accuracy on your task | Good, generic | Usually equal or better, it was built for it |
| Who owns it | The vendor; you rent access | You: weights, data, code |
| Vendor risk | Model deprecations, price changes, ToS changes | Nothing to deprecate, no external calls |
| Works offline | No | Yes |
| Open-ended chat, coding, reasoning | Excellent | Not the goal, one job, done well |
From $7,000 to train and deploy your model. Two engineers, data, training, verification, installation on your server, and a follow-up fine-tune one month after go-live. No per-token pricing, no subscription: the model is yours, using it costs nothing extra.
Clinical extraction and PHI redaction usually land in the upper half of the range because of the accuracy bar and the fully private build. Routing and triage tasks are at the lower end.
Same method, different jobs. Pick the page that speaks your language.
Two to four weeks. One task. A measurable result before you commit.