Private AI, built for one job
We don't sell access to a big general model. We build a compact AI system for the one or two tasks that matter to you, starting from the task itself, not from a vendor's API, and hand it to you. It runs on an ordinary server inside your network, with no internet access and no per-token bill.
Every team wants what GPT-class models can do. Most can't send their data to one.
Contracts, patient records, KYC files, source code, internal tickets, legal, compliance or the client simply says no to a third-party API.
A classifier that runs on every document, every day, turns a cheap experiment into a six-figure line item, and the price is set by someone else.
You don't need a model that writes poetry. You need one that routes a ticket or extracts twelve fields from an invoice, correctly, 10,000 times a day.
We start from your task and your success metric, build the data, train a compact model, prove it, and deploy it where your data lives. You keep everything.
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.
A compact open-weights model is trained for your task, and only your task, and measured against the gold set until it clears the bar.
Delivered as a container with an OpenAI-compatible API. Air-gapped, on your hardware or your cloud tenant.
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.
Regulated, data-sensitive, or simply disconnected environments.
KYC document extraction, transaction categorization, complaint routing.
Clinical note structuring, claims triage, PHI redaction, no BAA needed when nothing leaves.
Clause classification, contract field extraction, matter intake.
Air-gapped plants: maintenance log analysis, incident classification, SOP Q&A.
Ticket routing, log triage, SQL generation over your schema, internal docs Q&A.
Citizen request classification, document processing under data-residency rules.
| Frontier cloud API | Purpose-built local model | |
|---|---|---|
| Where data goes | Vendor's servers, another jurisdiction | Stays inside your network |
| Cost model | Per token, forever, vendor-priced | One-time build + hardware 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 |
| Vendor risk | Model deprecations, price changes, ToS changes | Weights are yours; nothing to deprecate |
| Works offline | No | Yes |
| Open-ended chat, coding, reasoning | Excellent | Not the goal, one job, done well |
Two to four weeks. One task. A measurable result before you commit.