Your own AI model for finance, insurance, legal and professional services
We don't sell access to someone else's model. We train a compact AI model for the one or two tasks that matter to you, whether that is routing requests, extracting data, answering staff questions or generating queries, and install it on an ordinary server inside your network. No internet access, no per-token bill. The model, the data and the code are yours to keep.
What it gets done for the business, not just for IT.
Your own model inside your perimeter has no third party in the data path. Nothing new to disclose, no new subprocessor, no new line in the risk register.
Volume grows, the cost of the model doesn't. One flat fee replaces per-page OCR contracts, per-token API invoices and the next two hires in the back office.
Give teams their own sanctioned model that does the job better than a public chatbot, and they stop pasting client data into one.
Each line is a candidate for a first pilot: one task, one metric, three weeks. Each task gets its own compact model.
Strict JSON into your system of record, with a confidence flag per field.
To the right queue and priority, from a fixed taxonomy, in under a second.
Into your own categories, consistently, at any volume.
Before data reaches analytics, vendors or offshore teams. The model that protects the data is itself local.
With the source paragraph cited, and an honest refusal when the answer isn't there.
In your schema and your naming conventions.
Into your template, with your terminology.
Completeness, consistency, missing fields, wrong dates.
And what we do about it.
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.
Typical first tasks for this segment (routing, classification, extraction from clean PDFs) sit at the lower end of the range. Image-based documents, strict accuracy bars and fully air-gapped builds move it up; see the pricing page for what drives the number.
Same method, different jobs. Pick the page that speaks your language.
Two to four weeks. One task. A measurable result before you commit.