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ReTHINK CNERGY

AI for Sensitive Data

Private Language Model

When you have sensitive data you can’t trust ChatGPT and Claude. Your data is not safe with them.

Request a walkthrough See how it’s installed

Public AI services

  • Customer recordsblocked
  • Patient filesblocked
  • Case documentsblocked
  • Internal correspondenceblocked

Your server, your model

  • Customer recordsstays here
  • Patient filesstays here
  • Case documentsstays here
  • Internal correspondencestays here

the model moves onto your infrastructure — not your data onto someone else’s

Who this is for

Organizations that legally can’t use ChatGPT or Claude

If your data is regulated, “just use a public LLM” was never really an option. This exists for the companies that got left out of that conversation.

  • Banks & lenders

    Compliance

    Underwriting notes, account data, and internal risk models stay inside your own walls, reviewable by your own compliance team.

  • Hospitals & clinics

    HIPAA

    Patient records and clinical notes never touch a third-party API. The model runs on hardware you control, in a facility you control.

  • Government offices

    Sovereignty

    Case files, resident data, and internal memos are queried locally. Nothing is transmitted outside the agency's own network.

How it’s installed

Three steps. One server. No public API calls.

  1. 01

    Install the model on a server in your facility

    It runs on hardware you own or lease, inside your own network. There is no external endpoint and nothing is routed through the public internet.

  2. 02

    Feed it your own data

    Policies, records, contracts, case history — whatever your team actually works with. The model is shaped around that data instead of the entire internet.

  3. 03

    Your team gets a private, focused assistant

    Narrower than a general chatbot, but accurate about the one thing that matters: your institution's own information.

What it actually costs

The honest number, not the sales number

Most vendors sell “private AI” on the promise that it’s cheaper. For most organizations, that’s not the real reason to do it — compliance is. Here’s the range, plainly.

Deployment sizeHardwareCost range
Small, single-purpose assistant1× workstation-class GPU$3K–5K one-time
Department-wide assistant2–4 enterprise GPUs$8K–20K one-time
Ongoing maintenance, any sizeMonitoring, patching, tuning$3K–6K / month

Self-hosting only beats a public API on price at high volume — roughly 35–50 million tokens a month. Below that, the case for going private is compliance and data control, not cost. We’re upfront about that, because it’s the case that actually applies to most of our clients.

Get started

See it running on your own data.

We'll walk through what a private deployment looks like for your organization, and what it would take to stand one up.

Request a walkthrough