Private AI
AI that runs on your infrastructure, not someone else’s.
Some work can’t leave the building — client files, case notes, health records, board papers. We build and run open-weight models on hardware you control, so the data stays in your environment.
The problem
The most useful AI is the AI you can point at your real work. But your real work is exactly what you cannot send to a third-party API — the files under privilege, the records under regulation, the papers that have not been announced.
So the tools stay pointed at the safe, low-value edges of the business, and the value sits out of reach.
What we do
We build and run open-weight models on infrastructure you control. Nothing is sent to an outside provider, nothing is trained on what you feed it, and the data residency is something you can point to in an audit.
The models are chosen and tuned for your work rather than for a leaderboard, which usually means smaller, faster and cheaper to run than a frontier API.
How we get you there
You don’t have to buy a GPU to find out whether this works for you.
- 01 Rent
Prove it on rented hardware.
We stand your models up on rented GPUs — RunPod and similar — so you’re running against real work in days, with no procurement cycle and no capital committed.
- 02 Prove
Measure it against real work.
We run it on a workload that matters and measure the result, so the business case rests on evidence rather than a projection.
- 03 Own
Move it in-house.
Once the value is proven we size and specify the hardware and move the whole thing onto machines you own. Same models, same systems, now entirely yours.
What you get
The capability stays with you. The models run on your hardware, against your data, on terms nobody else can change — and they keep running whether or not a vendor revises theirs.
- Your data stays yours
- Nothing leaves your environment. No third-party API, no vendor training on your content, and data residency you can point to in an audit.
- Start without the capital outlay
- We stand the models up on rented GPUs first and prove the value against a real workload, then move you onto owned hardware once the business case is made.
- Sized to the job
- Open-weight models tuned to your work are often smaller, faster and cheaper to run than a frontier API, and they keep running whether or not a vendor changes their terms.
Have work that can’t leave the building?
If the most valuable thing you could point AI at is the thing you cannot send to an API, that is exactly the problem this solves. Start with a conversation about the outcome you need.
Start a conversation