Private & On-Premise AI

AI that works without your data leaving the building.

For organisations that cannot send customer records to a third-party API — whether because of regulation, a contract clause, or a board that will not sign it off. We build AI that runs on hardware you control, with no outbound dependency at inference time.

Most AI work is a hosted API behind a form, and for plenty of use cases that is exactly right. It stops being right the moment the data is something you are not permitted to send anywhere — patient records, client files, transaction histories, anything under a confidentiality clause.

The usual answer is to abandon the project. The actual answer is that open-weight models have become good enough to run useful work on hardware you own, and the gap to a frontier API is smaller than the compliance risk for a large class of tasks.

We size the hardware against your real workload, choose and fine-tune a model on your data, and build the evaluation harness that proves it is good enough before it goes near production. Nothing leaves your network at inference time.

We know this works because we run it. Our founder's own assistant runs entirely on owned hardware with no cloud dependency — fine-tuned in-house, gated on evaluation, serving from an 8 GB edge device.

You might need this if…

1. Legal or compliance has vetoed a cloud AI tool

2. A client contract forbids processing data outside your systems

3. Per-token costs make the business case fall apart at volume

4. You need it to keep working when the internet does not

What you end up with

Outcome

A model running on hardware you own and control

Outcome

A measured accuracy figure on your own data

Outcome

Hardware sizing with honest running costs

Outcome

No outbound data dependency at inference time

• On-premise model deployment
• Fine-tuning on your own data
• Evaluation harnesses
• Air-gapped & edge inference
• Hardware sizing & cost modelling

The other practices