Services

Private AI implementation

Private AI means the model runs where your data stays: on your hardware or private cloud, with no third-party AI service in the loop. We implement assistants and pipelines on open models, the same approach we run every week to produce this newsletter.

What you get

When this fits

  • Your data cannot be sent to a third-party AI service.
  • You want to know what hardware an open model needs before you buy it.
  • You need logging and guardrail hooks built in, not added later.

How it works

01

Size it

We match open models to the task and to the hardware or private cloud you have.

02

Deploy it where your data is

The assistant or pipeline is installed on your infrastructure and tested without sending your data out.

03

Hand over

You keep the code in your repository, with the guardrail and audit-logging hooks in place.

Common questions

What is private AI?
Models that run on infrastructure you control, so prompts and documents are not passed to a commercial AI provider. Open models make this possible because you can run them yourself.
Can you build and test without our data leaving the building?
Yes. We run mainly on open models on our own hardware, and we can build and test on your infrastructure, so your documents are not handed to a commercial AI API to log or train on.
Do we still need guardrails if the model is private?
Yes. Where the model runs does not change what an agent can reach or what an application can say. That is why the implementation includes hooks for guardrails and audit logging, and why the security review covers private and commercial AI alike.
What does it cost?
It is scoped per project. You receive a one-page written scope with deliverables and timing, and nothing starts until you agree to it.

Related

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