From exposed to owned, in four steps.
One accountable person, a fixed scope, and a system that belongs to you at the end of it.
Private consult
We map where AI could help most, and where your data can't afford to be public. You leave with a clear, honest recommendation... even if that's "start small."
Design & deploy
I stand up your private AI environment on infrastructure you own, select the right model for your needs and budget, and lock it down.
Connect & build
I securely connect your data and build the first workflows and agents around your highest-value use cases.
Run & evolve
I keep it maintained, secure, and current as the technology moves... so your advantage compounds instead of aging.
Questions leaders ask first
Is a private model actually as good as the public ones?
For the work most businesses need... drafting, synthesis, review, answering from your own documents... open-weight models are now more than capable. Where a frontier model is genuinely required, I'll say so, and we'll scope what can and can't go to it.
Do we need our own hardware?
No. Most engagements run on a private server or private cloud tenancy you own and control. On-prem is an option when policy requires it.
How long does it take?
A first private environment is typically live in weeks, not quarters. Data connection and workflows follow in priority order, so you see value before everything is finished.
What happens if we part ways?
You keep the system. It runs on your infrastructure under your accounts, documented, with no license to revoke. That's the point of owning rather than renting.
Our team already pastes work into public tools. Is that fixable?
Usually, yes... by giving them something better rather than a policy. Shadow AI persists when the sanctioned option is worse. A private assistant that knows your business tends to win on its own merits.