Deployments where your content stays inside your environment, isn’t retained by a third party, and isn’t used to train anyone’s model — for the organizations that can’t send data outward.
The Real Problem
A practice holding patient records, a firm holding privileged client material, a school holding student data — each has obligations that make sending content to a consumer AI service straightforwardly non-compliant. The productivity case is just as strong for them as for anyone else, which is precisely why staff use the tools anyway unless a sanctioned option exists.
Private deployment resolves the conflict. The capability arrives; the data stays under your control and inside your obligations.
What’s Included
Self-hosted or tenant-isolated deployment matched to your obligations and your budget.
Clear answers on where content is processed and stored, which is often the first regulatory question.
Configurations and contractual terms confirming your content isn’t used to improve anyone’s model.
Identity-based access with logging, so usage is attributable rather than anonymous.
Connection to your own documentation, so answers come from your material rather than the open internet.
Evidence of controls for auditors and clients who ask how AI is handled.
Common Questions
Often close for practical business tasks, though frontier commercial models still lead on the hardest work. The trade-off is capability against control, and for regulated data control usually wins.
Not necessarily. Tenant-isolated cloud deployment satisfies most obligations without buying infrastructure.
If the honest answer is no, there’s still a way to get the capability.
