Research for better decisions.

Substantive guides to the responsibilities, controls, and choices behind managed AI.

Version 2 · 2026-10-21

What Managed AI Should Include

A managed AI service is not access to a model. It is an ongoing arrangement in which a provider takes responsibility for named AI work: deploying it, connecting it to company information and tools, keeping it running, supporting it and improving it. Managed private AI adds explicit controls over information, access, processing environment and permitted use, and the label alone does not prove those controls exist. Public descriptions of managed private AI from two providers differ widely in what they include, and cloud shared-responsibility guidance shows that some responsibility always stays with the customer [S1][S2][S6][S7]. This paper offers seven areas a scope should address: setup, integrations, access, monitoring, support, improvement and exit. It turns them into a responsibility matrix that you fill in from a provider's written proposal, not from conversation. A blank cell becomes a question to send back before you compare prices. The paper also explains when a simpler subscription is enough. It rests on seven public documents from six organizations, two of them vendor descriptions of their own services. The worked example is invented, and the paper reports no measured results. Dominus Foundry publishes this research and develops Sabina, a commercial interest the reader should weigh.

Version 3 · 2026-10-21

What Makes Business AI Private

Private AI is a label, and the label covers very different arrangements. Two managed private AI providers inspected for this paper describe dedicated, single-tenant, multi-tenant and cloud-hosted options under the same phrase [S6][S7]. This paper helps a business owner look past the label. It separates three kinds of evidence behind any privacy statement: a contractual promise, a technical control, and observed evidence of what actually happened. It follows a single question through a retrieval-based data flow, showing where information is stored, processed, logged and possibly reviewed, and it notes that where data is stored need not be where it is processed [S1]. It compares five common arrangements without ranking them, because each one moves the questions rather than removing them. It treats training use, retention, human review and stored conversation history as four separate questions that documents answer separately [S1][S2]. It closes with a fourteen-question questionnaire and a way to score the answers. The evidence is eight public documents from eight organizations, two of them vendor descriptions. The data flow is a constructed illustration, nothing was tested, and the paper is not legal advice. Dominus Foundry publishes this research and develops Sabina, a commercial interest the reader should weigh.