About
Built for environments where AI must withstand review.
MLNavigator builds governed AI systems for organizations that need local control, policy enforcement, and reviewable evidence.
Our mission
Organizations in regulated and high-assurance environments deserve AI that meets their governance requirements without forcing a choice between capability and control.
adapterOS is our answer: a governed AI workspace that runs inside your facility, operates within defined policies, and produces reviewable evidence for every operation. We handle hardware, installation, and ongoing support so your team can focus on the work.
MLNavigator
MLNavigator is the company behind adapterOS. We focus exclusively on governed AI systems for sensitive environments — organizations where data classification, regulatory compliance, or operational policy requires that AI infrastructure remain on-premises and AI operation produce reviewable evidence.
We have filed patent applications covering core aspects of the adapterOS system. Our work is informed by extensive operator research across defense, healthcare, financial services, and government.
Company facts
| Entity | MLNavigator |
| Founders | James KC Auchterlonie, Donella Cohen |
| Focus | Governed AI for sensitive environments |
| IP | Patent applications filed |
| Status | Available for pilot engagements |
| Support model | Managed deployment with annual maintenance and direct support |
Why adapterOS
- Evidence-producing. Every operation generates audit logs, signed run receipts, and policy enforcement records. The evidence layer is the product.
- Governance-first. RBAC, policy packs, tenant isolation, and change approval built into the operating model, not bolted on.
- Locally controlled. Dedicated hardware inside your facility. No cloud calls, no external data exposure, no third-party model training.
Contact
General inquiries: [email protected]
Security: [email protected]
Privacy: [email protected]
Legal: [email protected]
See how it works
The briefing covers capabilities, evidence model, security approach, and engagement structure.