Architecture, Not Policy, Defines the Surveillance Boundary
Vendor acceptable-use policies can change overnight. The data paths that exist in the system architecture are harder to alter and easier to verify.
MLNavigator builds governed offline AI infrastructure
designed for regulated, local, and high-assurance environments.
Documented operating policies keep offline AI operations controlled and reviewable.
Reviewable deployment records support audit workflows, incident response, and change control.
Runs without outbound network calls, license servers, or telemetry. Designed for air-gapped and classified networks.
ITAR, CMMC, and air-gapped deployment requirements
Offline operation in energy, transport, and utility systems
Audit trails and model governance for compliance teams
Sovereign data, classified networks, and FedRAMP-aligned controls
Vendor acceptable-use policies can change overnight. The data paths that exist in the system architecture are harder to alter and easier to verify.
The Anthropic-DoD dispute and OpenAI's subsequent contract show how government procurement pressure reshapes AI vendor policy commitments in real time.
cuDNN guarantees reproducibility only within the same GPU architecture and software stack. Across architectures, there are no guarantees at all.
Company and product overview for local AI operations in audit-heavy environments.
High-level positioning around CMMC, AS9100, and ITAR-aligned deployment needs.