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Offline Artificial Intelligence | Privacy AI | Provable AI

adapterOS runs locally, enforces policy-scoped determinism, and produces cryptographic receipts teams can verify during audit and incident review.

Air-gap compatible Determinism controls Receipt evidence

Company overview · Compliance roadmap · NSF I-Corps, $25k grant, and 50+ operator interviews

Design goals

Air-gap compatible

Designed to run without outbound network calls, license checks, or telemetry.

Traceable results

Receipts, manifests, and signed artifacts link outputs to inputs and configuration so audits don’t depend on screenshots or “trust us.”

Compliance-ready artifacts

Built for audit surfaces shaped by CMMC 2.0 Level 2 (a common requirement for DoD suppliers), AS9100 (aerospace quality), ITAR (export-controlled technical data), and FAA documentation workflows.

Compliance Roadmap

What the workflow looks like

Non-confidential schematic

A typical run looks like: upload a drawing or document package, run offline checks, review flagged issues, then export an audit-ready proof pack (receipts, configuration, and hashes).

Workflow diagram: upload drawing package, run offline checks, review findings, export proof pack

Problem scope

Regulated operators face audit exposure when AI execution cannot be reproduced or explained. MLNavigator focuses on runtime infrastructure that makes execution traceable, repeatable, and reviewable.

Long-term

Local-first, verifiable AI infrastructure for regulated industries where cloud deployment is not permitted.

Verifiable is not truth

We do not promise the model is right. We aim to show what ran, with what configuration, against what input. That is what you can verify in an audit.

Provenance

Artifacts should trace back to their origin. Model weights, adapters, and runtime are identified where possible.

Manifests

Structured declarations of what should run. Machine-readable. Diffable.

Signed Configs

Configurations can be signed so tampering is detectable.

Hash-Chained Logs

Log entries can reference the previous; deletion or modification becomes detectable.

Energy is a constraint

In transfer-heavy workloads, data movement dominates energy cost. Unified memory architectures can reduce this cost by eliminating copies between CPU and GPU memory. We measure this with Joules per token.

Methodology

We document a measurement methodology for Joules/token benchmarking on Apple silicon.

macOS powermetrics API • 10-run averaging • thermal normalization • documented tolerances

Recent Research Notes

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Feb 2026

MLNavigator and adapterOS

Company and product overview: market need, product scope, validation status, and business model.

Feb 2026

Verification Scope

What adapterOS verification covers, what it does not cover, and where human oversight applies.

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