AI that runs where cloud can't go
adapterOS is a deterministic multi-LoRA runtime for defense, aerospace, and other regulated environments where sending data to cloud AI services is not an option.
- Prove what your AI computed — with cryptographic receipts, not just logs
- Pass audits faster — every run produces exportable, hash-linked evidence
- Deploy without calling home — no cloud, no telemetry, no vendor access required
The company
MLNavigator builds adapterOS. The business model is runtime licensing plus deployment services — integration engineering, compliance mapping, and long-term support.
MLNavigator Inc. is a Delaware C-corporation. adapterOS is the core product and IP layer.
Our Research Group is an internal R&D lab that publishes specifications and methodology notes. Everything it produces feeds directly into the product.
The product
adapterOS sits between the model layer and your application. It replaces ad-hoc inference scripts with a governed runtime that manages models, enforces execution policy, and produces verifiable evidence of what ran.
It handles model and adapter lifecycle, enforces deterministic execution within a defined scope, and generates execution receipts for every run. Receipts prove provenance — which model, which adapter, which configuration, which inputs produced which outputs. They do not claim the output is correct. That distinction is deliberate.
→ Full breakdown: MLNavigator and adapterOS
Why this exists
What you get
Reproducible inference on your hardware. Audit-ready evidence exports with hash-linked receipts. A runtime your compliance team can document and your auditors can verify — without network access, vendor callbacks, or cloud dependencies.
Why offline matters
ITAR facilities cannot send controlled data to third-party services. Classified networks have no outbound path. Air-gapped manufacturing floors cannot call home. When a compliance deadline or failed audit forces the question — "how do we run AI on this data?" — the answer has to work without the internet.
How it works
adapterOS controls model and adapter state, enforces execution policy, and logs every state-changing operation into a hash-linked chain. Data stays on your hardware. At the end of a run, you export a proof pack — inputs, configuration, hashes, and receipts — ready for review or archive.
The diagram below shows the end-to-end workflow: upload a drawing package, run offline checks, review findings, and export the proof pack.
→ Architecture detail: control, data, and evidence planes
Who it's for
Defense contractors handling CUI under CMMC. Aerospace manufacturers under AS9100. ITAR-controlled facilities where outbound data transfer is prohibited. Air-gapped production floors where cloud AI is not an option. These organizations already have compliance obligations that demand audit evidence — adapterOS is designed to produce it.
Where we start, where we go
First market
On-premises inference for defense and aerospace — offline deployments where cloud AI is prohibited and compliance deadlines are creating budget for AI governance tooling now.
Next markets
The broader defense industrial base as CMMC Level 2+ deadlines hit. Then critical infrastructure, energy, and pharmaceutical manufacturing — anywhere AI outputs need traceability and the network is restricted.
Leadership
James KC Auchterlonie
Engineering and architecture leadership focused on deterministic runtimes, verifiable execution, and offline deployment systems.
Donella D Cohen
Product and commercialization leadership focused on deployment fit, customer engagement, and operational adoption in regulated environments.
Funding + validation
Non-dilutive early funding used to validate deployment assumptions, evidence formats, and integration constraints in regulated environments.
Structured customer discovery and commercialization work focused on regulated operator needs, procurement signals, and offline deployment requirements.
Market learning from defense, aerospace, and compliance stakeholders informed the product scope, evidence model, and rollout priorities.
What we heard in customer discovery
Anonymized interview quotes are grouped by recurring themes from operators and program stakeholders across primes, OEMs, MROs, suppliers, and compliance teams.
“Our CTOs are investing in sandboxes, but tools still have to be usable by teams.”
— Business development leader, prime contractor
Security controls are necessary, but day-to-day usability decides whether programs actually adopt AI workflows.
“Our IT department is too busy to track AI usage.”
— Engineering director, aviation OEM
Teams need lightweight evidence capture because governance owners cannot manually monitor every AI interaction.
“People want the tech, but they do not trust it yet.”
— Sales stakeholder, aviation OEM
Interest is present, but adoption depends on verifiable outputs that reviewers can inspect and approve.
“We already have an AI policy. Some uses are allowed and others are restricted.”
— Quality analyst, enterprise technology company
Solutions must map to role-based rules and approved use cases rather than relying on broad, one-size-fits-all controls.
“Companies are not ready.”
— Independent CMMC advisor
Most organizations need a staged rollout path with guardrails before scaling AI into compliance-sensitive programs.
“Every part goes through a camera, and when it fails someone has to stop and fix it.”
— Production operator, manufacturing supplier
AI support has to fit existing production checkpoints and exception handling, not require operators to change core workflows.
Talk to us
Interested in offline AI for regulated workflows? Reach out — we're happy to share what we're building and where it's headed.