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A research company building the missing instrument.

MLNavigator researches how AI can answer from facts on paper, show the source, and run without outside help — and builds adapterOS as the instrument that does it.

Why now: compliance pressure and facility boundaries are pushing document work local. We make that work safer to try, priced by scope, and easier to defend internally.

Delaware C-corporation. Pilot-ready. Patent applications filed. Product depth: adapteros.com.

Why this company exists

Teams with sensitive documents want the speed of AI without copying contracts, reports, tickets, policies, or technical records into tools they cannot inspect.

The bottleneck is not only model access. It is whether the work stays controlled, cites the right sources, and fits the review process people already trust.

We build the local workspace and review layer around that problem. Operators get useful answers; reviewers get the source trail, control result, and context needed to decide whether the output is acceptable.

Leadership

James KC Auchterlonie

Engineering and architecture

jkca.me

Donella D Cohen

Product and operations

donella.info

Validation

ACCEL-KS Proof of Concept Grant

Non-dilutive grant funding supporting deployment tooling and compliance documentation.

Great Plains Regional I-Corps

Structured customer discovery focused on regulated operator needs and offline infrastructure requirements.

50+ discovery interviews

Conversations with security, compliance, and operations stakeholders across defense, aerospace, healthcare, finance, government, and critical infrastructure informed product scope and deployment priorities.

The four principles

Responsibility starts with a narrow boundary: approved sources, local operation, visible review context, and humans deciding whether an output is acceptable.

Truth

Unsupported claims are marked. A source trail shows what an answer depended on, not that it is correct.

Source

Answers stay bound to approved records and cite where they came from.

Autonomy

Data stays local. No outbound calls. No telemetry.

Efficiency

Compute cost measured per run; smaller models when adequate.

How we hold the line →

Talk to the founders

Operators: propose one bounded pilot. Investors: ask for the wedge, traction, and milestone story. We respond within 2 business days.

Funding / PartnershipDiscuss a pilot

Or see how it runs without outside services first →

Start with a fixed-scope field deployment

One workflow, your environment, hardware included — roughly 4–8 weeks from kickoff. Local, offline-capable, and priced by scope — not by the token. You leave with a review record you can show security and compliance, whether or not you proceed.