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Offline AI for sensitive operations, with proof built in.

adapterOS is a managed on-prem/offline AI system for organizations that need AI over sensitive records but cannot accept cloud data movement or untraceable answers.

Request a BriefingHow it works

A governed run in plain English

A run starts with approved records, stays inside the local environment, returns a cited answer, and leaves a receipt reviewers can inspect later.

01Source

Private documents and operational records

02Local run

Grounded question answered without external AI calls during serving

03Cited answer

Visible support and citation strength, separate from the audit trail

04Proof packet

Data, actor, workspace, policy, system state, and remaining evidence

05Review

Receipt-bound, evidence-backed, approximate, or degraded status

What is being sold

Local/offline runtime for sensitive documents and operational records inside the customer-controlled environment.
Source ingestion that prepares approved records for grounded questions and reviewable answers.
Cited answers that separate the answer from the proof metadata behind it.
Proof and receipt records for policy, audit, inspection, and replay-readiness reviews.

The operating loop

The product loop is intentionally concrete: upload approved sources, process them locally, ask a grounded question, receive a cited answer, preserve a proof packet, then inspect or replay the work during review.

Records -> run Private documents and operational records are processed by the local runtime.
Answer -> citations The response points back to the sources that support it.
Proof -> replay The proof packet keeps review facts separate from the answer text.

What the proof surface answers

Answers and proof metadata are distinct. The answer is not the audit trail. A reviewable run should help a reviewer answer five practical questions.

  • What data was touched?
  • Who acted, and in what workspace?
  • What policy applied?
  • What system or focus ran?
  • What evidence remains for inspection or replay?

If proof is degraded or incomplete, adapterOS should label the limitation instead of presenting an answer as fully proven.

Deployment model

adapterOS is deployed as a managed on-prem/offline system according to engagement scope. The local runtime is designed so serving sensitive records does not depend on external AI calls.

  • Source ingestion and validation for approved records.
  • Local/offline runtime for serving sensitive workflows.
  • Policy categories covering egress, evidence, isolation, retention, compliance, and incident handling.
  • Pilot deployment and validation with customer review.

Built by MLNavigator

MLNavigator is the company behind adapterOS. It builds governed offline AI systems for sensitive environments, with patent applications filed covering core aspects of the adapterOS system.

Available for pilot engagements. Learn more about our team.

See it in detail

The briefing covers capabilities, evidence model, security approach, and engagement structure.

Request a BriefingSecurity