Offline AI that leaves a receipt.
adapterOS runs AI on sensitive records inside your environment, then shows the sources, policies, system state, and proof record behind each answer.
Use AI on private documents and operational records without sending the work away, while keeping a reviewable record of what happened.
Sensitive records
Local run
Two incident notes and the current policy binder support the summary.
Proof packet
- Data touched
- Actor + workspace
- Policy applied
- System state
- Evidence remains
Sensitive work needs answers that can be reviewed
Many teams can use cloud tools for ordinary work. Sensitive operations are different: records cannot casually leave the environment, and important answers cannot be accepted without a trail.
adapterOS is built for the concrete loop serious buyers ask for: sensitive records, local/offline run, cited answer, proof packet, then review or replay.
The product is not a generic chatbot, developer suite, or training dashboard. Chat can be one surface; the product is reviewable work over private records.
Offline AI becomes useful when it also becomes inspectable.
What adapterOS includes
A managed on-prem/offline AI system for organizations that need cited answers and proof records over sensitive sources.
Local/offline runtime
Run AI over approved records inside the customer-controlled environment, without external AI calls during serving.
Source ingestion
Prepare private documents, operational records, and policy sources so answers can cite the materials that support them.
Cited answers
Give operators answers that point back to visible source evidence, with citation strength and limitations surfaced for review.
Proof and audit surfaces
Preserve receipt records, policy records, audit surfaces, and replay readiness for the workflows selected in the pilot.
Proof is separate from the answer
A cited response is useful. A reviewable proof surface is what lets sensitive teams inspect whether the work should be trusted.
Every meaningful workflow is a governed run: object, action, state, and proof surface. Public materials explain the artifacts and review outcomes without exposing proprietary implementation details.
When proof is incomplete, adapterOS should say why instead of pretending an answer is fully proven.
Managed on-prem/offline deployment
Pilot deployments are scoped around a concrete workflow, approved sources, local runtime boundaries, and proof validation.
Offline serving
Sensitive records are served locally/offline according to engagement scope. No external AI calls during serving.
Policy surfaces
Policies describe egress, evidence, isolation, retention, compliance, and incident handling at a buyer-safe level.
Reviewable operation
Citations, receipt records, proof packets, and audit surfaces are planned into the workflow from the start.
The pilot loop is upload, process, ask, cite, prove, inspect or replay. MLNavigator handles deployment support while the customer controls the operational environment.
Internal baseline measurements may be discussed during qualified briefings, but public claims stay artifact-level: local runtime, cited answers, policy records, proof packets, and review outcomes.
Pilot is validation, not a casual app trial
A pilot selects one sensitive workflow and proves the operational loop under controlled conditions.
Briefing
Select the workflow
We cover the product loop, sensitive-source boundary, security review, and what proof must demonstrate.
Install + validate
Prove the loop
Controlled installation, source validation, evidence/proof validation, security review, and operator feedback.
Expansion
Decide what comes next
The expansion decision is based on fit, proof quality, operational value, and deployment requirements.
Commercial details, support, and pricing are covered during the briefing. Engagements are structured around pilot validation, deployment setup, and ongoing support.
Common questions
What does adapterOS do?
adapterOS lets teams use AI on private documents and operational records without sending the work away, while keeping citations and proof records for review.
What runs locally?
The sensitive-record workflow runs inside the deployment environment according to engagement scope: source ingestion, local/offline serving, grounded questions, cited answers, and proof capture.
Does customer data leave the environment?
The product is designed for on-prem/offline deployments where customer operational records stay under customer control. The public website collects inquiry and briefing data only.
What is a receipt?
A receipt is a review record for an AI operation. It helps show what evidence, policy, and system context were involved so the answer can be inspected later.
What does proof mean here?
Proof means the answer is paired with reviewable metadata: what data was touched, who acted, what policy applied, what system or focus ran, and what evidence remains.
What happens when proof is incomplete?
adapterOS should label the limitation instead of pretending the answer is fully proven. Reviewers can see whether an answer is receipt-bound, evidence-backed, approximate, or degraded.
What does a pilot include?
A pilot includes briefing, workflow selection, controlled installation, source validation, evidence and proof validation, security review, operator feedback, and an expansion decision.
Is adapterOS a chatbot?
No. Chat is one operator surface. The product is a governed workspace where sensitive records are processed, cited, reviewed, and proven.
Is adapterOS a training dashboard?
No. adapterOS is not a generic training dashboard or developer suite. It is a managed offline AI system for cited work over sensitive records.
Who is MLNavigator?
MLNavigator is the company behind adapterOS. adapterOS is its current product for governed offline AI in sensitive environments.
Get started
Request a briefing
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