Private AI systems for work that cannot leave your boundary.
MLNavigator builds local AI workspaces and offline inference systems for sensitive work. adapterOS gives teams cited answers, policy results, and replayable review records without routine data egress.
- MSA v4 sec. 8.2
- Addendum p.3
- Policy sec. 11
- Policy
- Human approval required
- Record
- Saved locally for replay
See the review packet
Use local AI to compare source material, draft findings, and support compliance-adjacent review while keeping evidence attached to the answer.
The boundary is the product.
MLNavigator designs AI systems around local hardware, approved source sets, offline-capable inference, and evidence records. The first requirement is not a better chatbot. It is a controlled operating boundary.
Customer environment
Source docs
adapterOS
Specialist workspace
Review records
No routine data egress
adapterOS is the first workspace.
adapterOS turns approved source material into controlled specialist workspaces. The initial wedge is sensitive document review, but the runtime is built for broader private AI operations.
Privacy
Local boundary first.
Pilot deployments are designed around local hardware and no routine egress for sensitive source material.
Security
Approved sources define the job.
Each workspace is configured around a specific document set, policy boundary, and reviewer path.
Compliance
The answer is not the artifact.
Citations, control results, and reviewer notes remain attached to the work they supported.
Research becomes runtime behavior.
The company layer is private AI systems research and deployment. The product layer turns that work into concrete operating behavior.
Offline inferenceRun sensitive workflows on local hardware without making cloud model upload the default path.
MLX/local model pathsResearch and deployment work focuses on practical local model operations, especially where Apple and edge hardware fit.
Approved source setsWorkspaces are bounded by the material, policies, and reviewer routes that define the job.
Policy-gated answersAnswers carry citations, policy results, and the context a human reviewer needs to decide what happens next.
Replay evidenceExecution records, source links, and reviewer notes stay close enough to make the work inspectable later.
Operator reviewThe runtime is designed to support accountable operation, not to hide judgment behind a conversational surface.
A pilot starts with one bounded workflow.
MLNavigator brings the workspace, install path, and operating discipline around one controlled document workflow before anyone talks about expansion.
01
Pick the workflow
Choose the review, reporting, or compliance task where sensitive documents already slow the team down.
02
Configure the workspace
Load the approved sources, define the reviewer route, and set the boundary for what the specialist can use.
03
Use it with real work
Run questions, comparisons, summaries, and drafts on local hardware with the team that owns the process.
04
Decide with evidence
Measure usefulness, source quality, review fit, deployment burden, and whether the workflow should expand.
- NSF I-Corps
- ACCEL-KS Grant
- 50+ operator interviews
- Patent pending
Start with one review record your team can inspect.
Bring one sensitive workflow, the source boundary, and the review standard it has to satisfy. We will map the pilot around that record.