MLNavigator and adapterOS
We build local AI machinery for environments where data movement, review, and operational accountability are material — not optional features.
companyadapterOSgovernanceoffline
Briefings
A structured reading list of research notes from MLNavigator. New here? Start with the short list below.
Read first
Recommended reading order for understanding MLNavigator, adapterOS, and governed offline deployment.
Compliance Roadmap
adapterOS against CMMC, AS9100, and ITAR-shaped deployment constraints — market alignment, not a claim of certified status.
complianceCMMCAS9100ITAR
Offline AI Has No Safety Net: Dependency Control as Architecture
Local inference does not establish local operation. A runtime remains externally dependent when initialization, model loading, telemetry, or update paths require services outside the deployment boundary.
offlinedependenciesarchitectureoperations
Industry insights and roadmap
Industry context, compliance positioning, and deployment planning.
Field observations informing this work:
- "Multiple departments handle compliance, and teams adhere once rules are set." — Program manager, enterprise integrator
- "Unified memory changes on-device AI efficiency." — Senior software engineer, platform ecosystem
- "More members join the AI division every month." — Meetings director, defense association
Architecture, Not Policy, Defines the Surveillance Boundary
Vendor acceptable-use policies can change in a news cycle. Data paths that exist — or do not exist — in the system are harder to rewrite and easier to test.
offlinesurveillancearchitecturegovernanceprivacy
When Procurement Pressure Rewrites AI Policy
The Anthropic–DoD dispute and OpenAI’s follow-on contract show how procurement pressure can revise vendor policy commitments in weeks — not years.
policyprocurementgovernanceenterprisedefense
Multi-Tenant AI Infrastructure Needs Explicit Boundaries, Not Assumptions
Shared GPUs force real isolation questions: who approved the workload, which model and adapter ran, and whether one tenant could affect another. Informal separation is not an answer.
securitymulti-tenantisolationgovernanceoperations
Offline AI Deployment Changes the Threat Model, Not the Need for Discipline
Cutting the network removes some attack paths and elevates others. The result is a different risk profile, not a free reduction in risk.
securityofflinethreat modelCMMCcompliance
When the Same Model Gives Different Answers, Governance Breaks
Same model, same input, same GPU — different outputs across runs. Without treating that as a declared property, audit reconstruction and change validation become unreliable.
governancenondeterminismauditcomplianceinference
Mixture-of-Experts Routing Adds a Second Layer of Inference Variation
MoE models route each token through a subset of experts. That routing can change which parameters ran — and without routing logs, reconstruction fails even when inputs and weights match.
architectureMoEnondeterminisminferencegovernance
Research briefs and public teasers
Public-facing notes on deployment governance, operating constraints, and research direction.
GPU Hardware Variation and What It Means for Repeatable Inference
cuDNN guarantees bit-wise reproducibility only within the same GPU architecture and software stack. Across architectures, that guarantee does not exist.
hardwaredeterminismGPUinferenceoperations
Execution Receipts and the Problem of Citable AI
Citations need stable, retrievable, verifiable sources. Typical AI outputs have none of those properties. An execution receipt binds input, model, config, and output into a signed record — and still does not prove the answer was correct.
provenancecitationsreproducibilityreceiptsacademic integrity
FAQ for evaluators
What should I read first?
Start with the company overview, then the compliance roadmap. Technical readers can continue into the security and deployment boundary briefs.
Are these product claims or research notes?
Both appear here, but the reading list separates orientation from technical briefings so evaluators can distinguish market context, product direction, and implementation evidence.
Where should a pilot customer go after reading?
Use the pilot page to understand scope, duration, and expected artifacts, then contact MLNavigator with one workflow and the review constraints that matter.
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RSS FeedDefine one workflow before deciding whether to evaluate it
Begin with a paid workflow-definition engagement under a scoped agreement. A controlled evaluation is a separate next step only after the workflow, source boundary, reviewers, prerequisites, and measures are agreed. This does not promise implementation, hardware, deployment, or a validated outcome.