adapterOS
AI for public-sector records without losing control of the record.
adapterOS runs on procurement packets, program files, case materials, and public-records workflows inside the agency boundary, then leaves legal, IT, and program owners with cited work and a replayable review record.
- FedRAMP / FISMA alignment
- NIST AI RMF
- CJIS-aware workflows
- FOIA / public records review
- State and local data residency
Built for FedRAMP-adjacent governance, CJIS-aware workflows, and public-records review. Patent pending.
The gap is usually operational.
Regulated teams are not short on AI demos. They get stuck when useful output has to fit data boundaries, reviewer habits, change control, and audit expectations. adapterOS closes that gap by running in your boundary and producing evidence by default.
Unclear runtime
If no one can tell what ran, reviewers cannot trust the work record.
Loose change control
Ad-hoc prompts and shifting tools make repeatable review difficult.
Wrong boundary
Cloud upload is often the one thing the workflow cannot do.
What teams can do with it
Read and compare documents
Review contracts, reports, tickets, policies, spreadsheets, and technical records inside one approved workspace.
Ask within a job
Configure the workspace around the dataset, policy boundary, and output shape the reviewer actually needs.
Keep sources attached
Answers include citations, proof tier, and reviewer context so a person can inspect why the response says what it says.
Work locally
Pilot deployments are designed for local hardware, no routine sensitive-data egress, and explicit update handling.
What is real now
Working now
- ✓Source-backed answers with a proof tier on every response: exact, evidence, lineage, or degraded.
- ✓Deterministic replay for any past answer.
- ✓Authoritative token accounting. Estimates are not presented as billable usage.
- ✓Evidence-write durability. If sources are not persisted, the answer downgrades visibly.
- ✓Pilot validation. The latest strict-mode run passed all steps on May 3, 2026.
Pilot evidence
The credible commercial wedge is one workflow, one environment, one deployment scope, and one review path. adapterOS shows when an answer is exact, evidence-backed, lineage-backed, or degraded.
How it gets into the room
Hardware included
MLNavigator ships hardware pre-loaded with adapterOS. No separate procurement cycle just to try the workflow.
Managed install
We install adapterOS in your environment, configure the source set and review path, and validate the workflow against your acceptance criteria.
Operator handoff
Your team operates adapterOS. Ongoing support covers update distribution, review-record retention guidance, and incident response.
Interfaces, inputs, and supply chain
Access
Use adapterOS through a web UI, a REST API, or the command line. Access is gated by JWT or API keys, with RBAC and policy packs defining boundaries before activation.
Inputs
Ingests PDFs, Markdown, and JSONL into an approved source set. No external model training; no data leaves the premises.
Supply chain
Every deployment ships SBOM artifacts for its components and signed run receipts for inference, so security teams can attest to what is installed and what ran.
What adapterOS does not do
- ✕Does not guarantee output correctness. Sources show why an answer was produced. Humans decide if the output is right.
- ✕Does not guarantee model safety or alignment. We govern how document work is scoped, cited, and reviewed. Model quality is upstream.
- ✕Does not make outbound network calls, send telemetry, or auto-update. Updates are explicit and verified.
- ✕Not a cloud document upload service, model marketplace, or open-ended consulting engagement.
- ✕Does not replace reviewer sign-off. adapterOS records evidence for review. It does not make the decision for the team.
Who adapterOS is for
Teams in controlled, disconnected, or export-sensitive environments — where reviewability, change discipline, and deployment control matter as much as model capability.
Defense & aerospace
ITAR-sensitive and CUI document work, CMMC-aligned evidence, air-gapped and classified deployment.
Critical infrastructure
Offline energy, transport, and utility operations that cannot depend on public cloud inference.
Healthcare & life sciences
PHI-bound document work kept on-premises, HIPAA-aligned, with an audit trail for every answer.
Regulated finance
Model-risk-governed AI with books-and-records capture and data residency inside the perimeter.
Government & public sector
Sovereign data and classified networks requiring local control and defensible operating records.
Try it on one real workflow
Fixed-scope pilot. One document workflow. Hardware included. Deployment starts with the selected source set, reviewer path, and acceptance criteria.