Technical document retrieval
Making decades of specifications, drawings, test reports, and maintenance manuals queryable — where the knowledge exists but is unfindable in practice.
AI for Defense & Aerospace
AI deployment for controlled and classified environments — including enclaves with no external connectivity at all.
The constraint
The API was never an option
For defense contractors the usual enterprise AI debate does not apply. Controlled Unclassified Information and export-controlled technical data cannot be sent to a commercial API, and in classified enclaves there is no external connectivity to send it over.
That makes private deployment the only starting point, which is clarifying — the architecture question is settled before it is asked. What remains is the engineering: GPU infrastructure inside the boundary, model serving without internet-dependent components, and an update path that works offline.
Air-gapped operation changes the lifecycle, not the capability. Model updates become media transfers under change control. Dependency management cannot assume a package registry. Monitoring cannot phone home. These are solvable, but they must be designed for at the start rather than retrofitted after a connected pilot.
Applications
Where AI creates value in defense programmes
Contract and compliance analysis
Parsing FAR/DFARS clauses, flowdown requirements, and compliance obligations across a large contract portfolio.
Logistics and sustainment
Surfacing parts, failure history, and maintenance procedures at the point of work, including on disconnected maintenance networks.
Proposal and capture support
Retrieving past performance, technical content, and pricing precedent from prior submissions without material leaving the enclave.
ISR and reporting support
Summarization and cross-referencing over structured and unstructured reporting, inside the accreditation boundary.
Compliance
Regulatory landscape
| Framework | What it drives in the architecture |
|---|---|
| CMMC 2.0 | Level 2 practices apply to most CUI handling. AI systems inherit the control set; the assessment evidence has to cover them like any other system. |
| NIST SP 800-171 | The underlying control baseline for CUI in nonfederal systems. Access control, audit, and media protection all bear directly on AI architecture. |
| ITAR / EAR | Export-controlled technical data cannot be exposed to foreign persons or offshore infrastructure — which rules out most commercial AI services outright. |
| FedRAMP High | Where cloud is used at all, the authorization boundary and inheritance model must be established before design, not after. |
We align AI governance to NIST AI RMF and ISO/IEC 42001 alongside your sector-specific obligations.
Integration
Deployment inside the boundary
We deploy on GPU infrastructure inside your accreditation boundary, with model serving that has no internet-dependent components, offline dependency and model update procedures suitable for change control, and monitoring that works without external telemetry. Integration targets PLM systems, technical data repositories, and maintenance and logistics systems on the internal network.
Questions
Frequently asked
Can you work in a fully air-gapped enclave?
Yes. The engineering differences are real — offline model and dependency updates, internal-only observability, no external package resolution — but they are known problems with known solutions when designed for from the start.
What about personnel clearances?
State only what you hold. [CONFIRM CLEARANCE LEVELS HELD BEFORE PUBLISHING]
How do model updates work without connectivity?
Weights and dependencies are staged, integrity-verified, and transferred under your media control procedures, then validated against your evaluation set inside the enclave before promotion. It is a release process rather than a download.
Does CMMC apply to the AI system itself?
Yes, if it processes or stores CUI. It is in scope like any other information system, which is why the control mapping and evidence should be produced during implementation rather than assembled before an assessment.
Related
Private AI
Production AI systems running entirely inside your infrastructure, with no third-party processor in the data path.
Private AIDeployment Models
Managed API, private cloud, on-premise, edge, and air-gapped compared — including where each one fails.
Compare modelsDiscuss your constraints.
The assessment evaluates your data, infrastructure, and regulatory position, then recommends a deployment architecture your review process can actually approve.
We will sign your NDA before a detailed technical discussion.