Capabilities, Not Products
Purpose-built sovereign AI for organizations where mission failure is not an option.
We use "capabilities" and "solutions" language, not "products." Defense buyers procure capability.
Edge AI Resilience
- Use Case: Autonomous intelligence at the tactical edge where cloud reach-back is unavailable or prohibited
- Constraints: No connectivity, limited power, contested RF environment
- Approach: AriaOS Context Kernel maintains deterministic state through agent crashes, memory pressure, and network partitions
- Deliverables: Validated edge deployment package, benchmark data, integration documentation
- Integration: Hardware-agnostic (x86, ARM, Apple Silicon, NVIDIA, AMD)
- Security: Zero external dependencies, sovereign compute, full audit trail
DDIL Orchestration
- Use Case: Multi-agent coordination across distributed nodes with intermittent or denied connectivity
- Constraints: Disconnected, degraded, intermittent, limited (DDIL)
- Approach: Supervisor-authorized agent spawning, shared memory bus, role-based execution within governance boundaries
- Deliverables: Mesh orchestration framework, recovery time benchmarks, federation protocol documentation
- Integration: Peer-to-peer mesh networking, no centralized controller
- Security: Operator-controlled mesh admission, encrypted inter-node comms
On-Device Governance and Audit
- Use Case: Ensuring all AI actions comply with operational and legal policy before execution, without external validation
- Constraints: No cloud policy server, no reach-back for authorization
- Approach: Pre-LLM compliance layer enforces policy at kernel level before model inference. Unsafe operations blocked before execution.
- Deliverables: Governance framework, audit trail specification, compliance verification toolkit
- Integration: Policy injection via operator-defined rule sets
- Security: All governance enforced locally, tamper-evident audit logs
Federated Updates Without Cloud
- Use Case: Model improvement across distributed sovereign nodes without centralizing data or requiring persistent connectivity
- Constraints: Data sovereignty, intermittent comms, heterogeneous hardware
- Approach: Federated learning with convergence guarantees under sovereign constraints, privacy-preserving aggregation
- Deliverables: Federation protocol, convergence benchmarks, privacy bound analysis
- Integration: Opportunistic sync during connectivity windows
- Security: Data never leaves node, differential privacy, secure aggregation
Ruggedized Field Inference
- Use Case: AI inference on field-deployable hardware under thermal, power, and physical stress
- Constraints: Extreme temperatures, vibration, power instability, dust/moisture
- Approach: Adaptive resource management with thermal-aware scheduling, graceful degradation under power constraints
- Deliverables: Hardware validation report, thermal profiles, power consumption curves, recommended configurations
- Integration: Standard deployment on validated hardware platforms
- Security: No wireless emission requirements, offline-first by default