Sovereign infrastructure for regulated mobile AI, from agent runtime to device attestation.
Enterprise AI Infrastructure. Post-Quantum Secure. Compliance-Native.
Sovereign AI agents inside your own boundary: on-prem, private cloud, or fully air-gapped. No public-cloud dependency. No compromises.
Agent Execution Pipeline
A full runtime with cost accounting, HITL gates, PII evaluation, and constitutional constraints, not a wrapper around an LLM SDK.
- Core loop state machine: plan → tool-execute → reflect → budget-check → stream
- Constitutional AI constraints enforced in the pipeline, not by prompt
- Token budget gates prevent agents from overrunning cost limits
- Human-in-the-loop checkpoints for high-stakes decisions
- Sub-agent delegation protocol with fan-out and collective reasoning
Context Assembly
Before any LLM call, a 7-provider pipeline assembles context and three post-processing stages filter it. The model sees a curated, policy-filtered window.
- Seven context providers: RAG, graph-RAG, document retrieval, customer data, compliance state, financial context, ontology
- Neo4j ontology-aware reasoning on top of vector RAG for richer context than embeddings alone
- Security filter removes PII and enforces access boundaries
- Prioritizer re-scores by keyword relevance and recency
- Token budget enforcer ensures the assembled context fits model limits
Compliance as Code
24 regulatory frameworks modeled as code with enforcement handlers, evidence collection, and scoring. Compliance is enforced at runtime, not audited after the fact.
- GDPR, HIPAA, SOC 2, NIST 800-171, FedRAMP, ISO 27001, CMMC, and 17 more frameworks
- Each framework has enforcement handlers that block non-compliant actions in real time
- Automated evidence collection tied to every agent action
- POAM tracking and SPRS scoring for continuous compliance posture
- Audit trail with cryptographic integrity: every decision is traceable
Trust & Identity
Three interlocking trust layers: privilege-based access control, self-sovereign identity for agents, and hardware device attestation.
- PBAC (Policy-Based Access Control): privilege checks, never role identity
- SSI (Self-Sovereign Identity): DID-based cryptographic identities for agents, not just humans
- Device attestation via Apple App Attest and Google Play Integrity with server-issued nonces
- Non-human principals are first-class: agents carry verifiable credentials
- Master key material never touches JavaScript on either endpoint
Post-Quantum Cryptography
NIST-standardized algorithms at Level 3 and Level 5 security. Defense-in-depth dual-layer: TLS 1.3 outside, post-quantum AWP inside.
- Level 5: ML-KEM-1024 key exchange and ML-DSA-87 signatures for sovereign and defense workloads
- Level 3: ML-KEM-768 key exchange and ML-DSA-65 signatures for standard enterprise deployments
- XChaCha20-Poly1305 payload encryption for every frame
- Same Rust code on both phone and server, with no JavaScript in the crypto path
- Runs on hardware as small as an ESP32-S3 with 2.1s cold boot
AethyrWire Protocol
Custom binary-framed transport where every session is cryptographically bound to a verified device identity and a tenant boundary.
- 638-byte binary header carrying the sender's identity hypervector
- Every session cryptographically bound to device identity and tenant boundary
- Attestation-gated by Apple App Attest and Google Play Integrity
- Defended against confused-deputy and replay attacks at the auth boundary
- TCP-based with post-quantum encryption per frame, TLS 1.3 outer layer
MCP Integration & Learning Loop
Model Context Protocol as the universal tool bus, plus a built-in learning loop: reviewer feedback becomes training data your organization owns.
- MCP clients (Node, Stdio, External), session manager, tool bridge, and discovery cache
- Every external integration registers through MCP: tools discovered at runtime, not hardcoded
- RLHF feedback from human reviewers feeds preference pairs and principle extraction
- Batch orchestration and fine-tuning triggers run in the learning loop
- Adapter lifecycle with admin approval gates and full data lineage: candidate, staged, active
- Learned corrections apply to agent behavior today; serving fine-tuned adapters back into your workspace is in active development
Ready to Build?
Dive into our documentation or schedule a technical deep-dive with our engineering team.