Full-Stack Sovereign AI: One Platform From Inference to Audit Trail
Build an enterprise AI system today and count the vendors.
LLM inference: OpenAI or Anthropic or Google. Vector database: Pinecone or Weaviate or Qdrant. Orchestration: LangChain or CrewAI or AutoGen. Identity: roll your own or hope Microsoft ships Entra Agent ID. Secrets: HashiCorp Vault if you're sophisticated, environment variables if you're honest. Compliance: spreadsheets and prayers. Observability: Datadog or Grafana or another vendor. Audit trails: logs you hope nobody asks to see.
That's seven or more vendors, seven integration surfaces, seven points of failure, seven security reviews, seven contracts, and zero unified audit trail.
Now try deploying that stack air-gapped.
The Integration Tax
Every vendor boundary is a tax. Not just financial — operational.
Security review. Each vendor needs a security assessment. Each integration point is an attack surface. Each data flow between services is a potential exfiltration path. Your security team reviews seven vendors. Then reviews them again next quarter when they ship updates.
Compliance mapping. Your compliance team needs to map each vendor to your control framework. Where does data rest? Where does it transit? Who has access? Which jurisdictions apply? Seven vendors means seven data processing agreements, seven sub-processor lists, seven breach notification obligations.
Incident response. When something goes wrong — and it will — your team has to trace the issue across seven systems with seven different logging formats, seven different retention policies, and seven different support teams. The error happened in the orchestrator? Or the vector database? Or the LLM provider returned something unexpected? Good luck.
Vendor risk. Each vendor is a dependency. Pinecone raises prices. OpenAI changes their API. LangChain ships a breaking change. CrewAI gets acquired. Any one of these events derails your roadmap. Seven vendors means seven independent risks, any of which can cascade into a production incident.
This is the actual cost of "best of breed" in enterprise AI. It's not the sum of the license fees. It's the integration tax that compounds with every additional vendor.
What Full-Stack Means
Aethyr ships a single platform that covers the entire stack from LLM inference to compliance audit trail.
| Layer | What Aethyr Provides | What It Replaces |
|---|---|---|
| LLM Inference | Self-hosted via Ollama + cloud fallback (Claude, GPT, Gemini) | OpenAI/Anthropic/Google direct |
| Agent Runtime | AIOS — 84 syscalls across 22 categories | LangChain, CrewAI, AutoGen |
| Tool Integration | 241+ MCP tools across 14 plugins | Custom integrations per vendor |
| Knowledge Retrieval | Ontology Augmented Generation (HDC) | Pinecone, Weaviate, Qdrant |
| Identity | Post-quantum W3C credentials | DIY OAuth, Entra Agent ID |
| Secrets | HashiCorp Vault envelope encryption | Standalone Vault deployment |
| Compliance | 137-privilege PBAC, 8-dimension sovereignty scoring | Spreadsheets |
| Observability | Prometheus, Grafana, Loki, Tempo | Datadog, New Relic |
| Audit | 30+ event types, cryptographically signed | Application logs |
One deployment. One security review. One compliance mapping. One vendor.
Deploy Cloud to Air-Gapped
The same platform runs in three deployment modes without code changes:
Cloud
Full-featured deployment using cloud LLM providers. Best model quality. Requires internet connectivity. Data transits to third-party infrastructure.
On-Premises
Self-hosted LLM inference via Ollama. Data stays on your infrastructure. No internet required for inference. Cloud connectivity optional for model updates.
Air-Gapped
Zero internet dependency. Ollama with pre-loaded models. PSAM cognitive modules in Rust. Local mesh networking. Agents operate, communicate, and coordinate entirely within the local network.
The critical point: these aren't three different products. It's one platform with a deployment configuration. An agent built for cloud deployment migrates to air-gapped without rewriting. The cognitive state, credentials, and tool integrations carry over. The only thing that changes is where inference happens.
This is what makes Aethyr deployable in defense, intelligence, and critical infrastructure — environments where "just use the API" isn't an option.
The Compliance Stack
Enterprise AI without compliance infrastructure is a demo. Here's what Aethyr ships:
137-Privilege PBAC. Not role-based access control. Privilege-based. 137 discrete privileges that control every operation in the system — from "can view agent list" to "can approve tool invocation in production." Privileges compose into roles, but the underlying model is granular enough for any organizational structure.
8-Dimension Sovereignty Scoring. Every deployment scores across eight dimensions: data residency, inference location, model provenance, encryption standard, identity authority, audit completeness, network isolation, and compliance framework coverage. The score gives CISOs a single metric for sovereignty posture.
HITL Workflows. Human-in-the-loop approval for sensitive operations. An agent can propose an action, but execution waits for human authorization. Configurable per operation type, per privilege level, per data sensitivity. The audit trail records who approved, when, and what context was visible at approval time.
30+ Audit Event Types. Every significant action generates a signed audit event: agent creation, credential issuance, tool invocation, data access, configuration change, privilege grant, HITL approval, model selection, and more. Events are cryptographically signed with the acting agent's or user's key. Tamper-evident by construction.
The Observability Stack
Production AI systems need observability that understands AI-specific failure modes:
- Prometheus for metrics — agent count, tool invocation rate, LLM latency, token consumption, squad health
- Grafana for dashboards — pre-built views for agent performance, tool reliability, compliance status
- Loki for logs — structured logging with correlation IDs that trace from user request to agent action to tool invocation to LLM call
- Tempo for distributed tracing — full request lifecycle across the agent mesh
This ships with the platform. Not as an add-on. Not as an enterprise tier. Every deployment includes full observability from day one.
241+ Tool Integrations
Agents are only useful if they can act. Aethyr ships 241 tools across 14 plugins via the Model Context Protocol:
- HubSpot (49 tools) — CRM, contacts, companies, deals, tickets
- ServiceTitan (81 tools) — jobs, invoices, dispatch, equipment
- Google Workspace (26 tools) — Gmail, Calendar, Drive, Docs
- SharePoint & Outlook — document management, email
- Zapier — 6,000+ app connections
- And more — extensible plugin architecture for custom integrations
Every tool invocation is logged, audited, and governed by the privilege system. An agent can only use tools its privileges allow. A HITL gate can require approval for sensitive tools. The audit trail records every invocation with full context.
Why This Matters for Regulated Industries
Healthcare, finance, defense, energy, government — these sectors can't deploy multi-vendor AI stacks because:
- Data residency requirements prohibit sending data to third-party cloud services
- Audit requirements demand complete, tamper-evident records of every AI action
- Access control requirements need granularity beyond what RBAC provides
- Continuity requirements mean the system must function during network outages
- Security requirements mandate encryption standards that survive quantum computing
Aethyr was designed for these constraints from the start. Not bolted on after a cloud-first MVP.
One platform. One deployment. One security review. One compliance mapping. One audit trail. Cloud to air-gapped.
That's what full-stack sovereign AI means. It means you stop assembling and start deploying.
Aethyr Research — Salt Lake City, UT