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AI Infrastructure
Autonomous systems that run the business while you sleep. Claude-native agent fleets running operational workflows end-to-end — intelligence, commerce, content, outreach, client ops. Same architecture we run on ARCANA AI, pointed at clients.
Operational scope.
We design, build, and operate autonomous agent systems. Forward-deployed engineering on the same Claude-native architecture we run continuously on our own books — ARCANA AI is the reference implementation. Every fleet ships with observable behavior, bounded tool access, auditable reasoning traces, and documented failure modes.
Architectural
decisions made
before code.
Architectural posture.
We build agent fleets that run — not demos that present. Every system we ship has observable behavior, bounded tool access, auditable reasoning traces, and documented failure modes. We architect for continuity: agent memory that survives process restarts, orchestration that handles upstream API failure, and observability that makes behavior debuggable after the fact.
Our reference implementation is ARCANA AI, an autonomous 30-agent fleet operating on our own P&L. It trades, publishes content, synthesizes research, and runs revenue operations continuously. The same architectural patterns — Claude-native reasoning, MCP-integrated tools, Supabase-backed memory, n8n-orchestrated workflows — are what we deploy for clients.
We do not ship agents without observability infrastructure. Production agents that cannot be inspected are production incidents waiting to happen. Every action logs to a structured audit trail; every escalation is documented; every tool surface is bounded by the principle of least privilege.
What ships.
- 01Agent fleet architecture
- 02MCP server integrations
- 03Memory / RAG layer (Supabase pgvector)
- 04Orchestration (n8n or custom)
- 05Observability and log infrastructure
- 06Operator handoff and documentation
- 07Monthly agent performance review
How we work.
Representative work.
Where the muscle translates.
Federal and regulated-industry buyers need agent systems that are auditable, bounded, and model-accountable. Claude is the most defensible reasoning substrate for that posture — responsible scaling policy, constitutional AI framework, and on-prem-capable inference pathways available through partner providers. The architecture we build is designed around those properties, not despite them.