01 · control plane
Control plane for AI
One plane over every model, provider, and agent. Route, pin, and govern from a single seam.
The control plane for AI
In the AI era, those with moats survive.
Build yours now.
Frontier capability is renting itself out by the token, and your hardest problems are training someone else’s model. moat is the control plane that brings it home — isolated harnesses, virtualized models, and reinforcement on your own data. Take control of your LLM usage. Repatriate your innovation.
The thesis
The moat moved.
Models commoditize. Providers churn. The frontier leader changes every quarter — today it’s a name you didn’t know last spring. Chasing the best model is a treadmill.
The durable advantage isn’t which model you call. It’s owning the plane that routes, isolates, and improves them — and the data that compounds underneath. Control is the moat.
The platform
One plane. Every model. Your metal.
02 · harnesses
Isolated coding harnesses
Sandboxed, reproducible agent environments. Blast radius of zero, repeatable to the byte.
03 · virtualization
Virtualized models
Swap providers behind a stable interface — local, edge, or frontier. Your code never knows the difference.
04 · learning
Local reinforcement learning
Improve on your own data, on your own metal. Every session compounds into capability you own.
05 · repatriation
Repatriated innovation
Bring capability back in-house. Your hardest problems stop training someone else’s model.
06 · governance
Governance & observability
Every token, every call, every dollar accounted for. Usage you can see is usage you can control.
Architecture
One seam between your work and every model.
Deployment
Run it where your data lives.
Primary · private inference & training
Your weights. Your metal. Zero egress.
Serve and train on hardware you control. Prompts, weights, and gradients never leave your network — and every session of reinforcement compounds into capability you own, not a moat you’re renting from someone else.
- Air-gapped inference — prompts and weights stay inside your perimeter.
- Fine-tune & RL on proprietary data — your hardest problems stop training a vendor’s model.
- Own the weights — no per-token rent, no provider lock-in.
- Your hardware, your scheduler — GPUs, quotas, and SLAs under your control.
Hybrid burst
Keep sensitive work on your metal; burst to frontier providers only when a task demands it. Per-route policy makes the call.
Multi-provider routing
Virtualize Claude, GPT, Gemini, and open weights behind one interface. Swap or fail over without touching a line of code.
Air-gapped & regulated
Fully isolated deployments for finance, health, and defense. Compliance-grade audit on every call, zero egress.
Request access
Build your moat.
Private beta for teams who’d rather own their intelligence than rent it.