Autonomous operators that actually run the business
Labs builds Maia — an agentic operating system where persistent AI operators own real business functions end to end, executing against live systems behind a hard approval fence, on hardware you control.
Maia Nexus Core
Most agent tooling stops at chat. Maia is a runtime: long-lived operators with durable memory, a governed tool layer, and an execution gate that decides what an agent is allowed to actually do.
Operator runtime
Persistent agents scoped to a business function — not one-shot prompts. Each operator carries its own memory, capability set, and audit trail across sessions, and keeps working the function between conversations.
Governed tool layer
Every tool is tiered. Reads run free; writes and outward-facing actions pass a three-layer fence and an explicit approval gate before they touch a real system.
DeployedLocal inference router
Keeps sensitive work on local GPUs and escalates to frontier APIs only when the task earns it — per-call, per-operator, policy-driven.
DeployedOperative fleet
Specialized operators running concurrently against shared state, with supervision, hand-off, and escalation to a human when confidence drops.
In developmentLocal-first, on NVIDIA silicon
Maia is built for teams that cannot ship operational data to a third-party API — and for the economics of agents that run continuously rather than occasionally.
- ✓Runs on DGX Spark (GB10). 128 GB of unified memory lets a single desktop-class node hold models that would otherwise need a rack.
- ✓Continuous open-weight evaluation. We benchmark new open-weight releases against real agentic tasks — tool-call fidelity and multi-step reliability, not leaderboard scores.
- ✓Hybrid by policy, not by accident. Local models handle the high-volume inner loop; frontier models are called deliberately and stay substitutable.
- ✓Data stays put. The full operator loop — memory, retrieval, tool execution — can run without egress.
From function to autonomous operator
Maia is deployed against one business function at a time, and earns autonomy as its track record accumulates.
Scope the function
Pick a bounded, high-repetition function. Define what done looks like — and what the operator may never do.
Wire the tools
Connect the systems of record. Every tool is registered with a tier: read, act, or restricted.
Run supervised
The operator works the function with every write gated. Each approval and rejection becomes signal for its policy.
Release the gate
Proven action classes graduate to autonomous execution. Everything else stays gated. The audit trail never stops.
Where Maia goes next
Operative fleet
Multiple supervised operators running concurrently, with hand-off and shared state across business functions.
Deployable node
Maia packaged as a self-contained appliance image, so a team can stand up the full stack on their own NVIDIA hardware.
Operator catalog
Pre-built operators for the functions every small company runs, installable into a customer's own Maia node.
We’re onboarding design partners
If you run a business function that is high-volume, rule-heavy, and too sensitive to hand to a public API, we want to hear about it.
hello@rawrtech.ai