Intelligence that understands the machine it runs on — a universal retrofit layer plus a four-tier intelligence stack, coordinated by an operations OS that runs across construction, cleaning, logistics hubs, and mining sites.
The OS coordinates six worker classes across every site: humans, retrofit robots, purpose-built robots, humanoid robots, AI agents, and wearables. Each class is a data source for the flywheel and an execution channel for the operations layer.
Foundation models can't sense a scrubber's brush pressure or a mining haul truck's articulation angle. The stack starts with a universal retrofit — one hardware kit across 238 machine types and 45+ OEMs — and layers four intelligence tiers on top. Each tier is what most robotics companies stop at. The OS delivers all four.
Sensor pod, Jetson compute, actuator bridge, safety controller. One hardware kit that fits any machine — including humanoids on wheels.
The machine physically does the work — steering, brush pressure, arm extension, vacuum control.
Completes whole tasks, not just motions — "make the floor clean," not "drive route A."
Understands the world — forklifts, weather, shift patterns, safety zones. 18,000+ catalogued edge cases.
Delivers the business outcome — SLA tracking, scheduling, fleet coordination, auto-reporting. One field support tech per site.
Frameworks, Systems, Agents, Tools, Playbooks, Processes. Each module composes into the OS the way subsystems compose into a classical operating system. Tap any card to see what it contains.
The base classes — worker classes, autonomy stack, edge-case taxonomy, site maturity curve.
The engines — navigation, perception, agent runtime, predictive maintenance, SLA, edge-case pipeline.
Distributed autonomous agents — site ops, FST mobile, NOC operator, edge-case curator.
The dashboards and applications — customer portal, NOC console, fleet manager, task planner.
Repeatable ops — site onboarding, exception routing, safety response, quality audit.
The operational cadence — data ingest, training, deployment, validation, iteration.
The OS gets smarter the way classical operating systems did — usage accumulates. Each of the six worker classes emits data from live sites, and the flywheel turns it into L1–L4 intelligence, validated V0 through V3.
60+ live sites, 238+ machine types, three years SLA-graded. Real production, not simulation.
Smiling Buddha — 18,000+ edge cases, contributed and validated by the community.
Explore the open library →Generated from simulation and public data. What labs and academic groups start with.
One real production observation confirms a synthetic case. Individual validation.
Confirmed across sites, machines, and environments. Pattern-level validation.
Emerges only from live deployment. Cannot be scraped, cannot be synthesized. The moat.
Every capture reduces the next site's Human-in-Loop. Minimal manpower. Maximum automation.
The curve is the mechanic. FSA is the outcome.
Human-in-loop is delivered from low-cost operating centers — margin-positive today, shrinking as autonomy climbs.
Foundation models can plan and reason. They cannot sense a scrubber's brush pressure, a haul truck's articulation angle, or a construction site's shifting shadow patterns. The general-intelligence approach spends billions before it works anywhere. The retrofit + L1–L4 approach ships revenue from Day 1.
The OS runs the same intelligence stack across four site categories. Different machines, different edge cases, one platform.
Revenue-generating. 60+ sites. Reference deployment for the retrofit + L1–L4 architecture.
Material handling and warehousing. Same retrofit kit, MHE-specific edge cases.
Earthmoving, brick-laying, finishing. Humanoids on wheels + purpose-built AGVs.
Haul trucks, dozers, drills. Long-tail edge-case severity — where the retrofit moat compounds hardest.
Not a research project. A deployable, revenue-generating platform — starting from the hardware up.