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The goal is the world's first general-purpose robot. You can't download that capability — it has to be earned in the real world. So we build task-specific robots — Tyler, Wattson, and Trey — that do real jobs today. Every hour of work trains one shared brain, compounding toward general physical intelligence. The bodies change; the brain keeps learning.

▸ One brain · every body

Built for the machine, not the screen.

One shared brain runs the hardware — perception, planning, and control on purpose-built robots engineered for the jobsite, not the lab.

Four hard problems

between a demo and a finished floor.

Every one has to be solved to finish real work — and each is what our model is built to handle.

▸ Perception

vision · slab-aware

01

Sees through dust, glare, and uneven slab.

Our perception stack handles real-world lighting, jobsite debris, and inconsistent substrate — without a controlled environment. The benchmark that matters is whether the model still places a tile when the lights are bad and the floor isn't flat.

▸ Navigation

imperfect floors

02

Plans around the obstacles that show up.

Commercial jobsites are stacked with carts, materials, crews, and partial walls. The model maps, replans, and routes continuously — it doesn't assume an empty floor and doesn't stop when it finds one that isn't.

▸ Tolerances

±1 mm

03

Holds ±1 mm despite slop in the materials.

Tiles vary. Adhesive thickness varies. The slab is never level. Our model closes the loop between sensors and end-effector continuously, holding tolerance the trade actually inspects against — not a CAD model.

▸ Learning across embodiments

one brain · many bodies

04

Same brain powers Tyler, Wattson, and Trey.

Skills learned on one robot transfer to the next. Flooring, wire-pulling, cable tray — every different job feeds one shared brain. That's what compounds toward general capability, not a tile-setter's autopilot.

An AI that aces a benchmark is one thing.
One that finishes a real floor is another.

The gap between the two is where the real work lives — and it's the moat. We're building on the hard side of it.

field · HFR

Field models

Four problems Screen models never face — and the four our model was built to solve.

01

Perception

Dust, glare, shifting light, partial occlusion

Slab variation under every tile

No labels, no clean ground truth

02

Navigation

Imperfect floors, obstacles, live human crews

No GPS, no fixed map of the room

Online replan as the floor changes

03

Tolerances

±1 mm against material slop and substrate flex

Force, pressure, and adhesion controlled in real time

Every action commits — no undo on a placed tile

04

Embodiment transfer

Same brain across Tyler, Wattson, and Trey

New end-effectors plug in; the model adapts

Field data from one robot improves all of them

screen · SaaS

Screen models

What "AI" usually means in 2026. Mostly fine for chat. Doesn't finish a floor.

Runs in a browser tab

Outputs tokens

Trained on internet text

Stuck inside a single context

See the brain on a real floor

Watch Tyler think in person.