Statement / Physical AI

Physical AI

Physical AI usually means robots. The definition is simpler: AI attached to a physical process — a system where the model's output becomes a physical action in the real world. A robot arm is one way to close that loop. A warehouse crew is another.

We are the second kind.

How it runs today

These workflows are in production.

A new hire appears in your HR system. We are integrated today with all major HRIS platforms — the systems companies use to run hiring and employee records. The hire triggers a workflow. Our AI agent reads the laptop type assigned to the role, debits your device stock, verifies the shipping address — requesting one if it's missing — confirms accessories if none were specified, and prints the shipping label. A person on our floor picks the configured unit, packs it, and ships it on your carrier account. Offboarding runs the same way in reverse.

A device fails. A swap request comes in. The agent takes ownership of the case, assesses it, collects the shipping address, prints the label, and alerts the warehouse. A person picks a configured spare and ships it. The failed unit comes back for repair processing, tracked by serial number the whole way.

What the agent replaced, and what it can't

Everything the agent does used to be a coordinator's job: reading the request, checking the records, chasing the missing address, updating stock counts, cutting the label. Screen work.

Everything after the label prints is work the agent cannot do. Receiving. Configuring machines to your standard. Building spare pools. Picking. Packing. Shipping. Wiping returned devices. Hands.

The jobs this creates

Across the fleets we manage, we measure 0.66 movements per device per year over a 16-month window — a movement is any physical event: a new-hire shipment, a swap, a return, a repair. Software coordinates every one. A person executes every one.

The work the agent absorbed was the fragile kind — fragile because each model release does more of it. The work that remains is different in three ways:

It sits on the physical side of the loop. A better model writes better text. It does not put a laptop in a box, configure a machine at a bench, or receive a pallet. As models improve, this work doesn't shrink — the coordination around it gets cheaper, which makes more of it economical to do well.

It scales with hardware, and hardware is growing. Every AI system a business adds arrives as devices — controllers, edge machines, handhelds, workstations. More AI in the world means more hardware in the world, and every unit of it gets configured, staged, swapped, and retired by someone.

It happens where the machines are. This work exists at the bench and on the floor. It cannot be done from a screen in another country. It does not offshore.

In this model, growth adds hands, not desks. The jobs are hands-on, local, and hourly — and the people doing them are learning the scarce skill: running the floor of an AI-coordinated operation.

One boundary, stated plainly

The agent runs our logistics workflow. It is not a help desk, and we don't sell it as one. Your team owns everything on a running device — a broken app is your help desk's ticket. A dead device is our swap.

Why this shows up in your price

The coordination layer most providers bill as account management and dispatch is software here. No human queue sits between the trigger in your HR system and the label on the box. That is part of why the rate card is flat and published, and why a swap is a stock event, not a project.

Rates are on /pricing.

Where this leaves you