Motion™

Physical operations are planned with tools that cannot simulate the plan. Layout, throughput and fleet-sizing decisions are made in spreadsheets and validated only after capital is committed, so mistakes are discovered in concrete. Robotics compounds this: fleets from different vendors arrive with incompatible control stacks, so an operator with three OEMs runs three consoles and has no unified view of availability or throughput.

• Planning decisions are validated after commitment, when they are expensive to reverse.
• Multi-OEM fleets fragment into separate control stacks with no common availability picture.
• Training perception models requires edge-case data that real operations rarely produce safely.
• Vendors sell hardware and disclaim the outcome, so uptime is nobody's contractual problem.

Motion runs physical operations against a live digital twin. Layout, routing and fleet-sizing decisions are simulated before commitment, and the twin remains synchronised with the floor in operation, so deviation is visible against plan rather than discovered at the end of a shift. Synthetic data generation supplies the edge cases that perception models need and real operations cannot safely produce. Fleet orchestration is multi-OEM by design, presenting one availability and throughput picture across heterogeneous robots. Physical actions are human-gated; the commercial model is SLA-backed on uptime and throughput.

Simulation and synthetic data address the fundamental constraint of physical AI: you cannot collect enough real-world failure data safely, and you cannot iterate on a floor plan that has already been built. Training in a twin and transferring to the real system inverts the order — the expensive decision is tested before it is made. Orchestration across OEMs is a planning problem over heterogeneous capability, which is exactly where a model outperforms a per-vendor scheduler.

TWIN

Live digital twin, synchronised with floor state

SIMULATION

Layout, routing and fleet-sizing tested pre-commitment

DATA

Synthetic generation for perception edge cases

FLEET

Multi-OEM orchestration under one availability view

SAFETY

Physical actions human-gated

COMMERCIALS

SLA-backed on uptime and throughput

1. 01Capital decisions simulated in the twin before commitment, not validated after.
2. 02Twin stays synchronised in operation, so deviation from plan is visible live.
3. 03Synthetic data supplies perception edge cases real operations cannot safely produce.
4. 04One availability and throughput view across robots from different vendors.
5. 05Physical actions gated by human authorisation at defined boundaries.
6. 06Contracted on uptime and throughput, so the outcome is the vendor's problem.

~30%

Lower planning costindicative target

SLA-backed

Robot availabilitycommercial commitment

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