Decisions that move work to machines.
An autonomy programme is not a software roll-out. It is a redistribution of work, risk and accountability across humans and machines. The decision to deploy a fleet. Of pickers, vehicles, drones, surgical assistants. Touches workforce, regulation, insurance and brand for years.
Helios Brain for Robotics & Autonomy.
Robotics decisions sit on the boundary of capability and consequence. They redistribute work and risk in ways that take years to play out. The discipline of rehearsing each deployment. Its safety case, its workforce impact, its regulatory posture. Is what separates a programme that scales from one that gets recalled.
6 decisions, tested before they are made.
Fleet deployment under workforce constraints
Project autonomy roll-out across sites and roles. See workforce transition paths, retraining needs, and union and regulatory friction before the press release.
Operational design domain calibration
Test the boundary of the autonomy envelope. Speeds, weather, lighting, payloads. Against actual operational data. Identify where the system actually holds versus where the brochure overstates.
Safety case and incident response
Rehearse incident scenarios. Sensor failure, edge case, mis-classification. And the response policy each triggers. Build the safety case the regulator will demand.
Human-in-the-loop policy design
Compare alternative supervision modes against throughput, error rate and operator load. Identify the policy that holds safety without throttling value.
Liability and insurance positioning
Project the liability surface across operator, manufacturer, integrator and insurer. Identify the contracting structure that survives the first incident.
Capital case and unit economics
Test deployment economics across utilisation, downtime, maintenance and capital cost scenarios. Identify where autonomy actually pays back versus where it merely automates.
Three desks, one substrate.
Calibrates the operational design domain.
Speed, payload, weather and lighting envelopes tested against real data.
Builds the safety case the regulator demands.
Incident scenarios, sensor failures, edge cases rehearsed end-to-end.
Plans the human side of the roll-out.
Role transitions, retraining and union friction surfaced before deployment.
Built for the regulations that govern your sector.
We rehearse the policy change in their model first, then walk into the credit committee with the full picture. The questions get answered before they are asked.