One mission. Every machine. Every Signal. Every Change.

Dashboards only tell you what is happening; Sunshine.commanderAl actively responds to it. With contextual awareness, it can break complex missions down into tasks and delegate them across your entire fleet of robots and physical assets. Your machines and assets become one unified autonomous team, while you retain ultimate control.

Mission-level task decomposition and multi-agent coordination, adapting in real time. 

Automate with what you already run. No teardown.

The true value of an automated fleet comes from coordination, not isolation. Sunshine.commanderAl connects your existing assets and systems such as cameras, lifts, doors, and robots with AI agents to act as a single responsive layer that bridges your operational technology (OT) and IT stacks.

Vendor-neutral API layer bridging OT systems and IT infrastructure without protocol replacement.

Prove it in simulation. Deploy with confidence.

Physical Al must be reliable before it hits the floor. Our spatial world model transforms video data into a high-fidelity 3D digital twin of your physical environment. It simulates real-world conditions, tests robot fleet strategies against rare and unsafe scenarios, and continuously updates the model with real-world data. By closing the gap between simulation and reality, reinforcement learning produces more robust AI policies, higher task success rates and more predictable robot behaviour—enabling safer, faster deployment at scale.

Physics-accurate digital twins from video that close the reality gap.

Stops the risk before it happens.

Safety isn't a one-time setup; it's a continuous loop. Sunshine.commanderAl constantly monitors your fleet and environment to catch perception failures or latency issues before they cause physical harm. Built on a deterministic "fail-stop" principle, the system anticipates what could go wrong and immediately halts action if it detects a risk. We don't just ensure a single robot is safe—we secure your entire operating environment.

Closed-loop reinforcement learning against live operational data, tuned for fail-stop response.

To support enterprises in accelerating robotic deployment, NCS offers a comprehensive library of pre-trained agents and functional skills—such as wayfinding and conversational Al. Built on our bespoke development framework, these skills are designed to be deployed across hardware-agnostic robot fleet.

Backed by a comprehensive evaluation framework, our training process is accelerated through an end-to-end data pipeline that combines real-world data collection with simulated environments for rapid scaling. We continuously feed actual operational data back into the system to refine behaviour, ensuring every machine operates with the necessary safety guardrails and governance for secure, seamless human-robot interaction.