The IntelligenceBehindPhysical AI.

Real people performing the real task, recorded with wearable cameras and sensors and structured into training data.

• Task identification with your team
• First-person and wearable capture
• Synchronized video, sensor and action data
• Annotation and dataset structuring

Demonstrations broken down into what a machine can learn: the steps, actions, objects and outcomes of the task.

• Task and step segmentation
• Object, action and outcome labels
• Success criteria for each skill
• Reusable skill definitions

Pre-trained robotics, language and multimodal models adapted to your domain, tested and optimized for your hardware.

• Model selection for the task
• Fine-tuning on domain and demonstration data
• Accuracy, safety and edge-case evaluation
• Optimization for on-device inference

Memory, context, reasoning and goals that decide what the machine does next and coordinate its capabilities.

• Short and long-term memory
• Context and domain knowledge
• Goal planning and task coordination
• Guardrails on speech and action

Cognitive decisions connected to physical hardware through sensors, actuators and validated control systems, enabling movement, speech and manipulation within defined safety constraints.

• Sensor and actuator interfaces
• Integration with existing control systems
• Timing from sensing to action
• Safety constraints on movement and speech

Behaviour monitored, outcomes evaluated and feedback turned into controlled model updates.

• Behaviour and task monitoring
• Success, failure and intervention tracking
• Evaluation against agreed tests
• Controlled model updates