Architecture

Physical AI system architecture

Definition

A six-layer reference architecture, created by Mohammed Cherifi, for making physical infrastructure AI-native — from sensor data capture to autonomous fleet orchestration. The six layers are SENSE (sensors and data acquisition), CONNECT (communication and data pipelines), COMPUTE (edge and cloud processing), REASON (AI agents and decision engines), ACT (autonomous control systems), and ORCHESTRATE (software-defined coordination). Most AI consulting stops at REASON; most production failures live in ORCHESTRATE.

Examples

SENSE → CONNECT → COMPUTE → REASON → ACT → ORCHESTRATE

Related Terms

Physical AI

AI systems that perceive and act in the physical world through robotics, autonomous vehicles, smart manufacturing equipment, and industrial sensors. Physical AI integrates computer vision, reinforcement learning, and real-time control to automate tasks previously requiring human dexterity and judgment in unstructured environments.

Edge AI

Running AI models on edge devices—industrial controllers, cameras, vehicles, mobile phones—rather than in the cloud. Edge AI reduces latency, preserves data privacy, and enables operation in environments with limited connectivity, making it critical for physical AI deployments.

Embodied AI

AI systems situated in a physical body or robot that perceive the world through sensors and act on it through actuators. Embodied AI bridges software intelligence and the physical world, making it central to robotics, autonomous vehicles, and smart manufacturing.

Hyperion Physical AI Product System

A three-engagement delivery model that ensures every AI initiative has a production path. A Product Decision Review (identify the real problem and decide what deserves to reach production), a Product Leadership Mission (architecture that survives real users, with security, evaluation and safety designed in — through discovery, productization and launch, with kill switches and graduation criteria), and a Executive Product Leadership (governance under real regulation, capability transfer, and measurable ROI at scale). For SMEs, the same path runs on compressed, budget-conscious timelines.

Related Services

Product Leadership Program

Knowing the Terms Is Step One. Applying Them Is Step Two.

Book a Physical AI Fit Call to discuss how these AI concepts translate to your specific industry and business challenges.