Cloud AI generates text. Physical AI drives vehicles, controls robots, and runs factories. Different constraints. Different architecture. Different expertise required.
NVIDIA's Jensen Huang declared 'the ChatGPT moment for physical AI is here' at CES 2026. The transition from cloud to physical AI is accelerating.
100ms response time is an eternity in autonomous vehicles. Your cloud-first architecture can't meet real-time constraints.
Edge devices can't 'try again' when inference fails. Physical AI requires reliability that demo-ware doesn't deliver.
Physical AI systems interact with the real world—hallucinations aren't embarrassing, they're dangerous.
A structured approach to deploying AI in the physical world—from use case discovery through production deployment on edge devices, robots, and autonomous systems.
Physical AI use case identification. Determine where AI creates value in the physical world. Edge vs. cloud vs. hybrid architecture decisions.
Edge AI architecture design. Hardware selection, model optimization, OTA update strategy. Failsafe and fallback design for safety-critical systems.
Model development optimized for edge deployment. Sensor fusion strategy. Simulation and testing frameworks for physical AI validation.
Production deployment on edge devices. MLOps for physical AI. Safety certification and regulatory compliance.
A structured approach to deploying AI in the physical world, developed from hands-on experience building connected vehicles at Renault-Nissan and edge AI systems at Cisco. Designed for the constraints that make physical AI fundamentally different.
You're building systems where AI controls physical devices—vehicles, robots, industrial equipment, edge appliances. You understand that cloud AI architecture doesn't translate to the physical world. You need expertise that spans both AI and embedded systems, from someone who's built real-time systems at scale.
Cloud AI (LLMs, image generation) runs in data centers with abundant compute, high latency tolerance, and graceful degradation. Physical AI runs on edge devices with strict latency constraints (milliseconds, not seconds), limited compute, and zero tolerance for failures. Different architecture, different expertise required.
Automotive (ADAS, autonomous vehicles), robotics (AMRs, humanoids, cobots), manufacturing (quality inspection, predictive maintenance), energy (smart grid, EV charging), and any domain where AI must control physical systems in real-time.
Vendor-agnostic guidance across NVIDIA (Jetson series), Qualcomm (Snapdragon), Intel (Movidius), and custom silicon. Hardware selection depends on your specific constraints: power budget, inference requirements, thermal envelope, and cost at scale.
Safety certification (ISO 26262 for automotive, IEC 62443 for industrial) must be designed in from the start, not bolted on. I help you understand regulatory pathways, design for certification, and build the documentation and testing frameworks required for approval.
With careful architecture. LLMs can provide planning and reasoning capabilities, but the physical control loop must have hard real-time guarantees and deterministic behavior. We design hybrid architectures where LLMs inform decisions but safety-critical execution uses verified, deterministic systems.
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