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Hyperion Consulting logo - Physical AI and industrial AI consulting

Decision engineering for Physical AI — full-cycle product leadership and architecture across solution, system, software, data and AI.

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Industries

Primary market

  • Manufacturing
  • Automotive & mobility
  • Energy & infrastructure

Adjacent sector

  • Logistics & fleets
  • Agrifood robotics
  • Maritime operations
  • Urban systems
  • Defence & dual-use
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Product & architecture capabilities

One Physical AI product. Every critical layer.

Physical AI is not a model or a stack of vendors. It is one product system across hardware, software, data, AI, control, people and operations. Hyperion connects those layers at product, solution, system and software architecture depth.

Trust, Safety & Cybersecurity

  1. L5Orchestration & Operations

    Run, observe and recover a fleet across edge and cloud.

    vLLM · TensorRT · fleet ops

  2. L4Intelligence & Knowledge

    Vision-language-action models, open-weight intelligence and industrial knowledge.

    Mistral · VLM/VLA · industrial RAG

  3. L3Perception & Robotics

    Seeing, mapping, planning and moving in the physical world.

    ROS 2 · SLAM · Isaac Sim

  4. L2Embedded Platform

    The kernel-to-fleet software foundation that makes a device dependable.

    Linux · Yocto · PREEMPT_RT · OTA

  5. L1Device & Controlground

    Sensors, actuators and deterministic control at the physical edge.

    Sensors · MCU/PLC · OPC-UA · CAN

Grounded in engineering execution: embedded and edge systems, robotics and VLM/VLA, open-weight models, industrial RAG and knowledge, safety and security — the depth to lead productization credibly.

The full Physical AI stack — embedded foundations at the base, building up through perception, intelligence and operations, with a trust, safety and cybersecurity envelope around every layer.

Cross-cutting practice

Software-Defined Transformation for Physical Products

Hardware defines the operating envelope. Software lets the product evolve. AI can change perception and decisions—but only inside evidenced boundaries.

The practice cuts across the complete capability stack. It determines what belongs in the physical product, deterministic control, software, data or AI—and how those choices survive deployment.

  • Value after shipment
  • Physical and software boundary
  • Platform and variants
  • Update and field lifecycle
  • AI candidacy and authority
  • Assurance, ownership and economics
See how the capability stack becomes an evolvable physical product
  • SYSApplied AI SystemsProduct, architecture, data and evaluation decisions for edge intelligence, governed knowledge and task-specific models.Explore pillar
  • L1·L2Embedded & Edge SystemsReal-time control and AI at the edge — from silicon and RTOS to on-device inference.Explore pillar
  • L3Robotics, VLMs & VLAsPerception-to-action systems on real robots, with a deterministic safety boundary.Explore pillar
  • L4·L5Mistral & Open-Weight ModelsSovereign AI you own and run — open-weight models adapted to your domain.Explore pillar
  • L4Industrial RAG & KnowledgeAnswers grounded in your manuals, specs and standards — traceable to source.Explore pillar
  • —Physical-AI Safety & SecurityThe independent boundary that constrains, monitors and fails safe.Explore pillar

Across the open Physical AI ecosystem

The perception, control, and autonomy patterns we work in — illustrated across open robotics platforms.

  • Pixel-art illustration of a robot work cell inspecting parts on a conveyor with a perception layer.

    Robot work cells

    Inspection and manipulation cells read by a perception layer.

  • Pixel-art illustration of an autonomous fleet's charging grid with energy levels read by a monitoring layer.

    Fleets & charging

    Energy and readiness monitoring across an autonomous fleet.

  • Pixel-art illustration of an autonomous ground vehicle navigating with a forward perception scan.

    Ground vehicles & AGVs

    Navigation and forward perception for mobile ground platforms.

SLM, RAG or neither?

Turn your task, evidence and operating constraints into a least-complex model strategy and a decision brief.

Run the diagnostic