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.

Hyperion does not position itself as a general engineering bench. Technical fluency is used to make better product decisions, challenge assumptions, define evidence and coordinate the critical specialists required by the mandate.

Four capabilities, one purpose

Capability here means the ability to make and hold a decision — not a catalogue of technologies for sale. The domains further down the page are where these four are exercised.

  • Product decision ownership

    Holding the consequential product and architecture decisions: what the product must do, what it must not do, which option is chosen and on what evidence. This is the capability; everything else exists to serve it.

  • Whole-system architecture fluency

    Reading a complete Physical AI system — hardware, embedded control, software, data, models, safety and field operations — well enough to see where a choice in one layer becomes a constraint in another, and to challenge a specialist's answer instead of accepting it.

  • Evidence and readiness design

    Deciding what would have to be observed before a system may advance: the acceptance bar, the tests that settle it, which classes of evidence count, and the conditions under which the work stops.

  • Specialist execution orchestration

    Scoping, briefing and coordinating the specialists a mandate requires, with their role disclosed. Hyperion holds the decision and the acceptance bar; the implementation is carried out by your team or by named partners.

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.

Where those decisions are exercised

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