aiQu Labs

ai · PHYSICAL AI & EDGE

Intelligence on the device, decisions in milliseconds.

Perception and control that run on embedded silicon — no cloud round-trip in the loop.

Talk to an EngineerIndustrial AI

WHAT MAKES IT PHYSICAL

A model that has to act, not just report.

Physical AI is what happens when a model has to act, not just report. A camera on a robot arm, an NPU on a drone, a controller on a press — the decision has a deadline measured in milliseconds, and the power budget is fixed by the enclosure.

Embedded GPU and NPU modulesVision MCUs and smart camerasIndustrial PCs and edge gatewaysCustom carrier boards

CAPABILITIES

Four things we build at the edge.

Each one is scoped against measured hardware limits, not a datasheet claim.

  • On-device inference

    Models quantised and compiled for embedded SoCs and NPUs, profiled on the actual board rather than a desktop GPU.

    What you get

    • Measured latency and power on target hardware
    • INT8 / FP16 paths with accuracy deltas documented
    • Runtime packaged for the board you ship

  • Sensor fusion

    Camera, depth, thermal, IMU, acoustic and vibration streams aligned in time and fused into one state estimate.

    What you get

    • Time-synchronised capture across sensors
    • Calibration procedure your technicians can repeat
    • Graceful behaviour when one sensor drops out

  • Real-time control loops

    Perception wired into actuation: reject gates, pick-and-place, speed and alignment correction, safety stops.

    What you get

    • Deterministic cycle timing, budgeted per stage
    • Fail-safe default when confidence drops
    • Operator override always available

  • Hardware and firmware

    Board selection or custom design, sensor interfaces, firmware, thermals and enclosure — specified for the environment.

    What you get

    • Bill of materials with sourcing alternatives
    • Firmware with OTA update support
    • Thermal and ingress validation for the site

DESIGN CONSTRAINTS

Four constraints we fix in discovery.

Settle these before the model, and deployment stops being a surprise.

Latency
Budgeted per stage and measured on the target board, not estimated on a desktop GPU.
Power
Model and runtime chosen to fit the enclosure, the thermal path and the supply.
Connectivity
Decisions stay local. Cloud handles retraining and fleet views, never the loop.
Failure behaviour
Defined before deployment: fail-safe default, operator override, logged reason.

NEXT STEP

Have a device that needs to see and decide on its own?

Send us the board, the sensor and the deadline. We will tell you whether it fits.

Talk to an EngineerEnd-to-end custom AI