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Pole-mounted camera and edge compute unit overlooking a logistics yard at night

S/08 · Connected hardware

Edge AI & computer vision

Axio Intelligence deploys machine learning where the data is created: computer vision detection and tracking on edge hardware such as NVIDIA Jetson, anomaly detection on sensor and fleet telemetry, and the data pipelines, labeling workflows and model monitoring needed to keep models accurate in the field.

When teams call us

You probably need this if…

  • 01

    Streaming raw video or telemetry to the cloud is too slow, too expensive or not possible.

  • 02

    You have months of sensor data and no early warning when equipment starts to fail.

  • 03

    A model works in a notebook but not on the device, at the power budget you have.

  • 04

    Field data isn’t making it back into training in a usable form.

Deliverables

What you get

01

On-device inference

Model optimisation and deployment on Jetson, ARM and x86 edge compute with TensorRT or ONNX Runtime.

02

Computer vision

Detection, classification and tracking pipelines for cameras and EO/IR sensors.

03

Anomaly detection

Baselines and alerting on telemetry to catch drift, faults and tampering early.

04

Data & labeling pipelines

Getting field data into models: capture, quality checks, labeling workflows and dataset versioning.

05

Model operations

Versioned model delivery over OTA, shadow deployment and on-device performance monitoring.

06

Field validation

Accuracy, latency and power measured on the target hardware in real conditions, with a written report before rollout.

Technologies

What we work in

NVIDIA JetsonDeepStreamTensorRTHoloscanOpenVINOONNX RuntimeGStreamerMISB KLV / STANAG 4609ROS 2DDS (RTI Connext / Cyclone)OpenCVPyTorchC++RustPython

Named platforms and standards describe what we build with and build to; they don’t imply a formal partnership or certification with the vendor.

Questions

Edge AI & computer vision: common questions

Can you run computer vision on a drone or a pole-mounted camera?

Yes. We size the model to the compute and power available — typically NVIDIA Jetson-class hardware — and send detections and events upstream instead of raw video.

What does anomaly detection need to get started?

A few weeks of representative telemetry and a list of the failures you care about. We start with simple statistical baselines that are explainable to operators, then add learned models where they earn their keep.