Edge AI: computer vision on devices

Edge AI runs your models directly on the device that has the camera, for low latency, lower running costs and operation without a constant internet connection.

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What we build

Edge AI Development: what you get

Model optimisation for embedded hardware
Real-time video pipelines on Jetson and similar devices
Local processing with results sent to your systems
Device monitoring and remote updates
Prototype to production hardening
Integration with sensors and PLCs where needed

This is a good fit if…

Cameras are in places with poor connectivity
Latency matters, such as on a production line
Sending video to the cloud is too costly or not allowed

Typical uses

Inspect products on a line with no internet connection
Detect events on a remote site and send only the results
Run a camera model on a small device instead of a server

How it works

A simple process, no surprises

1
Discovery
We review your devices, power, network and the speed you need.
2
Scope and plan
We choose hardware and agree the model size and update method.
3
Build and review
We optimise the model for the device and test it in your real environment.
4
Deliver and support
We deploy to the devices, set up monitoring and remote updates, and document maintenance.

Technologies we use for this

NVIDIA Jetson
TensorRT
ONNX
DeepStream
C++ / Python
Docker

Reviews

What clients say

★★★★★

“They took a task my team repeated every week and turned it into a single button. Clear communication throughout, and the handover documentation meant we could use it straight away”

James · Managervia Fiverr

FAQ

Edge AI Development: common questions

Which hardware do you work with?+

We work mainly with NVIDIA Jetson and similar embedded platforms, and recommend hardware after reviewing your requirements.

Can devices be updated remotely?+

Yes. We can build update and monitoring so you don't visit each device.

From the blog

Camera frame of an industrial scene with labelled bounding boxes around parts, linked to icons for a dataset, a neural network and an edge device.

Computer Vision / AI

Custom Object Detection Models: Data, Training and Deployment

What it typically takes to build a custom object detection model, from collecting and labelling data to training, evaluation and running it on edge or cloud hardware.

11 Oct 2026 · 5 min read

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Need edge ai development?

Tell us where the cameras are and what the device must do. We will suggest suitable hardware and an approach.

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