Edge AI
Models that run beside the machine or camera, measured on the device, with no round trip to the cloud.
01 · Build
What we build
Some decisions cannot wait for the cloud: a reject gate, a safety zone, a camera on a line with a weak link.
- Models quantised and compiled with ONNX Runtime, TensorRT or OpenVINO
- Deployment to NVIDIA Jetson, Hailo, Intel and Arm boards
- Latency, accuracy and power measured on the target device
- Over-the-air model updates with staged roll-out and rollback
- Local buffering when the network drops, synced when it returns
- Drift and confidence monitoring, with samples returned for review
02 · Demo · sample data
Try it live.
A working sample with made-up data. It runs in your browser; nothing is sent anywhere.
- Devices online23
- Latency p9518 ms
- Defect recall97.1 %
- Board power11.6 W
03 · Features
What it does well
- 01
Measured on device
Latency and accuracy are measured on the real board, at the real frame rate.
- 02
Right-sized models
Quantisation and pruning are traded against accuracy, with both numbers reported.
- 03
Staged roll-out
A new model goes to a few devices first and widens only when its metrics hold.
- 04
Offline first
Devices keep deciding without a network and upload results when it returns.
- 05
Low-confidence samples
Uncertain cases are saved and sent back for labelling, so the next model learns.
- 06
Fleet view
Model version, health and accuracy for every device are visible in one place.
04 · FAQ
Questions
Why not run the model in the cloud?
The cloud is right when a decision can wait a second and the link is reliable. At the edge you get answers in milliseconds, no dependence on the network, and images that never leave the site. We choose per use case and often combine both: decide at the edge, learn in the cloud.
Which hardware should we buy?
It depends on the model, the frame rate and the power budget. We benchmark your model on two or three candidate boards and report latency, accuracy, power and cost per unit, so the hardware choice is made from measurements.
How do model updates stay safe?
Each model is signed, tested on a reference device, rolled out to a small group first, and kept beside the previous version. If accuracy or latency on the devices falls outside agreed limits, the update stops and devices roll back.
05 · Services
Services that go with it
- ServiceAI integrationLanguage and vision models built into your existing software and workflows, with guardrails and measured accuracy.
- ServiceCustom software developmentSoftware designed and built around the way your business works, shown to you working at every review.
- ServiceDeployment and DevOpsAutomated pipelines that build, test and release your software the same way every time.
06 · Software
Related software
- SoftwareComputer visionCamera-based software that inspects, counts and detects, running on edge devices beside the camera or in the cloud.
- SoftwarePredictive maintenanceVibration, temperature and current data turned into early warnings and work orders before a machine fails.
- SoftwareRobotics softwareSoftware for robot arms, mobile robots and fleets, built on ROS 2 and tested in simulation first.
- SoftwareIoT platformsConnected devices onboarded securely, monitored live and updated remotely from one platform.