Intelligence right where the work happens.
What It Is
Some decisions cannot wait for a round trip to the cloud: rejecting a part, stopping a conveyor, warning a forklift driver. Others produce too much data to send - high-rate vibration, video. Running the model at the edge solves both.
Running AI at the edge reliably is an operations job as much as a modelling one: hardened devices, models optimised for the hardware, remote updates, health monitoring and a way to collect new examples so models keep improving.
How It Works
The system from the sensor to the people who act on it. Every project is sized to the site, but the layers stay the same.
- Input
Cameras & sensors
Video, images, vibration or process data captured at the source.
- Compute
Edge device
Industrial PC or GPU module running optimised models in containers.
- Act
Local action
Results sent straight to the PLC, a light, a buzzer or an operator screen.
- Sync
Upstream
Results and selected samples sent up over MQTT for review and re-training.
- Manage
Fleet & MLOps
Remote updates, health monitoring and model versioning across devices.
Key Technologies
The protocols, standards and components we work with on this use case.
Hardware
- Industrial PCs
- NVIDIA Jetson
- Intel CPUs & iGPUs
- Fanless enclosures
Runtime
- ONNX Runtime
- TensorRT
- OpenVINO
- Docker
- K3s
Operations
- OTA updates
- Model versioning
- Drift monitoring
- MQTT
- OPC UA
Typical Deliverables
What a project usually hands over. We agree the exact scope with you before work starts.
- Use-case definition and data collection plan
- Trained and optimised models
- Edge hardware selected and installed
- Integration with PLCs and operator screens
- Remote update and monitoring set-up
- Re-training workflow
Where It Applies
Industries where this use case answers a problem we see often. Each links back to the sector page.
Related Use Cases
Let's Scope Your Project.
Tell us about your site and what you want to improve. Our engineers will help you scope a practical first step.