Capability
Edge AI & Real-time
On-device inference, low-latency systems, industrial camera integration and real-time processing pipelines for AI that runs where the data is created. Suited to sites where connectivity is limited or decisions cannot wait for the cloud.
When milliseconds matter and connectivity cannot be assumed, we engineer AI that runs on the edge, with support for its full lifecycle.
01Architecture
Reference architecture
A typical arrangement of the components. Every implementation is adapted to your systems, data-residency and security requirements.
Sensors
- Industrial camerasGigE · USB3
- Sensor inputsMQTT
Edge device
- Optimised inferenceTensorRT · ONNX Runtime
- Stream processingGStreamer
- Local decisions and buffering
Fleet management
- Over-the-air updatesBalena · Docker
- Central monitoring
Model lifecycle
- Training and optimisation
- Staged releases
02Components
What this capability covers
01
On-device inference
Optimised models running on Jetson, mobile and embedded targets, with managed updates.
02
Real-time pipelines
Streaming video and sensor pipelines with low-latency decision loops, sized to the process they control.
03
Industrial cameras
Integration with GigE, USB3 and CSI cameras at the frame rates the process requires.
04
Edge fleet management
Over-the-air updates, monitoring and remote diagnostics across distributed devices.
03Implementation
How we implement it
Latency and reliability budgets are set before model design, because they decide the hardware, the model size and how much processing stays on the device. Models are optimised and tested on the target hardware, not only on development machines.
- Quantisation and pruning are validated for any loss of accuracy.
- Devices report health and model versions to a central view.
- Updates roll out in stages, with automatic rollback.
04Deliverables
What you receive
Latency and hardware specification
Optimised models for target devices
Edge application and update pipeline
Fleet monitoring
Technology
Technologies we work with
- NVIDIA Jetson
- TensorRT
- ONNX Runtime
- GStreamer
- Triton
- MQTT
- Docker
- Balena
Chosen per project and aligned with your existing standards. Listed as technologies we use, not as partnerships or endorsements.
05Use cases
Typical applications
Patterns this capability is designed for, not a list of delivered projects.
01
Inline visual inspection
Edge inference at production speed, without a round trip to the cloud.
Performance depends on your data, hardware and context, and is confirmed during the engagement.
02
Distributed smart cameras
Vision devices across several sites with central monitoring.
03
Real-time decision loops
Closed-loop control with safety-bounded decisions on the edge.
06Related
Where to go next
Industry contexts
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Next step
Scope Edge AI & Real-time for your organisation
Share your process, data and constraints. We will propose an approach, the evidence to collect first and a realistic plan.