AI LAB · PHYSICAL AI
Intelligence that sees, guides and acts.
Vision on the production line. Autonomy in the air and field. Inference at the edge. Physical AI is where our models leave the screen and run the real world — in production, not in pilots.
Most AI stops at the screen. Ours verifies every part on the line, keeps flying when the cloud drops, and decides on-device in milliseconds — where latency, safety and real conditions leave no room for a demo.
We build the full stack of physical intelligence — cameras and sensors, edge inference, autonomy, and the integration into PLCs, ground-control systems and field networks that turns a model into a system that runs.
Where we put intelligence to work.
Physical AI across the factory floor, the field and the air — each with its own constraints, all running in production.
Vision on the line
Machine vision as an active control layer on the production floor — not inspection after the fact.
- Part identification & variant verification
- Multi-camera assembly monitoring
- PLC-interlocked error-proofing
- Real-time operator guidance
Autonomy in the field & air
Systems that keep operating when the network doesn't — intelligence that runs on the machine itself.
- Companion-computer (on-board) autonomy
- YOLO edge inference in flight
- Event-first, high-frequency telemetry
- Cloud-independent missions
Intelligence at the edge
Models that run on-device, in real time, under real-world constraints.
- On-device inference on Jetson / edge GPU
- Optimised runtimes — ONNX, TensorRT
- Offline-first, low-latency operation
- Built for variable field conditions
Systems running in the field today.
Built, deployed and operated — autonomous fleets, EV charge networks and automotive production lines.
Edge-first autonomous fleets
We build the ground-control and on-board intelligence to plan, fly and monitor autonomous fleets without depending on the cloud — companion-computer autonomy with YOLO inference at the edge, on an event-first telemetry architecture.
- Cloud-independent autonomous missions
- Defence-grade, high-frequency telemetry
- On-board (edge) inference and decisioning
Vision error-proofing on the production line
Machine-vision systems that identify parts, verify the right variant before packing, and monitor multi-step assembly — interlocked with the line's PLC so wrong parts and missed steps are stopped in real time, not discovered later.
- Variant verification before packing — addressing CKD packing loss reported at ~30%
- Multi-camera assembly monitoring with PLC interlocking — addressing rework reported at ~10% of volume
- Image & event logging for full traceability
EV charge-point orchestration
An orchestration layer that keeps large fleets of EV charge points online, observable and billable — with fault detection and utilization intelligence across a widely distributed network.
- Thousands of charge points orchestrated
- Real-time telemetry, fault detection & billing in production
- Utilization signals across the network
What it takes to run in the real world.
The hardware, models and integration layer behind every deployment.
From problem to live system.
A repeatable path from understanding the environment to running in production.
Use-case study
Understand the process, failure modes, part variations, operator workflow and the business impact we're solving for.
Data collection
Capture image and signal datasets from the actual environment — real parts, real lighting, real field conditions.
Model development
Train and tune the model for the required part, step, defect or behaviour, then optimise it for the edge.
System integration
Bring together cameras, sensors, edge compute, dashboards, operator interfaces and PLC / device communication.
Trial & validation
Run in production conditions — validating accuracy, latency, alerts, interlocking and operator usability.
Go-live & support
Deploy for continuous use with monitoring, remote support and ongoing improvement cycles.