// TECHNICAL_CAPABILITY

Fields of
Expertise

The architectures we build with, what each one is chosen for, and how they are made to run on the hardware you already have.

visibility

Perception is the prerequisite for autonomy. Everything we build is a way of turning raw sensor data into a decision a machine can act on.

center_focus_strong DETECTION
gesture SEGMENTATION
thermostat THERMAL
deployed_code GEOMETRIC

Selection, Not Fashion

The Architecture Follows the Constraint

A conveyor running at line speed and a bench inspecting a surgical instrument are different problems, and they do not take the same model. We choose architecture from the constraint that actually binds — frame rate, defect scale, available labels, or the compute already bolted to the machine.

HOW WE DEPLOY IT arrow_forward

Model Architectures

01 / CHOSEN BY CONSTRAINT
0.1 YOLO family Real-time multi-class detection at production-line speed, in low light and dust.
0.2 U-Net Pixel-level segmentation of irregular, unbounded degradation — corrosion, fouling, peel.
0.3 SAM Promptable segmentation for asset types with no labelled training data yet.
0.4 Vision Transformer Global context over fine-grained texture — tool wear and surface finish prediction.

Running on Your Hardware

02 / EDGE CONSTRAINTS
memory

Edge Optimisation

Models are compressed and quantised for the compute already installed in your facility.

speed

Latency Budget

Inference frequency is treated as a hard constraint from scoping onward, not tuned afterwards.

thermostat

Thermal & Memory

Validated against sustained load, so throttling and memory leaks surface before deployment.

All three are proven in Hardware-in-the-Loop testing against your exact edge processors — see Phase 03 of our pipeline.

Know which architecture you need — or want us to work it out?

Bring us the constraint and the data you already have. We will tell you what is achievable before you commit to a build.

Talk to an Engineer arrow_forward