AI, Data & Emerging Tech

Computer Vision & Sensor Fusion

Vision systems that work on the line and in the field — defect detection, document extraction, object tracking and multi-sensor fusion, deployed to the cloud or onto edge hardware.

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Overview

The demo works. The factory floor is the hard part

Part of our AI, Data Science & Emerging Tech practice.

Vision models rarely fail on the benchmark. They fail when the lighting changes, when a new part variant arrives, when the camera is nudged during a shift change, or when nobody planned how to retrain.

We design for those conditions from the start: data capture that represents real variation, deployment that fits the compute actually available, and a retraining loop somebody owns.

Capabilities

What we deliver

Industrial inspection

Surface defect detection, dimensional measurement, presence/absence and assembly verification, with false-reject rates tuned against the real cost of each error type.

Document & OCR pipelines

Structured extraction from invoices, forms, engineering drawings and scanned archives, with confidence thresholds and a human review queue.

Detection & tracking

Object detection, multi-object tracking, counting and zone analytics for logistics, safety monitoring and process analysis.

Sensor fusion

Camera, lidar, radar and IMU fusion with calibration, time synchronisation and coordinate-frame management — including automotive perception support.

Edge deployment

Model quantisation, pruning and compilation for Jetson, OpenVINO, Hailo, Coral and embedded GPUs, with measured latency and power budgets.

Data & annotation strategy

Capture rig design, annotation guidelines and QA, synthetic data generation, and active learning so labelling effort goes where it changes the metric.

How we work

A predictable path from first call to handover

No two engagements are identical, but the shape is consistent — so you always know what happens next and what you get at the end of each stage.

Engagement models

Define the decision

What the system must accept or reject, and what each type of mistake costs. This sets the metric — accuracy alone is rarely the right one.

Capture representative data

Real variation, real lighting, real defects. Most vision projects that fail, fail here.

Model and measure

Baseline first, then improvement, scored against a held-out set drawn from the actual line or environment.

Deploy and maintain

Edge or cloud deployment, drift monitoring, and a retraining path with a named owner.

Outcomes

What changes for you

A metric tied to business cost, not a leaderboard score.
Latency and power measured on the target device before you commit to hardware.
A retraining loop, so accuracy does not quietly decay after month three.
Honest guidance when classical image processing is cheaper and more reliable than a neural network.

Tools & standards

What we work with

PyTorch
OpenCV
YOLO / RT-DETR
Segment Anything
ONNX Runtime
TensorRT
NVIDIA Jetson
Intel OpenVINO
Hailo
CVAT
Roboflow
Open3D
ROS 2

Questions

Frequently asked

How much labelled data do we need?

Less than people expect for a focused task — often a few hundred well-chosen examples with transfer learning. Breadth of variation matters far more than raw volume.

Can it run without internet on the factory floor?

Yes. Edge deployment with no outbound connectivity is a common requirement and we design for it explicitly.

Do you do automotive perception?

We support perception pipelines and sensor fusion. Safety-critical perception releases run jointly with the V-Model Testing and Functional Safety practice.

Let’s scope your next build.

Tell us what you are trying to ship. We will come back with an approach, a team shape and an honest estimate — we aim to reply within two working days.

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