SERVICE

Computer vision development

Models that read images reliably enough for someone to act on the output.

What this actually is

Computer vision turns pixels into a decision: what is in this image, where, and does it matter. The modelling is the well-understood part. What decides whether a system is usable is everything around it — image quality, labelling consistency, and what happens on the inputs the training set never contained.

Published accuracy figures travel badly. Change the camera, the lighting, the angle or the population and performance moves, sometimes a lot. Any number that matters has to be measured on your images rather than inherited from a paper.

What we do

Data and labelling
Assembling a set that reflects real conditions, with a labelling standard written down — inconsistent labels cap accuracy more often than model choice does.
Model development
Segmentation, detection or classification, chosen for the decision being supported rather than for what benchmarks well.
Validation
Held-out sets that match your distribution, with performance broken out by the conditions you actually operate in.
Deployment
Inference where it needs to run — server, edge device or browser — with the latency and cost that implies.
Monitoring
Drift detection on inputs and outputs, because a camera moved six inches is a silent accuracy problem.

The stack

PyTorchU-Net and transformer architecturesOpenCVONNX RuntimeNVIDIA TritonDICOM and medical imaging formatsDocker

What it connects to

Integration surface is the honest driver of effort — ten systems is not ten times one system.

PACS and DICOM archives
Camera and RTSP video streams
Edge devices — Jetson, Coral
Object storage for image pipelines
Labelling platforms
Alerting and case-management systems

How a project runs

Feasibility

2–3 weeks

Whether your images support the task, tested rather than assumed.

Labelling

2–6 weeks

A consistent labelled set with the standard documented.

Development

4–10 weeks

Models validated on your distribution, reported by condition.

Deployment

2–4 weeks

Inference in place with monitoring and a human review path.

Where teams use it

Healthcare

Segmentation and risk stratification on CT and MRI, with a clinician signing the output.

Manufacturing

Defect detection on a line, where false negatives and false positives have very different costs.

Retail

Shelf and stock recognition from fixed cameras or phone photos.

Insurance

Damage assessment from claim photographs taken in uncontrolled conditions.

Logistics

Label, seal and load verification at handover points.

Security

Event detection from existing camera infrastructure rather than new hardware.

When this is the wrong answer

If your images are inconsistent — varying angle, lighting or resolution — fixing capture is cheaper than compensating for it in the model, and usually more effective.

Labelling is the largest hidden cost and the most common reason projects stall. Budget for it explicitly rather than discovering it in week three.

A model validated elsewhere is not validated here. Any accuracy figure that has not been measured on your data is marketing.

Where an error has real consequence, the output supports a human decision rather than replacing it. That is a design constraint, not a limitation to engineer away.

Proof

NHS-grade clinical AI cutting reporting time by 85%

Deep-learning CT analysis with role-based clinical workflows and automated reporting for lung nodule risk stratification.

>0.85 Dice coefficient92% classification accuracy85% faster reporting

Frequently asked questions

How many labelled images do we need?

For a narrow, well-defined task with consistent capture, often a few thousand. For varied real-world conditions, considerably more. Feasibility answers this in weeks rather than assuming it.

Can it run on-device?

Often yes, with quantisation and a smaller architecture. Edge inference removes latency and data-transfer concerns at some cost in accuracy — a trade-off we measure rather than guess.

What accuracy can we expect?

Nobody can answer that honestly before seeing your images. What we can commit to is measuring it properly and telling you if it falls short of the threshold that makes the project worthwhile.

Can you use our existing cameras?

Usually. Working with installed hardware is normal and it constrains what is achievable, which is better established at the start than after a procurement decision.

Who owns the code?

You do, from the first commit — work happens in your repository under your licence, and the contract assigns IP outright. We keep no rights and build no dependency that makes leaving expensive.

What does it cost?

We do not publish a number, because the honest one depends on scope, integrations and the accuracy bar. Tell us the budget you are working with and we will say what it buys — or say plainly if it does not buy enough.

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