Services

Computer vision that ships, not just scores.

Custom computer vision — detection, classification, segmentation and OCR — from data labelling and model training to deployment on the edge or in the cloud.

A model that hits a benchmark in a notebook is a long way from one that runs reliably on a camera feed in a warehouse. We build computer-vision systems across that whole distance — detection, classification, segmentation and OCR — paying as much attention to labelling, edge cases and deployment as to the architecture itself.

Where we help

Object detection, classification and instance segmentation
Optical character recognition and reading text from images and scans
Custom model training and fine-tuning when off-the-shelf falls short
Data labelling strategy, augmentation and dataset curation
Quality-control and defect-detection systems for production lines
Edge deployment on constrained or offline hardware
Model export and acceleration via ONNX and quantisation
Monitoring for accuracy drift as real-world conditions change

Example builds

Custom networks for hard problems. We design and train custom neural networks for narrow, well-defined tasks — including vision problems where a small model trained on your data outperforms a general-purpose API on both accuracy and cost.

Text from documents and images. OCR sits where vision meets language: lifting figures off a scanned statement or a label off a photo. It anchors our document-AI work, where reading the page reliably is the whole game.

Trained for ROI, not novelty. We only build a vision model when it earns its place. Often the right answer is a focused classifier or detector solving one expensive manual step — not a sprawling system chasing a leaderboard.

How we engage

Computer vision lives or dies on data, so we start there — what you can label, how varied it is, and where the model will actually run. A two-week sprint produces a baseline on your own images and an honest read on feasibility before any larger commitment. We build for the deployment target from day one, whether that is a GPU in the cloud or a constrained device on the edge. See our services for how we work.

Common questions

Do we need a huge labelled dataset to start?: Not always. We use transfer learning, augmentation and pre-trained backbones to get a useful baseline from modest data, then advise where targeted labelling will move the numbers most. We are honest early if the data simply isn't there yet.
Can a model run on-device or offline?: Yes. We export and quantise models for edge deployment so they run on constrained or disconnected hardware — useful on factory floors, in the field, or anywhere a round-trip to the cloud isn't practical.
Off-the-shelf vision API or a custom model?: Whichever serves the goal. A hosted API is the fastest path for common tasks; a custom-trained model wins when the domain is narrow, accuracy matters, or per-call costs add up at volume. We will recommend the cheaper route that clears your bar.

AI & machine learning Document AI & OCR MLOps & model deployment

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Where we help

Object detection, classification and instance segmentation
Optical character recognition and reading text from images and scans
Custom model training and fine-tuning when off-the-shelf falls short
Data labelling strategy, augmentation and dataset curation
Quality-control and defect-detection systems for production lines
Edge deployment on constrained or offline hardware
Model export and acceleration via ONNX and quantisation
Monitoring for accuracy drift as real-world conditions change

Example builds

How we engage

Common questions

Do we need a huge labelled dataset to start?

Not always. We use transfer learning, augmentation and pre-trained backbones to get a useful baseline from modest data, then advise where targeted labelling will move the numbers most. We are honest early if the data simply isn't there yet.

Can a model run on-device or offline?

Yes. We export and quantise models for edge deployment so they run on constrained or disconnected hardware — useful on factory floors, in the field, or anywhere a round-trip to the cloud isn't practical.

Off-the-shelf vision API or a custom model?

Whichever serves the goal. A hosted API is the fastest path for common tasks; a custom-trained model wins when the domain is narrow, accuracy matters, or per-call costs add up at volume. We will recommend the cheaper route that clears your bar.