Computer vision turns cameras and images into business intelligence. From quality inspection on manufacturing lines to facial recognition in security systems and object detection in autonomous vehicles, we build computer vision systems that are accurate, real-time, and deployable at the edge or in the cloud.
Discuss Your ProjectState-of-the-art models achieving 95%+ accuracy on real-world industrial and commercial tasks.
Edge-optimised models that run at 30+ FPS on standard hardware without a cloud round-trip.
Models trained on your data, your use case — not general-purpose demos.
Task specification, labelling requirements, and accuracy targets.
Dataset annotation with labelling tools, quality review, and augmentation.
Model training, fine-tuning, and benchmark evaluation.
Edge or cloud deployment with monitoring and retraining pipeline.
AI & Machine Learning
The DevOps discipline that keeps your ML models working in production.
AI & Machine Learning
Language models that understand your customers, automate your documents, and scale your knowledge.
Architectural BIM, scan-to-BIM, 3D visualisation, and automation — all under one roof.
Common questions about our Computer Vision service.
Transfer learning from pre-trained models (like YOLOv8 or EfficientNet) allows useful results with as few as 500–1,000 labelled images per class for many industrial tasks. We advise on the minimum viable dataset and validate results before you invest in large-scale annotation.
Yes — we optimise models with quantisation, pruning, and ONNX export for deployment on NVIDIA Jetson, Raspberry Pi, industrial edge devices, and mobile hardware. Inference at 30+ FPS on constrained hardware is achievable for most detection tasks.
We design training datasets with controlled variation — different lighting conditions, viewing angles, and background states — and apply extensive augmentation. Models are then evaluated on held-out samples capturing the worst-case production conditions.
For well-defined industrial inspection tasks with consistent imaging conditions, 95%+ precision and recall is achievable. For complex scene understanding with high variability, accuracy targets depend heavily on dataset quality and annotation consistency. We set realistic benchmarks upfront.
Dataset collection and annotation takes 2–4 weeks depending on size. Model training and iteration takes 2–3 weeks. Edge deployment, integration, and testing add another 2–3 weeks. Total: 6–10 weeks for a focused single-task model.
We manage the full annotation workflow using Label Studio or Roboflow, including quality review, inter-annotator agreement checks, and active learning to prioritise the most valuable samples for labelling.
Yes — we build video analytics systems for activity recognition, object tracking across frames, counting, and motion detection. Video inference is more compute-intensive, so we design efficient frame sampling and processing pipelines.
Our team will scope your requirements and come back with a clear proposal within 48 hours.