Custom computer vision models that detect, classify, and understand visual information - turning cameras into intelligent business instruments.
99.2%
Avg Accuracy
50ms
Inference Latency
120+
Models Deployed
Capabilities
From image classification to real-time video analytics - end-to-end computer vision development.
Real-time detection and multi-object tracking across video streams - identifying, labelling, and following objects with sub-50ms latency.
Custom classification models trained on your specific visual categories - from product defect grading to medical imaging to satellite analysis.
Pixel-level scene understanding that maps every part of an image to a class - essential for autonomous systems, surgical AI, and construction monitoring.
Automated defect detection on production lines that catches surface flaws, dimensional deviations, and assembly errors at machine speed.
Identity verification, age estimation, emotion recognition, and liveness detection - built for access control, KYC, and retail analytics use cases.
Structured extraction from documents, forms, receipts, and handwriting - turning visual content into clean, actionable data records.
0+
Models Deployed
0.2%
Average Accuracy
0ms
Inference Latency
0 industries
Served
Industry Applications
Manufacturing
Defect detection on assembly lines with 99.4% accuracy
Retail
Shelf audit automation, footfall analytics, shrink prevention
Healthcare
Radiology analysis, wound classification, surgical guidance
Logistics
Package damage detection, label reading, warehouse robotics
Security
Perimeter monitoring, anomaly detection, access control
Agriculture
Crop disease identification, drone-based field mapping
Methodology
Define the exact visual task: what the model sees, what it must output, accuracy thresholds, and edge cases that matter for your application.
Curate or capture training images, run labelling pipelines (bounding boxes, polygons, classifications), and apply augmentation strategies to maximise coverage.
Train on state-of-the-art architectures (YOLO, EfficientDet, SAM, ViT), benchmark against your accuracy targets, and optimise for your inference hardware.
Deploy to cloud, on-premise server, or edge device (NVIDIA Jetson, Raspberry Pi). Integrate with your camera infrastructure, PLC systems, or web APIs.
Questions
It depends on task complexity. Simple classification can work with 500-1,000 images per class. Object detection typically needs 2,000-10,000 labelled images. We offer data augmentation and transfer learning techniques that can significantly reduce labelling requirements.
Yes. We specialise in model quantisation and compilation for edge hardware - NVIDIA Jetson, Intel Neural Compute Stick, Raspberry Pi, and custom FPGA/ASIC deployments. This eliminates cloud latency and keeps data on-site.
For well-defined defects with good lighting and consistent camera setups, we regularly achieve 99%+ precision/recall. The key factors are labelling quality, lighting consistency, and edge case coverage in training data.
Yes. We build video understanding systems including real-time object tracking, action recognition, anomaly detection in video streams, and multi-camera scene analysis.
We implement active learning pipelines: when a model encounters low-confidence predictions, those cases are flagged for human review, labelled, and fed back into training. The model continuously improves in production.
Share your visual inspection or recognition challenge and we'll design the model architecture in a free discovery call.