Computer Vision Solutions
End-to-end computer vision development — from dataset strategy and model training to production deployment on cloud APIs or edge hardware. Object detection, OCR, identity verification, defect detection, and real-time video analytics.
Vision that works in production
Computer vision has moved well beyond academic benchmarks — today's models power real-world systems that inspect factory output for defects, verify customer identity in milliseconds, count vehicles on a road, and track inventory movement through a warehouse. The challenge is not finding a model that works on a clean dataset — it's building one that maintains accuracy in the messy conditions of real deployment: variable lighting, partial occlusions, low-quality cameras, and distribution shift between training data and production input.
We approach every computer vision project with a data-first mindset. Before training begins, we audit the diversity of your training data, identify underrepresented scenarios, and establish a robust evaluation protocol that tests on held-out data representative of production conditions. A model that achieves 95% mAP on a clean test set but fails on blurry or angled images in production is worse than no model — because it creates a false sense of confidence. We don't ship until the model meets the agreed thresholds on realistic data.
From cloud API to edge hardware
Deployment target matters as much as model accuracy. For applications that can tolerate network latency, cloud-hosted inference (on AWS, Azure, or a dedicated GPU server) is the simplest option. For factory inspection, security cameras, or retail analytics where millisecond latency is critical and connectivity is unreliable, edge deployment is required. We optimise models for edge hardware using TensorRT (NVIDIA Jetson), ONNX Runtime (industrial PCs), or CoreML (iOS) — compressing models without significant accuracy loss using quantisation and pruning.
After deployment, model performance degrades as real-world distribution shifts away from training data. We build an active learning pipeline that continuously collects production examples where the model is uncertain, routes them to annotators, and triggers retraining when a threshold of new data is accumulated — keeping the model accurate without manual intervention.