Computer Vision Development Services

It depends entirely on the defect type, image quality and how consistently the defect is defined. Narrow, well-defined defects on a controlled production line are the strongest case, and vision systems run continuously without fatigue. Accuracy on your specific parts can only be established by evaluating against a labelled sample of your own images, which is how we scope these projects. For complex judgment calls, we design systems where AI flags anomalies and humans review edge cases.

Yes - industrial quality control is one of the most common applications we build. We deploy vision systems on production lines that inspect products in real time, detect defects, measure dimensions, verify assembly, and reject non-conforming items automatically. These systems integrate with your existing PLC or SCADA systems. We baseline your current escape rate before deployment so the improvement is measured against your own line rather than a generic figure.

OCR (Optical Character Recognition) converts images of text - from scanned documents, photos, or PDFs - into machine-readable text. Modern AI-powered OCR goes beyond simple character recognition: it understands document layout, extracts structured data from tables and forms, and handles handwritten text. We build OCR pipelines using PyTorch-based models and Hugging Face Transformers that handle complex documents with high accuracy.

Yes - medical image analysis is a specialized area we work in. We build AI systems that detect anomalies in X-rays, MRIs, CT scans, and pathology slides. These systems assist radiologists and clinicians by highlighting regions of interest, measuring lesion sizes, and classifying findings. All medical AI systems we build are designed to support clinical decision-making, not replace it.

We use PyTorch as our primary deep learning framework, OpenCV for image processing and classical computer vision tasks, and Hugging Face for vision-language models and pre-trained vision transformers. For object detection, we work with YOLO variants and custom-trained models. We deploy on AWS or GCP Vertex AI depending on your infrastructure requirements.

A focused computer vision project - like a defect detection system or OCR pipeline - typically takes 6–10 weeks from data collection to production deployment. Medical imaging projects take longer (10–16 weeks) due to data annotation requirements and validation processes. Timeline depends heavily on the quality and quantity of labeled training data available.