Computer vision quality control for production and packaging lines

I design and train a visual inspection model for your specific defect types, then deploy it on an edge camera setup that flags or rejects faulty units in real time. You get a review interface where your QC team confirms borderline calls — and every confirmation makes the model smarter.

The engagement includes a feasibility check on your sample images before any commitment, a labeled dataset built from your line, and acceptance testing against your actual defect rates. Typical deployments reach 98%+ catch rates within the first retraining cycle.