We develop, train, benchmark, and deploy deep learning vision models for automated visual quality control (QC) and surface defect inspection in manufacturing. We demonstrate edge AI smart cameras, embedded neural network inference, and streamlined dataset annotation workflows. The purpose is to help production and quality managers replace slow, subjective manual inspection with high speed AI vision, achieving over 99 percent defect classification accuracy and cutting scrap rates by up to 20 percent.
A documented AI development and testing outcome for the selected quality control application, including trained model results, evaluation of smart camera performance, and recommendations for implementation, further development, and user adoption. This process accelerates deployment time by up to 60 percent, targets defect detection accuracy above 99 percent, and builds on validated manufacturing cases in metal stamping, plastic molding, and pharmaceutical packaging to eliminate manual inspection costs.
The service is delivered as an AI development and implementation process including clarification of the quality control task, preparation and annotation of relevant data, development and training of AI models, testing of model performance and smart camera solutions, and delivery of documented results, demonstrations, and user guidance.
Manufacturing, Quality control and inspection, Machine vision, Robotics and automation, Industrial AI applications