The manufacturing industry is undergoing a rapid transformation, driven by increasing complexity, customization, and competition. Quality assurance is a crucial aspect of this process, but it often relies on costly, time-consuming, and inflexible methods. To address these challenges and opportunities, this project aims to create user-friendly End-to-End AI for automated quality control (QC) that can adapt to different environmental conditions and product designs. To achieve high-quality and efficient results, we will use state-of-the-art AI techniques, such as multimodal LLM, zero-shot defect detection, synthetic data generation, and robot motion planning. By developing these innovative and advanced End-to-End AI QC solutions, the project will not only improve the performance and competitiveness of the manufacturing industry, but also reduce its environmental impact and resource consumption. The project will also contribute to the scientific and societal progress of AI research and applications, by fostering collaboration and knowledge exchange among academic and industrial partners, and by sharing its findings, data, and code publicly. The project will thus support the digital transformation and sustainable development of the Swedish industry and society.