This paper presents the development of an automated visual inspection system based on artificial intelligence (AI), designed for quality assessment and cost estimation in the refurbishment processes of industrial electrolyzers. The proposed solution integrates high-resolution cameras, computer vision algorithms, and machine learning models to identify defects and standardize the technical evaluation of components. The methodology involves image acquisition and annotation, training of convolutional neural networks (CNNs), and integration with industrial control systems. Expected outcomes include improved operational efficiency, reduction of human errors, faster cost estimation processes, and enhanced traceability of quality control. The study also discusses technical limitations, implementation costs, and the requirements for integration in real industrial environments.