The integrity of welds in wind turbine monopiles is critical for structural safety, yet, traditional inspection methods, such as time-of-flight diffraction (TOFD) inspections, demand extensive manual interpretation and are prone to variability. This study presents an AI-assisted inspection solution to streamline inspections that integrates a U-Net–based machine-learning model and acceptance or rejection of indications according to ISO 15626:2018 standard. The model is deployed on an edge-computing unit and interfaced via a browser-based visualization tool. TOFD scans of monopile welds are collected using traditional equipment and procedures. The scanned data is passed to the model, which segments the most likely indications and post-processes them according to the standard. A lightweight web application overlays the automatically generated annotations and measurements on raw TOFD scans, enabling inspectors to quickly review the AI suggestions immediately after each scan, significantly reducing the manual burden. The inspector reviews the suggested indications and, if necessary, performs adjustments. The accepted results are used to pre-fill an inspection report, also reducing administrative effort.