Websites are integral to people’s daily lives, with billions in use today. However, due to limited awareness of accessibility and its guidelines, developers often release web apps that are inaccessible to people with disabilities, who make up around 16% of the global population. To ensure a baseline of accessibility, software engineers rely on automated checkers that assess a webpage’s compliance based on predefined rules. Unfortunately, these tools typically cover only a small subset of accessibility guidelines and often overlook violations that require a semantic understanding of the webpage. The advent of generative AI, known for its ability to comprehend textual and visual content, has created new possibilities for detecting accessibility violations. We began by studying the most widely used guideline, WCAG, to determine the testable success criteria that generative AI could address. This led to the development of an automated tool called \name, which extracts elements from a page related to each success criterion and inputs them into an LLM prompted to detect accessibility issues on the web. Evaluations of \name showed its effectiveness, with a precision of 95.2% and a recall of 87.69%. Additionally, when tested on real websites, \name identified an average of 8 more types of accessibility violations than the combination of existing tools.