This study explores the use of large language models (LLMs) in the selection and generation of Schema.org markup for web pages. The proposed artifact leverages Google Gemini 2.5 Pro to automate the generation of schema markup, which may increase search engine visibility and strengthen search engine optimization (SEO) efforts. The research compares the artifact-generated markup with pre-existing, human-generated schema markup from high-traffic websites in the U.S., evaluating syntactic validity and the schemas’ ability to trigger rich results in Google’s search engine result page. The study finds that while the artifact-generated schemas were more complex and longer than their human-generated counterparts, they exhibited a higher error rate, more warnings, and fewer schema and rich results items, suggesting that they could negatively impact search engine visibility. The analysis also reveals performance characteristics, with the artifact processing an average of 7041 input characters per second at an average processing time of 39 seconds, proving impractical for large-scale application. This work contributes to the emerging field of AI-driven schema generation, highlighting both the potential and the limitations of LLMs in producing high-quality structured data. While the results suggest that LLMs, when curated, could assist in schema generation for smaller-scale applications, further research is needed to address issues of error handling, runtime optimization, and scalability.