Choosing the pages: We built a GEO test group of one page per key commercial intent, selecting for queries where AI features were already producing a detailed answer and crediting a competitor. Working from a limited set let us validate the approach and measure it cleanly before scaling across the rest of the site.

Restructuring the content: AI Search Optimization starts at the paragraph level. Every meaningful block now opens with a direct answer, and content is divided into short segments on a one-question-one-answer basis, phrased the way customers actually ask. Each topic was reduced to a self-contained passage a model can read without the surrounding page. We replaced general descriptions with verifiable data: alloy grades, available formats and sizes, and the technical characteristics of each processing method.

Markup and internal structure: Markup and internal linking are where Generative Engine Optimization moves from wording to structure. We implemented schema markup so key entities — the organization, its services, its product range, and its service area — are read directly rather than inferred from prose. We then built an internal linking scheme that ties the test group into a single topical cluster with a defined role for every page, mapping product categories to the processing methods that apply to those materials. Each page now holds a ready answer to a core industry question in a form that can be quoted directly.