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Generative Engine Optimization (GEO) rebuilt this Miami metal supplier’s product and service pages into the source AI assistants cite for grade, thickness, and cutting-method questions.
C & R Metals
C & R Metals has supplied and distributed metal in Miami since 1985, serving Miami-Dade, Broward, Monroe, and Palm Beach counties from a 3,000-square-foot showroom on NW North River Drive. The company stocks more than 8,000 types and grades of aluminum, steel, and brass, plus over 4,000 showroom items including fasteners, hardware, abrasives, and paint. Alongside supply, C & R Metals provides waterjet, laser, and plasma cutting, machining, welding, fabrication, and coating services.
The Challenge
Demand in this market starts with technical reference questions: which grade and thickness suit a job, how cutting methods differ, whether an alloy fits a specific application. A growing share of those questions now go to AI assistants, which answer them without sending anyone to a website. C & R Metals had the expertise to answer all of them, but its product and service pages were built in a format language models could not extract from. Direct answers never opened a section, no question-and-answer structure existed, and category descriptions ran to a single general sentence with no technical parameters. The company’s real data — alloy grades, available formats, stock depth — appeared only in fragments, disconnected from the jobs customers select those materials for. AI answers on core industry queries were already being generated. They cited competitors.
We rebuilt a focused test group of pages using Generative Engine Optimization (GEO) — structuring content around how language models select and quote sources. Weekly AI impressions grew 2.4× over the following three months, and the gain held rather than spiking and fading.
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 block now opens with a direct answer, split into short question-and-answer segments phrased the way customers actually ask.
Each passage reads on its own, without the surrounding page. General descriptions gave way to verifiable data: alloy grades, formats and sizes, and cutting-method specs.
Markup and internal structure: Generative Engine Optimization moves from wording to structure here. Schema markup lets key entities — organization, services, product range, service area — be read directly rather than inferred from prose.
Internal linking ties the group into one topical cluster, mapping materials to the methods that process them. Each page now holds a quotable answer to a core industry question.
We measured AI impressions in Google's Generative AI features report from May 18 to August 15, 2026.
Weekly AI impressions grew from 471 in the opening week to 1,140 in the closing week — 2.4× growth
The pages accumulated 10.3K AI impressions across the measurement period
Growth began in late May, immediately after the changes went live
By mid-June the figure settled at 800–850 impressions per week and never returned to its starting level
Metal supply and processing has no meaningful seasonality, which rules out a seasonal explanation for the curve
The result rests on the structure of the content, not a one-time spike — and the same AI Search Optimization approach applies to the remaining product and service sections