Generative Engine Optimization (GEO) for Manufacturers
Most manufacturers already have the product data. The problem is how it’s structured. When AI platforms like ChatGPT, Perplexity, and Google AI Overviews generate a product recommendation, they don’t crawl PDFs, parse spec sheets, or interpret catalog pages the way search engines once did. They pull from structured, contextual, entity-rich content — and most manufacturer websites simply don’t provide it. That means well-engineered products get skipped, while competitors with better-structured data get recommended instead.
This webinar breaks down exactly how AI engines evaluate and surface product recommendations, where most manufacturer content fails the AI readiness test, and how to close that gap without rebuilding your entire catalog. If your buyers are increasingly turning to AI platforms to find, compare, and shortlist products, your data needs to be structured for that reality. GEO is how you get there.

This webinar is for you if:
Request a complimentary GEO Readiness Audit to understand exactly how AI platforms are currently reading your product data — where your content falls short of AI recommendation standards, which product categories are most at risk of being overlooked, and what specific structural changes will help get your products surfaced by ChatGPT, Perplexity, and Google AI Overviews.

CEO, Commerceshop
20+ years helping manufacturers improve digital performance and close the gap between product data and buyer discovery. Now focused on helping manufacturers make their product content visible to AI platforms and generative search engines before their competitors do.

VP, Commerceshop
Specializes in GEO strategy for complex product catalogs, structured data implementation, and content architecture for manufacturers. Has guided teams through AI readiness audits, schema deployment, and entity-rich content rewrites that earn AI recommendations at scale.