seoClarity's Chris Sachs and Aris Vrakas explained how readable pages, shopper language, consistent product feeds, and testing support AI shopping discovery.

A product can have the right material, price, and features and still give an AI shopping tool too little context to recommend it. The challenge is not simply getting a feed accepted. It is making product information retrievable, relevant to shopper needs, and consistent wherever it appears.

In this webinar, AI Shopping: Get Your Products Found In Google & ChatGPT, Chris Sachs, VP of Value Enablement, and Aris Vrakas, Product Lead at seoClarity, walked through four connected moves: make product pages readable, enrich content in shopper language, synchronize feeds and pages, and measure what changed.

The session focused on work ecommerce teams could do now, rather than waiting for checkout inside AI services to mature.

Vrakas began with a technical prerequisite: product content needed to be available to bots, not only visible to someone using a browser. He described JavaScript-heavy pages where shoppers could see details that an automated fetch did not expose.

For teams reviewing their stores, that distinction calls for checking what a crawler actually receives. Are core product details present in the HTML? Does important information depend on client-side execution? Where rendering was a bottleneck, Vrakas discussed prerendering and cached HTML as options.

His point was to address access before investing heavily in enrichment. Better descriptions cannot help a system that never receives them.

The presenters used socks as a recurring example. Color and fiber composition are useful attributes, but they do not answer every question about warmth, thickness, durability, or intended use.

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