seoClarity Webinar Maps Four Steps To AI Shopping Visibility

A product with the right material, price and features can still fail to surface in AI shopping tools because it gives recommendation systems too little context, according to a seoClarity webinar on getting products found in Google and ChatGPT. Chris Sachs, VP of Value Enablement, and Aris Vrakas, Product Lead at seoClarity, laid out four connected moves ecommerce teams can act on now: make product pages readable, enrich content in shopper language, synchronize feeds and pages, and measure what changed. The session deliberately focused on present-day work rather than waiting for checkout inside AI services to mature.

Why can't AI tools see your products?

Vrakas opened with a technical prerequisite. Product content must be available to bots, not only visible to a person using a browser. He described JavaScript-heavy pages where shoppers could see details that an automated fetch did not expose.

Teams reviewing their stores should check 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 pointed to prerendering and cached HTML as options. His order of operations was explicit: fix access before investing in enrichment, because better descriptions cannot help a system that never receives them.

How should products be described?

The presenters used socks as a recurring example. Color and fiber composition are useful attributes, but they do not answer questions about warmth, thickness, durability or intended use. Vrakas explained that conversational prompts can combine several of those needs in one request.

He recommended connecting product characteristics with the decisions shoppers are trying to make. Rather than leaving an AI model to infer a use case from a short specification, retailers can provide relevant details directly. He also discussed using attributable review evidence to support claims about how a product performed. The opportunity is not limited to long prompts: even a broad request for a good product can lead an AI system to consider more specific attributes, and retailers can anticipate those questions instead of relying on generic descriptions. That product-enrichment discussion appears at 28:18 in the on-demand clip.

Why do feeds and pages need to match?

Sachs warned against treating the product feed as a separate project owned elsewhere in the organization. Information added to a feed should also inform the product page and its structured data, rather than stopping at the feed.

Vrakas explained why consistency matters when a platform revisits a product page: product identity, price and supporting details must be clear and coherent. An enriched feed does not remove the need to make the corresponding page understandable. The presenters called this closing the loop — connecting SEO, merchandising, paid media and development so that one channel's improvements benefit another.

How do you measure AI visibility?

The presenters separated measurement into three distinct areas:

• Product-grid visibility: how often priority products appeared in shopping carousels for relevant queries.
• AI mentions and citations: whether an answer named a product or cited its supporting content, and how the brand was described.
• Paid-feed performance: impressions, clicks and conversions from feed-driven campaigns.

These are different signals, not interchangeable definitions of success. An AI mention alone does not establish a sale. The measurement discussion appears at 11:38 in the on-demand recording.

Sachs tied the measurement work to ongoing experimentation: "The best SEO teams are the ones that obsess about testing. The more testing you do, the better decision making you can have." The quote appears at 45:16 in the on-demand clip.

Is before-and-after comparison enough?

During the Q&A, a viewer asked whether a before-and-after comparison suffices to evaluate generative engine optimization. Vrakas distinguished large catalogs, where product groups can support comparisons, from smaller sites or broader brand questions. For the latter, he described improving content around a defined topic and comparing its movement against topics left untouched. The useful distinction is having a comparison set, rather than assuming every visibility change proves an optimization worked.

Where should teams start?

The webinar ended with a concrete first review plan:

• Check what bots can retrieve from a small set of priority product pages.
• Identify shopper questions those pages do not answer, then add details supported by actual attributes or review evidence.
• Confirm who owns the feed and how approved changes flow back to product pages and structured data.
• Record separate baselines for product-grid visibility, AI mentions and citations, and paid-feed results.
• Choose a defined change and an unchanged comparison group before rolling out more widely.

Search Engine Journal has announced its next session, a live website audit titled "SEO Fixes for AI Search Visibility in 2027," where founder Loren Baker will audit a real website and show how he prioritizes fixes for Google and AI search.