About StoreCited

Why we built StoreCited

AI is quietly rewriting how people find products. When a shopper asks ChatGPT for "the best dog bed for anxious dogs" or asks Google's AI Overview which vitamin C serum is safe for sensitive skin, an answer engine picks a handful of stores to name — and skips the rest. We built StoreCited because most stores have no idea whether they're being picked or skipped, and no clear way to find out.

The uncomfortable truth: AI doesn't skip your store because of bias. It skips you because you're missing the signals it relies on — structured data, clear product attributes, comparison content, credible reviews it can actually read, and a coherent brand entity. Those are all fixable. But first you have to see them.

What we actually check

We grade the same concrete signals an AI system uses to understand and cite a store — not vague "AEO magic." Every scan looks at six signal categories:

• Structured data — is your Product, Offer, Review, and FAQ schema present and machine-readable?
• Product-page readiness — are attributes, specs, and descriptions complete enough for AI to reason about?
• Review & social proof — are your ratings exposed as AggregateRating, or hidden inside JavaScript an AI crawler can't run?
• Buyer-question coverage — do you answer the questions shoppers actually ask before buying?
• Comparison & alternatives — is there content that positions you against the options AI weighs?
• Brand entity — can an engine connect your store to a consistent, trustworthy identity?

We crawl your homepage, product pages, collections, and FAQs the way an AI system would, then turn what we find into a score and a prioritized list of fixes.

Our stance: fix the signals, don't sell the hype

We have a strong opinion about this category, and we'll say it plainly: most "AI visibility" tools monitor; few fix. Watching a dashboard tell you your mention rate went from 2% to 3% doesn't change your store. Knowing that your review app renders ratings in JavaScript that crawlers can't read — and getting the exact schema to paste in — does.

We also refuse to promise placement. Anyone guaranteeing you a spot in ChatGPT is guessing, because no vendor controls a specific engine's output. What we can honestly promise is this: we'll show you exactly where your store is hard to read, hard to trust, or hard to find, and hand you the fixes. The rest is up to the engines — and to whether the web starts seeing you cited elsewhere.

The data behind the product

We don't just assert best practices — we measure them. Our first published study ran our audit engine across 24 leading Shopify DTC brands. The headline finding was stark: 88% of those stores show star ratings to human shoppers, but 0% expose review schema that AI can read, and only 4% publish FAQ schema. Rich social proof, invisible to the machines deciding what to recommend.

We publish these numbers openly, with the methodology, so they can be checked, challenged, and cited. As we scan more stores, we'll keep updating the benchmarks — because a tool that teaches trust signals should be transparent about its own.

Who's behind it

StoreCited is built and maintained by the StoreCited team, for Shopify and DTC store owners. We work in public where we can: our research, our llms.txt, and our guides are all open. If you want to reach a real person, the fastest way is our contact page — we read every message.