About StoreCited

Why we built StoreCited

AI-assisted products are changing how people research purchases. When a shopper asks a product question, an answer surface may synthesize information from multiple inputs. StoreCited does not observe that retrieval or selection. It shows which product facts, markup, policies, and content were visible in StoreCited's point-in-time public-page fetch.

A common, fixable website problem is missing or conflicting public information: structured data, clear product attributes, comparison content, visible reviews, and a coherent brand entity. StoreCited records whether those outputs were detected and internally consistent. It does not convert a missing check into a claim about indexing, retrieval, ranking, citation, or recommendation.

What we actually check

We grade concrete public readiness signals — not vague "AEO magic" and not a claim about a proprietary system's ranking inputs. Every scan looks at six categories:

• Structured data — are Product, Offer, Review, FAQ, and breadcrumb facts represented accurately?
• Product-page facts — are attributes, specifications, price, and availability clear on fetched product pages?
• Buyer-question content — do visible FAQs, comparisons, and guides address specific purchase decisions?
• Trust evidence — are reviews, press, and credibility claims visible and verifiable?
• Crawlability — are index controls, canonicals, robots, and discovery files internally consistent?
• Brand/entity clarity — do public pages and Organization facts describe one consistent store entity?

We fetch public homepage, product, collection, and FAQ content, then turn deterministic findings and clearly labeled category inferences into a score and prioritized recommendations.

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

We have a strong opinion about this category: measurement without a repair decision is incomplete. A sampled mention-rate chart does not change a storefront. A dated finding that price, availability, reviews, or Organization facts are absent or contradictory gives an operator something concrete to inspect and verify.

We also refuse to promise placement. No vendor controls a specific engine's output. What StoreCited can document is what its fetch reached, which deterministic checks passed, and which public outputs a merchant can inspect or correct. Indexing, retrieval, ranking, citation, recommendation, traffic, and sales require separate evidence.

The data behind the product

We don't just assert best practices — we publish the method and limits. Our first report was a single June 26, 2026 crawl-and-rules snapshot of a convenience sample of 24 named Shopify DTC storefronts. Visible reviews were detected on 88%; qualifying Review or AggregateRating JSON-LD was detected on 0%; FAQPage markup was detected on 4%. A markup miss does not mean visible reviews are unreadable, and none of these rates measures citations or recommendations.

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.