AI-search readiness, measured

See what your public storefront exposes — then verify the gaps.

When shoppers ask ChatGPT, Perplexity, or Google AI for the best product in your category, do they hear your name? StoreCited does not monitor those live answers. It scans your public storefront, grades observable readiness checks, and models buyer-question hypotheses for you to validate independently.

No login required For publicly accessible Shopify and DTC storefronts

Example buyer questions worth testing against your public pages

best dog bed for anxious dogs vitamin C serum for sensitive skin magnesium for sleep and anxiety non-toxic non-stick pan low-acid coffee for sensitive stomach wide-leg jeans for curvy figures

How it works

From guesswork to a readiness baseline in one scan

No vague “AEO magic.” We report concrete public outputs visible in StoreCited's fetch without claiming another system retrieved, ranked, cited, or recommended them.

We scan your store

Paste your URL. We fetch the public homepage, product pages, collections, and FAQs that a crawler can access.

We grade AI readiness

We check schema, product attributes, reviews, comparison pages, FAQs, and brand-entity facts exposed in StoreCited's fetched public output.

You see the readiness gap

Get a point-in-time readiness score, modeled buyer-question hypotheses, inferred peer candidates to verify, and failed or partial audit checks.

What we check

Public storefront checks you can verify

Google has been clear: AI features still run on core Search — indexing, useful content, and accurate structured data still matter. We audit observable public outputs and show which checks passed; we do not infer whether an external system understood, selected, or cited a product.

See the full methodology

Product (PDP) AI readiness

JSON-LD & structured data

Modeled questions & visible FAQ content

Comparison & buying-guide pages

Review & social proof signals

Brand entity & about info

Crawlability, robots & llms.txt

Mobile & content depth

By industry

Modeled category questions worth validating

🐾

pet brands

Pet shoppers ask specific questions by breed, age, allergy, and behavior. Product pages should state those attributes and their limits in visible, verifiable language.

Audit pet stores

✨

skincare stores

Skincare buyers filter by skin type, concern, and ingredient. Missing or vague ingredient and concern details leave less verifiable evidence for any product-matching system.

Audit skincare stores

💊

supplement stores

Supplement questions are dosage- and goal-specific. Ingredient amounts, certifications, intended uses, limits, and safety facts should be explicit and supported rather than left to inference.

Audit supplement stores

👗

fashion Shopify stores

Fashion shoppers compare fit, material, sustainability, and price tier. Product pages should expose concrete, variant-specific facts and the limits of each claim.

Audit fashion stores

☕

coffee brands

Coffee buyers care about roast, origin, acidity, and process. Put those attributes in clear visible copy and accurate structured fields instead of vague marketing language.

Audit coffee stores

🍳

home & kitchen stores

Home shoppers ask by material, safety, room size, and use case. Missing specifications and comparisons reduce the evidence available for answering those questions accurately.

Audit home goods stores

💍

jewelry brands

Jewelry buyers filter by material, metal, gemstone, and occasion. Clear material, dimensions, care, and compatibility facts make specific requests easier to evaluate.

Audit jewelry stores

💄

beauty & cosmetics stores

Beauty shoppers compare skin context, finish, ingredient, and ethics. Product pages should identify the formula, variant, evidence scope, directions, and claim limits.

Audit cosmetics stores

🏋️

activewear & fitness brands

Activewear buyers care about fit, fabric, support level, and use case. Missing attributes and fit data leave fewer concrete facts for product comparison.

Audit activewear stores

👓

eyewear brands

Eyewear shoppers ask by face shape, lens type, and use case. Explicit frame measurements, lens facts, and limitations support more accurate matching decisions.

Audit eyewear stores

🕯️

candle & home-fragrance brands

Fragrance buyers ask by wax type, scent throw, burn time, and safety. State those facts clearly in visible copy and accurate structured fields rather than vague prose.

Audit candle stores

🍼

baby & kids brands

Parents query by safety certification, material, and age range. Publish those facts and limitations clearly so they can be checked instead of inferred.

Audit baby stores

Frequently asked questions

What is AI search visibility?

AI search visibility describes observed presence, mentions, or citations across a defined set of answer surfaces and prompts. StoreCited does not measure that outcome; it measures point-in-time public storefront readiness and labels the distinction in every report.

How does StoreCited check my store?

Paste your URL and StoreCited fetches public homepage, product, collection, and FAQ content, then grades observable schema, product attributes, reviews, comparison content, and brand-entity checks. The resulting score is a point-in-time readiness model, not observed AI visibility.

Is the scan free?

Yes. The free scan gives you an AI-readiness score, inferred category-peer candidates, modeled buyer-question hypotheses, and observed public-readiness gaps — no login or card. The $49 Full Report expands the audit and adds a 30-day fix roadmap.

Can you guarantee ChatGPT will recommend my store?

No — and be wary of anyone who promises that. StoreCited reports crawlability, structured-data, content, and entity checks from submitted public pages. It does not observe or control retrieval, indexing, ranking, citation, recommendation, traffic, or sales.

Find public-page readiness gaps you can verify

One free scan. Your public-storefront readiness score, three category-peer hypotheses, and the observed audit checks — in about a minute.