Check whether public storefront pages expose the merchant trust signals Google Shopping and AI shopping systems need: policies, contact, product data, schema, price, stock, and proof.
The tool is a public-page risk screen. It does not inspect private Merchant Center settings, feed diagnostics, appeal history, legal claims, tax setup, or account status. It checks whether your storefront exposes the evidence a reviewer, crawler, or AI shopping system can see.
It is most useful when a Shopify or ecommerce store has unclear policies, weak support details, missing product facts, schema drift, price/stock ambiguity, or merchant trust concerns.
Shopping systems need enough public evidence to understand what you sell, whether the offer is consistent, and whether shoppers can find material terms like shipping, returns, refunds, warranty, support, and contact paths.
This precheck cannot see private Merchant Center diagnostics or decide whether Google will approve a store. It only finds public-page signals worth fixing before a deeper audit or appeal workflow.
The tool starts with public crawl evidence, then groups findings into merchant trust buckets that can be reviewed before ordering a paid audit.
The store has a return policy page, but product pages and the footer do not make the policy easy to find before checkout.
Product markup exists, but visible stock state and structured Offer fields do not give the same confidence signal.
A contact page exists, but shoppers and crawlers need a clearer support path, business identity, and response expectation.
The free tool finds the public blockers. The paid GMC audit turns the same evidence into page notes, screenshots, fix priorities, and copy/schema handoff recommendations.
Each bucket receives a practical finding, visible evidence, and the first fix to review before deeper manual work.
Each bucket receives a practical finding, visible evidence, and the first fix to review before deeper manual work.
Each bucket receives a practical finding, visible evidence, and the first fix to review before deeper manual work.
Each bucket receives a practical finding, visible evidence, and the first fix to review before deeper manual work.
Each bucket receives a practical finding, visible evidence, and the first fix to review before deeper manual work.
Each bucket receives a practical finding, visible evidence, and the first fix to review before deeper manual work.
The workflow is intentionally narrow: public pages first, visible evidence next, then a practical risk bucket that tells you what to review.
Use the homepage or a product/collection URL. The tool normalizes the URL and checks public pages only.
The precheck looks for crawler access, sitemap discovery, policy links, contact paths, product facts, schema, price, stock, and proof.
Results are grouped into pass, review, and missing buckets so the next fix is clear before a paid manual audit.
If the result shows review or missing signals, a paid audit can turn the blockers into screenshot-grade evidence and repair tasks.
Submit a public store URL. Email is optional. The scan checks public pages first and returns a GMC-style trust signal summary directly on this page.
No. This is a public-page risk screen. It checks visible storefront evidence and does not access your Merchant Center account or guarantee approval.
No. The free precheck uses public pages such as the homepage, product or collection candidates, robots.txt, sitemap, schema, policy links, and contact paths.
The paid audit adds screenshot-grade evidence, page-level notes, product and schema review, AI shopping readiness notes, and a prioritized repair plan.
Yes. AI shopping systems also need clear product facts, policies, contact paths, proof, and schema before they can confidently compare or recommend a store.
Start with missing policy links, unclear contact paths, product price or stock ambiguity, and schema that does not match visible page content.
