Public pages, status codes, titles, headings, internal links, robots, sitemap, visible product content, and reachable policy pages.
Audit methodology
Verified ecommerce audits need evidence boundaries.
Ecommerce audits get unhelpful when crawl facts, Product schema, Google Merchant feed rows, checkout evidence, analytics screenshots, live AI answers, and reviewer judgement are blended into one score. AI Store Audit keeps those layers separate so the report is useful, honest, and fixable.
The evidence layers
Each layer answers a different question. Keeping them separate makes the audit easier to trust, route, and retest after fixes ship.
Product, Offer, Organization, review, shipping, return, and merchant-quality fields compared against visible page content.
Crawler access decisions and paid-audit prompt worksheets for ChatGPT, Perplexity, Gemini, and buyer recommendation tasks, kept separate from SEO and commerce facts.
CSV rows, checkout screenshots, Google Merchant feed exports, payout records, analytics IDs, and other operational evidence are labeled before a reviewer recommends a route.
Reviewer judgement flags weak trust claims, missing proof, thin comparison pages, and repair priority after the crawl is complete.
Fix Pack suggestions are labeled as recommendations, not proof of future AI citations, rankings, or guaranteed sales.
Each serious fix needs an acceptance check: visible page change, schema validation, crawl recapture, prompt retest, or conversion-path review.
What the audit will not claim
These boundaries make the offer safer to sell and easier for store owners to evaluate.
Trust rules
The report can show blockers, weak signals, and recommended repairs. It should not claim guaranteed rankings or guaranteed AI recommendations.
- No guaranteed ranking, AI citation, or ChatGPT recommendation claims
- No pretending a source-discovery result is a live model answer
- No mixing Merchant Center account verdicts with public-page risk screens
- No treating llms.txt or AI files as replacements for crawlable HTML and structured data
- No generic blog advice when product, collection, policy, or comparison pages are structurally weak
Start with a checklist before sharing private evidence.
Build a public-page audit checklist first. Only move into a reviewed Truth Snapshot or focused audit when the checklist shows a blocker that needs CSV, screenshot, feed, checkout, payout, or tracking evidence.