AI Shopping Readiness Audit
Sample report using fictional merchant data. Shelf Report does not guarantee rankings or visibility in any AI system. Public pages only. No checkout, cart, account, admin, or payment actions.
Executive Summary (example)
Desert Trail Supply has a clean visual storefront and solid policy visibility. However, structured product data is sparse — no complete Product or Offer schema on most PDPs, and variant availability is not machine-readable. Adding standard schema and exposing stock at the offer level would deliver the largest immediate lift for AI shopping surfaces and search.
Desert Trail Supply has a clean visual storefront and solid policy visibility. However, structured product data is sparse — no complete Product or Offer schema on most PDPs, and variant availability is not machine-readable. Adding standard schema and exposing stock at the offer level would deliver the largest immediate lift for AI shopping surfaces and search.
AI shopping scenario
Buyer prompt to AI assistant:
“Find a durable weekend hiking backpack under $120 that ships quickly.”
Can AI understand this store’s products?
Partially. The store has clear product titles and photos, but variant availability and structured product evidence are incomplete. An AI assistant would struggle to confirm sizing options, stock status, or exact shipping windows from the public product pages.
This report shows exactly which signals are missing and how to make them machine-readable.
62
/100
Grade: C-
Partially ready — significant gaps found
Schema coverage is minimal: no Product or Offer markup detected on 60% of product pages. Policy pages rely on JavaScript rendering, making them invisible to most automated systems. Immediate wins in schema and crawlability could improve the score by 15–20 points.
Readiness by Category
Product Data Quality7/10
Schema / Structured Data4/10
Semantic HTML6/10
Policy Visibility8/10
Trust Signals7/10
Checkout-Path Clarity6/10
Catalog Structure7/10
Agent Navigability5/10
Top Blockers
Missing JSON-LD Product structured data on 60% of product detail pages.
Offer-level availability is not exposed — automated systems cannot tell what is in stock.
Top 3 Priority Fixes
HighAdd JSON-LD Product markup to all product templates
AI systems rely on structured data to accurately compare and index products. Pages without Product schema are skipped by structured search surfaces.
👤 Shopify developer
HighExpose variant-level availability and pricing in offers
Out-of-stock signals are missing at the variant level. Automated shopping systems may recommend unavailable items or skip the catalog entirely.
👤 Shopify developer
MediumMap size, color, and material options into structured variant fields
Buyer queries filter on attributes. Options that exist only in dropdown UI — not in structured data — lose attribute-filtered comparisons.
👤 Shopify developer / Merchandiser
Variant Evidence
14
Variants found
14
With price
6
With SKU
0
With availability
10
Variant images
40%
Schema coverage
Missing variant fields:
availabilitymaterialsizegtin/mpn
availabilitymaterialsizegtin/mpn
SEO + AI Readability Fixes
High No valid sitemap.xml detected, slowing down catalog discovery.
Critical for comprehensive automated indexing of the product catalog.
Medium 118 product images missing alt text.
Needed for multi-modal AI accessibility and image search.
Medium Return policy renders via JavaScript only.
Policy content invisible to non-rendering crawlers; link as static HTML from product pages.
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