Building an AI-Ready Product Catalog: A Retailer Checklist

Audit checklists for catalog teams tend toward the comprehensive and therefore the unusable. This one is meant to be actionable in a day. It covers the dimensions AI shopping agents actually check before forming a recommendation, in priority order. You do not have to fix everything at once. Start with the items at the top of each section.

Before You Start: What You Are Auditing Against

The evaluation model AI shopping agents use is different from Google Shopping feed requirements. A Google Shopping feed needs: title, description, price, availability, image link, product URL, brand, GTIN or MPN. All of those things are table stakes for AI agents too, but they are not sufficient. AI agents are trying to answer natural language queries about product suitability. They need enough information to say "yes, this product matches what you asked for" with specificity, not just "this product exists and is for sale."

With that in mind, the checklist below is organized around the failure modes we see most often in catalogs that perform well on traditional metrics but poorly in AI agent recommendations.

Section 1: Structured Attributes

Pick 20 of your best-selling SKUs. For each one, ask: if a shopper asked a comparison question about this product category ("What is the lightest option under $150?", "Which model works with X system?", "What is the washable version?"), could the answer be derived directly from the structured attribute data on this listing?

• Are category-specific attributes present? Each product category has a relevant attribute set. Apparel needs size scale, material composition, care instructions, and fit type. Electronics need compatibility, operating requirements, and connectivity standards. Consumables need quantity per unit and unit type. If these fields are absent or only present as unstructured text in the description, mark this as a gap.
• Are attribute values in normalized units? "Weighs about 2 pounds" and "870 grams" and "0.87 kg" are three ways to express the same fact. If your catalog has mixed units in the same attribute field, agents trying to sort or compare will either fail or produce incorrect results.
• Are attributes in machine-parseable format? Attributes embedded in the description paragraph are harder for agents to use than attributes in explicit key-value fields. Both should exist, but structured fields are the higher-value investment.
• Are variants (color, size, material) expressed as structured sub-entities, not as free text in the description?

Section 2: Product Descriptions

• Does the description answer evaluative questions, or just list features? Read the first two sentences of each description you are auditing. If they are benefit claims or emotional language ("the ultimate bag for adventure seekers"), they are not answering evaluative questions. Mark these for rewrite.
• Is the use case specific? "For hikers" is not a use case. "For hikers doing multi-day loaded carries in alpine terrain above treeline" is a use case. Specific use cases help agents match products to intent-specific queries.
• Does the description include at least three specific, verifiable facts that differ this product from similar items in your catalog?
• Is the description free of unsupported superlatives? "Best-in-class performance" and "unbeatable comfort" are not parseable. An agent cannot verify them and will not use them in a comparison response.

Section 3: Schema Markup

• Is there a valid Product schema object on every product page? Use Google's Rich Results Test to confirm. Invalid JSON-LD (missing closing bracket, malformed string) is common and silently fails.
• Are offers.price, offers.priceCurrency, and offers.availability populated with current values? Schema that reflects a sale price that ended two months ago is worse than no schema because it creates a conflict between page content and structured data.
• Are additionalProperty fields populated with category-relevant specifications? This is the most commonly missed field. It is not required for Google rich results but is the primary source of structured specification data for AI agents.
• If you have an aggregateRating on the page, is it also in the schema? Review signal in schema is used by agents as a product credibility signal.
• Is the brand expressed as a Brand entity with a name, not just a string?

Section 4: Title Quality

• Does the title identify the product specifically without keyword stacking? Read each title and ask: if you showed this title to someone unfamiliar with the category, would they know exactly what the product is and how it differs from similar items? Keyword-stacked titles often fail this test.
• Is the brand name in the title? For branded products, the brand name is a disambiguation signal that agents use when comparing products from multiple retailers.
• Is the variant (color, size, specific configuration) in the title if that variant is a meaningful differentiator?

Section 5: Price and Availability Clarity

• Is the current price visible in the page HTML without JavaScript execution? AI agents that index product pages without running JavaScript will see no price if your pricing is rendered client-side only. This is a particularly common issue with headless commerce setups.
• Is the availability status unambiguous? "Ships within 5-7 business days" and "In stock" and "Usually ships in 3-5 business days" all mean different things to an agent trying to determine availability. If the product is genuinely in stock, say so explicitly in a parseable format.

Section 6: Cross-Channel Consistency

• Does the content in your Google Shopping feed match the content on your product pages for the same SKUs? Discrepancies between feed and page confuse agents that index both sources.
• If you have a Shopify store, are your product metafields populated with the same attribute data that appears in structured attributes on the page?

Prioritization: Where to Start

If this audit reveals gaps across multiple sections, prioritize in this order. First, schema validity and availability fields (Section 3, first three bullets) because invalid or stale schema actively harms agent trust. Second, structured attribute completeness (Section 1) for your top 20 percent of SKUs by revenue or traffic. Third, description rewrite (Section 2) for those same high-priority SKUs. Title quality and cross-channel consistency matter, but they do not block AI agent recommendation as aggressively as absent attributes and stale schema.

We are not suggesting you need to complete every item in this checklist before AI agents can recommend your products. Getting to a passing state on the high-priority items for your top-traffic SKUs will produce measurable improvements before you have touched your entire catalog. The goal is the highest-impact fixes first, not perfection everywhere simultaneously.

What This Checklist Does Not Cover

This checklist covers listing quality: the information you provide about your products. It does not cover price competitiveness (if your products are significantly overpriced versus the market, agents will recommend competitors regardless of listing quality), review volume (thin review signal limits agent confidence regardless of listing quality), or demand-side factors (AI agents optimize toward what shoppers actually ask for). Those are real factors but they are outside the scope of catalog optimization. This checklist is specifically about making sure that what you have gets evaluated, not about changing what you have.