Guides
Optimize an ecommerce catalog for AI search
Reference guides on structured data, product data quality, feeds, and the surfaces AI agents read. Updated as the standards shift.
Start here
Three guides Lumio hands every new operator first
01 AI commerce foundations The 6 dimensions of AI readiness Lumio scores every product on six dimensions of AI discoverability — and a seventh when brand voice rules are set. Here is what each dimension measures, how scoring works, and how to read a scorecard. Read guide 02 Structured data mastery Product schema for Shopify Every Schema.org Product property that matters for AI surfacing, with Shopify Liquid examples and the variants that go wrong most often in production. Read guide 03 AI search engines Optimizing Shopify products for ChatGPT Shopping What ChatGPT's product crawler reads, what it ignores, and the changes that move products into recommendation rotation. Read guide
All guides by cluster
Seven clusters, one topical map
01
- What AI commerce actually is The phrase 'AI commerce' is used loosely to mean almost anything with an LLM near a product. The useful definition is narrower: when an AI assistant fulfills a shopping intent by citing or recommending specific products from a queryable index. The narrower definition is the one that has implications for a catalog operator.
- How AI agents discover and rank products The mechanics behind ChatGPT, Perplexity, Claude, and Gemini ranking one product over its competitors — what catalog signals each surface reads, where the rankings get decided, and what carries over from search SEO.
- Traditional SEO vs. AI search optimization What carries over from traditional SEO, what's been quietly inverted, and the tactics that no longer move the needle.
- The 6 dimensions of AI readiness Lumio scores every product on six dimensions of AI discoverability — and a seventh when brand voice rules are set. Here is what each dimension measures, how scoring works, and how to read a scorecard.
- How to audit a Shopify store for AI readiness A manual methodology for auditing a Shopify catalog before automating anything — what to check, in what order, what tools to use, and where the gaps usually hide.
- AI commerce glossary A working vocabulary for AI commerce — agentic search, structured data, GTIN, GMC, JSON-LD, and the other terms that come up repeatedly in operator conversations and trade press.
02
- JSON-LD vs. Microdata vs. RDFa in 2026 Three syntaxes for the same Schema.org vocabulary. Why JSON-LD won in practice, the cases where the other two still ship, and the failure modes from mixing formats on the same page.
- Product schema for Shopify Every Schema.org Product property that matters for AI surfacing, with Shopify Liquid examples and the variants that go wrong most often in production.
- Variant handling in product schema The Schema.org variant model is two patterns, not one. Choosing the wrong pattern is why most Shopify catalogs surface only the parent product to AI agents — and why size, color, and material queries return generic matches instead of the right SKU.
- Organization schema: brand identity in structured data Organization schema is where a brand's identity lives in the AI-readable layer — logo, social profiles, contact points, and the cross-references that connect a product to its company. The block most Shopify stores ship as a stub or skip entirely.
- Offer schema: pricing, availability, inventory Pricing, availability, sale states, pre-orders, backorders. Offer schema is small in size and large in consequence — the single block AI surfaces lean on most to decide whether a product is real, in stock, and priced correctly.
- Validating structured data Schema.org Validator, Rich Results Test, manual JSON inspection, and programmatic checks. The validation workflow for ecommerce product schema and the categories of errors that come up most in production catalogs.
- Product schema for WooCommerce What WooCommerce ships by default, what's missing for AI agents, and the filter hooks that close the gap without forking the plugin.
- Product schema for BigCommerce Stencil templates, the JSON-LD that Cornerstone ships, and the Handlebars helpers that close the gap to a complete Product schema.
- Product schema for Adobe Commerce (Magento) The catalog model is different enough to need its own playbook. Module patterns, the EAV trap, headless rendering, and PWA Studio considerations.
- Product schema for Squarespace What Squarespace's commerce templates ship by default, what they leave out, and the code injection patterns that close the gap on a closed platform.
03
04
- Writing product titles for AI agents Product titles optimized for human shoppers often read poorly to AI agents. The structural pattern that satisfies both, and the trade-off that has to be made.
- GTINs, MPNs, and brand identifiers Universal product identifiers are the difference between 'a product' and 'this product.' What each identifier means, when to use which, why even private-label catalogs need GTINs now, and the Shopify patterns that get them into the structured-data layer correctly.
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07
- Apparel: the fashion data stack Apparel has the largest required-attribute set of any Shopify category and the lowest completion rate. Here is the data stack that closes the gap.
- Electronics and consumer tech Model numbers, technical specs, compatibility, certifications. The most attribute-dense category — and the one AI agents reward most for getting attributes right.
- Footwear: the shoe data stack Footwear sits between apparel and electronics — sizing systems multiply by region and gender, materials carry brand weight, and width matters as much as size.
- Jewelry and watches High AOV, certifications matter, materials are the brand. Schema needs to carry stone certifications, metal purity, water resistance, and provenance the standard Product type doesn't model cleanly.
- Toys, games, and baby products Age recommendations, safety certifications, character and franchise data, gender-neutral pressures — the attribute mix changes more by sub-vertical here than in any other category.
- CPG, household, and cleaning Small AOV, high frequency, bulk-quantity attributes (count, size, refills) AI agents read. The CPG category where subscription and replenishment shape the data model.
