Our approach
AI Shopping Answers Are Assembled From Structured Data
When someone asks an assistant what to buy, the answer gets assembled from structured product data and from editorial content that compares options. Brand strength matters far less than in traditional retail search — a well-marked-up product from an unknown brand routinely gets recommended over a poorly-marked-up one from a household name.
That makes structured data the whole game for ecommerce, and it's where most catalogs fail. Not through absence — most stores have some Product schema — but through incompleteness and staleness. Prices that changed three months ago, availability that says in-stock for discontinued items, review markup that doesn't validate.
The second gap is editorial. Models cite sources that help someone decide, not sources that only sell. Stores with genuine buying guides, comparisons, and sizing content get cited in purchase questions; stores with only product pages and a homepage don't.
Typical ecommerce SEO
AI-era ecommerce SEO
Product schema present but incomplete
Full Product and Offer coverage, validated continuously
Prices and stock drift out of sync silently
Dynamic fields wired to live source data
Crawl budget consumed by faceted navigation
Crawl budget directed to products and guides deliberately
No buying-guide or comparison content
Editorial content built because models cite it heavily
Category pages with no entity definition
Categories defined as entities models can reason about
Success measured in organic revenue only
Citation share tracked in product recommendation answers