Connected live product feeds, JSON-LD and fashion search intent to make products easier for AI systems to understand, recommend and convert.
Real-time product feed and JSON-LD implementation generated $420K in monthly AI-referred revenue.
The fashion brand had strong products, but AI-assisted discovery was not yet driving enough measurable revenue.
Customers were searching by style, occasion, material and fit, but collection, product and filtered URLs competed for the same demand. This limited visibility across non-brand shopping queries.
We rebuilt the product search layer around collection architecture, product entities, live feed data, JSON-LD and recommendation-focused content, connecting AI discovery directly to commercial product pages.
The catalog was strong, but organic and AI search visibility fell short because product paths, search intent and machine-readable signals were not built to scale.
Colour, size, fit and material filters created multiple crawlable paths. We separated useful search landing pages from navigation-only combinations.
Style, occasion, material and fit queries needed dedicated destinations across collections, supporting content and products.
AI systems needed current product details, attributes, pricing and availability. We exposed those signals through feeds, structured data and clearer product content.
The opportunity was not more pages. It was stronger product, collection and AI search signals.
We combined technical ecommerce SEO with product search expansion: clean the catalog, map shopping intent, strengthen product signals, and scale the pages that drive discovery.
Controlled faceted crawl paths, canonicalized duplicates, cleaned product indexation and strengthened collection-to-product internal links.
Mapped product type, style, material, fit, colour, occasion and seasonal intent to distinct collection and content destinations.
Synchronized product data and implemented JSON-LD for product identity, offers, availability, identifiers and key attributes.
Built recommendation, comparison and use-case content so AI systems could better interpret why each product fits a shopper need.
We combined technical ecommerce SEO with product-level optimization to improve crawlability, indexation, shopping visibility and discoverability across the apparel catalog.
Separated SEO-worthy fashion collections from filters created for navigation.
Mapped fashion demand into distinct collection and content destinations based on how customers actually shop.
Connected live product information with structured data so critical commercial attributes remained machine-readable.
Structured content around product comparisons, recommendations, alternatives and occasion-based shopping questions.
The combined product-data and GEO program turned AI-assisted product discovery into a measurable commercial channel.
Revenue generated from shoppers arriving through AI-assisted product discovery.
Live product information synchronized across feeds, pages and structured data.
Expanded discovery across style, occasion, material, fit and product-intent searches.
AI search moved from an emerging visibility channel to a measurable source of product revenue.
The combination of stronger product architecture, cleaner indexation, and improved shopping signals created a scalable foundation for product discovery.