Structured Data for Wholesale Websites: Making Your Catalogue AI-Readable

The Invisible Catalogue

Wholesale distributors often have extensive product catalogues — thousands of SKUs across dozens of categories. But if that catalogue exists only as a PDF download or behind a login-gated portal, it's completely invisible to AI search tools. Every product you sell that AI tools don't know about is a missed recommendation opportunity.

Product Schema for Wholesale

Implement Product schema on every product category page. For wholesale, the key properties are: name, description, category, brand, offers (including price range and minimum order quantity), availability, and delivery information. You don't need to expose exact trade pricing — a price range or 'from £X per unit' is sufficient. The goal is giving AI tools enough detail to match your products against buyer queries.

Organisation and Capability Markup

Beyond products, implement Organization schema that communicates your business capabilities: areas served, number of years established, delivery fleet size, warehouse locations, and industry certifications. Use the hasCredential property for certifications like ISO 9001, BRC, HACCP, or industry-specific accreditations. AI tools use these signals to assess your suitability for specific buyer requirements — a food industry buyer will be matched with BRC-certified distributors, for example.

The Trade Directory Bridge

Ensure your structured data aligns with your profiles on trade directories like Europages, Kompass, ThomasNet, and any sector-specific platforms. AI tools cross-reference these sources. If your website says you distribute 500 products but your Kompass profile lists 50, the inconsistency undermines your authority score. Audit all directory listings quarterly and update them to match your current catalogue and capabilities.

TradeFlow's Implementation

TradeFlow Distribution implemented Product schema across 200 product category pages, Organization schema on their homepage and about page, and LocalBusiness schema for their three warehouse locations. The technical implementation took two weeks, but the prerequisite — ensuring each product page had sufficient descriptive content — took a month. The investment paid for itself within 90 days through increased qualified enquiries from buyers who found TradeFlow through AI-assisted research.