Why it matters
Two distributors carry the same valves. One serves a catalog as server-rendered HTML with normalized specs and Product schema, so Perplexity reads the page and cites it. The other ships a React single-page app where the product data loads after JavaScript runs, so a non-rendering crawler sees an empty shell and skips it. Same inventory. One catalog gets quoted in AI answers, the other is invisible.
For industrial catalogs this gap is wide. Distributor sites often hide thousands of SKUs behind faceted search and lazy-loaded grids, which means the exact spec a buyer asks an engine about never reaches the model.
What makes a catalog AI-ready
- The concrete requirements, not vague vendor labels:
- Server-rendered HTML for every product page, so the specs exist in the response before JavaScript runs.
- Normalized attributes: one unit, one format, one field per spec across the catalog (port size, pressure rating, material).
- Complete specs and part numbers in the page text, not locked inside a PDF or an image.
- Valid Product schema (schema.org) mapped from clean PIM data.
In practice
A hydraulics distributor with 40,000 SKUs renders each product page on the server and emits Product schema from its PIM. Ask an engine to cross-reference a discontinued Parker fitting and it can pull the replacement part number and pressure rating straight from the page, then cite the catalog as the source. The competitor's app-only catalog returns nothing a crawler can read, so it is never cited.