How ChatGPT, Gemini and Perplexity Choose Which Suppliers to Recommend

Most AI answer engines work in two steps. First, retrieval: the engine runs something close to an ordinary web search — ChatGPT search and Google AI Mode lean on Bing's and Google's own indexes — and pulls back a short list of pages that look relevant to the question. Second, generation: the underlying language model reads those pages and writes an answer, choosing which sources to name and which to leave out. This two-step process is sometimes called "grounding" (grounding means tying an answer to real retrieved pages instead of the model guessing from memory).

That matters for GEO (generative engine optimization, explained in GEO vs SEO) because it means an engine can only cite a supplier it actually found in that first retrieval step. A company that is invisible to ordinary web search — blocked crawlers, no indexed pages, nothing current — is invisible to the AI engine too, no matter how good its product is.

Some engines add a further step called query fan-out: instead of searching once for the literal prompt, the engine expands it into several related sub-questions, runs retrieval for each, then merges the evidence before writing one answer. A supplier that only answers the exact phrase a buyer typed, and nothing about the related sub-questions an engine fans out to, can still be missed even if the literal page is found.

For a buyer question like 'best contract manufacturer for [product]' or '[part] supplier with short lead time,' AI engines lean on the same kinds of sources a human researcher would check first: industry directories (Thomasnet/Thomas, Kompass and similar), trade associations, review and rating sites, and discussion threads on sites like Reddit where buyers compare real experiences. A supplier's own website matters too, but usually as a secondary confirmation once the engine already has a candidate name from one of those other sources.

That is a structural reason a listing in an industry directory or a trade association can matter as much for AI visibility as it does for a human buyer doing the same research — the engine is reading the same public web that buyer would read, not a private database of manufacturers.

No, and nobody honestly can — not Leadee, not an agency, not a directory. AI engines run the same prompt differently from one run to the next, change their retrieval sources over time, and weigh signals no outside party fully controls. What is realistic is improving the odds: the checklist above removes the reasons an engine cannot find or trust a supplier, and measuring repeatedly over time (see the methodology) shows whether those changes are actually moving mention and citation rates, rather than guessing from one chat answer.

This is one real run, not a benchmark: in a Netbase JSC internal measurement run on 2026-10-23, Leadee tracked 3 web-to-print buyer prompts across OpenAI, Perplexity and Google AI Mode for printcart.com (a product of Netbase JSC) against named competitors, with n=1 (one run per engine, not yet repeated for a confidence interval).

The domains the engines cited most often that week were apps.shopify.com, customerscanvas.com, designnbuy.com, woocommerce.com, reddit.com, teeinblue.com and help.customily.com — marketplaces, platform documentation and a community forum, not any single competitor's own marketing page. That single run illustrates the pattern above (directories, platforms and forums over brand pages) on one real brand; it is an example, not a claim about every industry.