How AI engines choose which suppliers to recommend

Seven signals, drawn from what the engines actually cited in our September 2026 beauty sourcing tests.

Published · Updated · 4 min read · BeautyGEO editorial team

Short answer
  • ChatGPT linked to suppliers' own sites; Perplexity leaned on marketplaces and roundups.
  • Category pages with numbers in text were cited far more than home pages or PDFs.
  • Suppliers named repeatedly publish one detailed page per product line.
  • Consistency across site and listings, and third-party confirmation, decide trust.

1. Entity clarity

The engine must be sure which company it is naming. Consistent name, domain, location and product focus across every source.

2. A page for the exact question

Perplexity cited eight pages from one factory site in our tests: product category pages, sub-category pages, buyer guides and landing pages. Not the home page, not a catalogue.

3. Numbers in text

Both engines repeated MOQ figures stated in page text — "3,000 units per SKU", "500 units per SKU". Numbers in images are invisible.

4. Evidence stated precisely

Certifications named with standard numbers; audits named where permitted; capacity with units.

5. Third-party confirmation

Perplexity's sources included Alibaba, Made-in-China, Accio, SourceReady and "top manufacturers" articles. Those pages confirm — or contradict — what your site says.

6. Crawlability

Fast, script-light pages reachable by each engine's search crawler.

7. Freshness

Guides titled with the current year and pages with visible update dates were among those cited.

The underlying data

All eleven answers, prompts and rankings are in the AI Supplier Index.

Free, no obligation

See what ChatGPT, Perplexity and Gemini say about your company — before your buyers do.

We run real buyer prompts for your category and send you the unedited answers, the suppliers being recommended instead of you, and the three fixes that matter most.