By Lana Plouffe, connexion.me
We asked ChatGPT (gpt-5.5) and Perplexity (sonar) one buying
question — "what AI-visibility / AEO monitoring tool should I use" — in 24 different
phrasings, written before any run and with no vendor name in any of them. Each engine was
called through its public API with a near-empty system prompt: "answer the user's question
directly and concisely." Neither engine was asked to produce a list or rank anything. 24
phrasings × 2 engines = 48 answers, all published verbatim.
Perplexity returns a source list with every answer — a set of URLs it says it read before answering. ChatGPT's API returned no such field on any of the 24 calls: no citation, grounding, or search-result data of any kind. So for ChatGPT we only have the outcome (who got named); for Perplexity we also have the inputs (what it read). Run A's 24 Perplexity answers carried 472 citation URLs across 301 distinct domains. A domain cited three times in one answer counts three times toward "citations"; "answers" counts how many of the 24 answers cited that domain at least once, regardless of how many times.
This is one phrasing-family of one question, measured on one day (13 August 2026), through two engines' public APIs. A different phrasing family of the same underlying question can return a different set of products — we've measured this directly on our own boards: SuiteCRM is named in 83 of 132 answers on one phrasing family and in 0 of 132 on another, same products, same day, same extraction. The full run was independently repeated once (run B, same 24 questions, same engines): the top ten vendors matched 9 of 10 between runs, so the top of the list is stable and the tail (11th place and below) moves. Being cited is not the same as being recommended — a URL in Perplexity's source list is a page the model read while answering, not a page it endorsed. All of this is checkable: the raw answers, the per-answer citation lists, and the extraction rules are linked at the bottom.
Ranked by how many of the 24 Perplexity answers cited them:
| Domain | Cited in (of 24) |
|---|---|
| reddit.com | 15 |
| linkedin.com | 10 |
| blog.hubspot.com | 8 |
reddit.com and linkedin.com were each cited in more answers than any single vendor's own domain. Third was blog.hubspot.com — not one post, seven different HubSpot blog URLs, one cited per answer in 8 of the 24 answers (one URL repeats once). No individual vendor's website beat any of these three domains on answer-count.
Separately from citations, we extracted which vendors got named in the 48 answers (a mechanical rule: a product appears in the count if and only if an engine named it; spelling variants folded, no hand-merging). 50 distinct vendors were named across 48 answers in run A. The top three — Profound (21 mentions, 43.8% of answers), Peec AI (20, 41.7%), Otterly.AI (19, 39.6%) — together outscored the other 47 vendors combined. Forty of the 50 were named once or twice out of 48 chances.
If a domain is being read by Perplexity more often than most vendors are being named by either engine, that domain is doing some of the work a vendor's own site isn't. We don't have data on why reddit.com and linkedin.com specifically get retrieved this often for this question — the citation files show what was read, not why the retrieval system picked it. What we can say is narrower and checkable: for this question, on this day, individual vendor domains did not out-cite reddit.com, linkedin.com or the HubSpot blog, and 40 of 50 named vendors were named only once or twice.
The same method runs across 43 categories on connexion.me, each with its own board, mention counts and citation list. Your category's own numbers, including which domains got cited for it: free lookup, no signup. Full method, every question, every answer and citation file for this run: the AEO board.
I run Connexion — one person, publishing what the answer engines actually say. Corrections and questions go to lana@connexion.me and I answer them myself.