Does AEO work if you sell through distributors or resellers?

AEO works when you sell through a channel, and the reason is that assistants answer brand questions and sourcing questions separately. A buyer asking which companies make a thing gets a list of makers. A buyer asking where to buy it gets a channel. Your partners can win the second question while you are absent from the first, which is the expensive version of this problem.

The thing a channel changes is not whether the work pays, it is who owns the pages that describe the product. Manufacturers who sell only through distributors often have a website built for brand and recruitment, not for specification, with the actual detail living in a partner's catalogue or in a PDF. An engine reading that site finds nothing to quote about the product, so it quotes whoever did write it down.

That is a fixable position rather than a structural disadvantage, and the fix is on your domain. Tannenbaum, September 2026, fitted a chronological stage model to 34,960 unbranded prompt-engine observations across 2,854 monitored prompts and repeated GPT and Gemini runs between June and September 2026. Where neither the brand nor its own domain appeared in the observable retrieval path, the brand was mentioned in 2.8% of GPT answers and 3.8% of Gemini answers. Where the brand's own domain was cited, mentions rose to 49.0% and 58.4%. The study comes from a single commercial monitoring vendor and is observational, so nobody should build a forecast on it.

What follows for a channel business is an order of work. Document the product on your own site in the words buyers use, then earn the partner and third-party pages that carry you in the places buyers ask about purchasing.

Who do AI engines name, the manufacturer or the distributor?

Nobody has published a clean measurement of manufacturer against distributor naming, so treat any confident number you see on that question as marketing. What we can offer is the closest adjacent evidence we have gathered ourselves, in a category where buyers ask assistants to name suppliers and the supply chain has several layers.

Our study of how four AI engines answer pharmaceutical process skid sourcing questions, published in 2026 and covering 27 queries across eight categories with five runs per query, found between 73% and 89% of citations pointing at fabricators' own capability pages instead of directories, marketplaces or listicles, consistently across six categories. In that market there was no intermediary standing between a supplier and its citation. The engines read the maker's own pages and named the makers whose pages described what they build.

The same study shows the layer problem in one category. In filtration, the engines led with the firm that makes the filter element rather than the firm that builds the skid: Mott topped combined share of voice at 65%, and its domain was cited 19 times, more than any fabricator in that category. When two companies in a supply chain both have a claim on the answer, the one whose pages document the product in the buyer's words tends to get the citation, whatever the commercial relationship is.

Cross-engine divergence then decides how much work a single reading of one assistant is worth. In the clean-in-place category, that study found IPEC and GEA both on 60% combined share by opposite routes, IPEC named by ChatGPT and Claude but not Perplexity, GEA leading Perplexity and Claude, and Central States Industrial topping Google's AI Overview. No fabricator was named by all four engines. If you check one assistant and conclude your distributor has taken your place, you may simply have checked the engine where your partner is strong.

Why does your own website decide the answer if you never sell direct?

Your own website decides the answer because retrieval runs on pages, not on commercial relationships. An engine writes its own searches, fetches what those searches return, and quotes from the pages in front of it. A product that is only described in a partner's catalogue is a product the engine can only attribute to the partner.

Chen and Koudas, University of Toronto, October 2026, measured how much of the citation decision sits in that retrieval step. Across two 90-day collections totalling 158,099 answers and 468,037 citations, retrieval explained 32% of the within-website variation in ChatGPT's citation counts, with a 95% confidence interval of 24% to 42%, against 2.8% for every page feature they tested combined. Their summary of it is the sentence worth keeping: every page ChatGPT cites is one its own search retrieved.

The second finding in that paper matters more for a brand with no direct sales. The engines partly decide what to search for from what they already know about brands. ChatGPT wrote a brand into its own searches in 67% of cases where that brand was in its prior knowledge for the question, against 11% otherwise, and websites in that prior had 1.60 times the odds of being cited at equal retrieval rank. The authors describe these as associations, not causal effects.

Put together, a manufacturer whose name never appears in the engine's own searches and whose domain holds no product detail is absent at two stages before any page competes. That is the honest case for doing the work on your own site even when every order arrives through someone else. It is also why we tell prospects with no footprint that a page score will overstate how close they are, which our page on third-party mentions goes through.

Should you get your distributors to publish the content instead?

Get your distributors publishing as well as you, not in place of you. Third-party pages are measurably stronger ground for the one page-level lever that works, and your own domain is what gets you into the retrieval path at all, so the two jobs are not substitutes.

The asymmetry is measured. Chen and Koudas, University of Toronto, October 2026, found that matching the question in a page's title and best paragraph lifted citations by +23.6% and +25.7% per standard deviation on third-party pages, against +14.1% and +15.5% within a website generally. On brand-owned pages, no feature they tested passed all five of their robustness checks. So wording a partner's page around the question a buyer asks is worth more per unit of effort than wording your own, which is an uncomfortable finding for anyone selling page work and a useful one for a channel business that already has partners publishing.

What the same paper rules out is treating the partner page as the whole answer. Mentions of a brand that the question did not name were unsupported by any cited page naming that brand in 46% of cases for ChatGPT, which means a share of brand naming comes from model knowledge and not from any retrieved page. A brand that exists only inside somebody else's catalogue entry accumulates very little of that knowledge.

Practically, the version of this that works is a short specification pack you own and your partners reuse: the product's own page on your domain, with the specification, the limits, the application notes and the questions buyers ask, plus the same facts supplied to partners as copy they can publish under their own name. You cannot dictate a distributor's website, but you can make publishing your detail the path of least resistance for them. Getting into the lists and roundups buyers' assistants retrieve is covered in how to get into the best-of lists AI engines quote.

Which pages win when a reseller sits between you and the buyer?

When a reseller sits between you and the buyer, four page types decide most of the answer, and they are owned by different parties. Reading them as a portfolio tells you where your effort goes, because two of the four are yours to write and the other two are yours to influence.

Page type	Who owns it	What it does in an AI answer	What to do about it
Your product and specification pages	You	Puts your domain in the retrieval path, which is the step that separates a 2.8% mention rate from a 49.0% one in the stage model above	Write one page per buying question, in the buyer's words, with the limits stated
Distributor catalogue and product listings	Your partner	Answers "where do I buy it", and often carries the only full specification online	Supply the copy, the spec tables and the application notes, and ask for your brand named in the text rather than only in a logo
Trade directories and buyer guides	A publisher	Gets retrieved for category and shortlist questions, where vendor sites rarely are	Claim and complete every listing, and treat the data fields as copy rather than admin
Comparison and best-of articles	A publisher or a competitor	Supplies the names an assistant repeats when a buyer asks who to consider	Pitch the ones that already rank for your category, with facts a writer can verify

One pattern in that table catches manufacturers out. The pages that answer purchasing questions are usually a partner's, and the pages that answer product questions are usually yours, so a brand that has outsourced its product detail has handed over both. Our own sourcing study is the counter-example worth keeping in mind: in that category the suppliers' own pages carried 73% to 89% of citations, and the firms that had written down what they build were the firms the engines named.

How do you measure AI visibility when the sale happens through someone else?

Measure three things separately: whether the engines name your brand, whether they cite your domain, and whether they cite a partner page that names you. Collapsing those into one number is what makes channel reporting useless, because each of the three has a different fix.

Brand naming without a citation of your domain means the engine knows you and is reading somebody else's page, which is a content gap on your own site. A partner page cited without your brand named in the answer means your detail is there but attributed to the channel, which is a copy problem in the partner's page. Neither of those is visible in a report that counts only mentions.

Run the prompts repeatedly before reading anything into them. Schulte, Bleeker and Kaufmann, in a study of ChatGPT, Gemini, Google AI Mode and Perplexity across 45 to 46 days between 24 January and 20 March 2026, found day-to-day overlap of cited sources averaging a Jaccard similarity of 0.34 to 0.42, while the brands named overlapped more at 0.45 to 0.59. Source lists churn faster than vendor lists, so a partner page appearing or vanishing between two runs is often noise while a change in who gets named is more likely to be real.

Attribution downstream stays hard, and a channel adds a layer to it. If the order arrives through a distributor, there is no referrer to read, and the assistant conversation that started it leaves no trace in your analytics. What works is asking, in the quote form and in the first call, where the buyer heard the name. Our page on tracking leads from ChatGPT covers the mechanics, and tracking AI visibility across engines covers the prompt-set design that produces a denominator you can compare month to month.

What else do people ask about AEO and channel sales?

The five questions below come up with manufacturers and software vendors whose channel sales run through distributors, resellers or marketplaces.