Type "best [your category] for startups" into ChatGPT and you get five to seven company names, each with a one-line pitch. That list is the new first page of Google — except there's no page two. If your company isn't one of the names, you're not losing the click; you're absent from the conversation entirely.
We've already published the checklist for getting an individual page cited — answer-shaped content, Bing indexing, quotable structure — in how to get cited by ChatGPT, and this post won't repeat it. Because a recommendation answer is a different beast: when a buyer asks "what's the best X," ChatGPT doesn't quote your pricing page. It names companies. That makes getting recommended by ChatGPT an entity problem — does the model know your brand belongs in this category? — not a page-structure problem. Different problem, different playbook.
What is a ChatGPT recommendation made of?
A recommendation is a pattern, not a lookup: the model shortlists brands that repeatedly appear next to your category name across roundups, comparison pages, review sites, and community threads.
There is no master list of "best CRM for startups" sitting inside ChatGPT. When the question arrives, the model draws on two things: what it absorbed in training, and what it retrieves live — and per Ahrefs' study of 1.4 million prompts, 88% of the URLs ChatGPT cites come straight from its search index. In both cases, what it finds for a "best X" question is other people's enumerations of the category: listicles, "top 10" roundups, alternatives pages, G2 category grids, Reddit threads where someone asked the same question. The names that keep showing up across those sources become the shortlist. The names that don't, don't.
The data backs the intuition. Ahrefs analyzed 75,000 brands across ChatGPT, AI Mode, and AI Overviews and found brand web mentions were the single strongest correlate of AI visibility — 0.664, roughly three times the correlation of backlinks at 0.218. Read that as the operating principle of this entire channel: AI recommendations run on mentions, not links. Every time your name appears next to your category in a source a model reads, you've cast a vote for your own inclusion. (More verified numbers on this shift live in our AI search statistics roundup.)
The co-mention strategy
So the work is simple to state: be present wherever your category is enumerated. Four surfaces matter most, roughly in this order:
Credible "best X" roundups
Find the listicles that already rank for "best [your category]" and pitch the authors honestly: here's what we do, here's who we're for, here's a login. Many maintain these pages for a living and refresh them quarterly. One inclusion in a well-read roundup is a durable, category-labeled mention a model will encounter again and again.
Comparison and alternatives pages — including your own
"X vs Y" and "[competitor] alternatives" pages are pure co-occurrence: your name, your rivals' names, and the category, all in one document. Don't just wait to appear in others' — publish your own honest ones, naming real competitors and conceding real trade-offs. Ours is at how FirstOrg compares, and it exists for exactly this reason.
Review directories, where relevant
A G2 or Capterra profile puts you inside a structured category taxonomy that models demonstrably read — with your brand filed under the exact label buyers ask about. If your category has an established directory, claim the listing, pick the right category, and gather a handful of genuine reviews. Skip this only if no directory covers your space.
Community threads
When someone on Reddit or in an industry forum asks "what does everyone use for X?", the replies become source material for the next thousand AI answers. Show up as a genuinely useful participant — founders recommending their own product with disclosure do fine; astroturfing gets clocked by moderators and models alike.
Notice what all four have in common: none of them is a backlink campaign. You're not chasing anchor text or domain ratings — you're engineering the co-occurrence of two strings, your brand and your category, in places machines treat as evidence of consensus.
If a human can't classify you, a model can't shortlist you
Here's the failure mode that undoes everything above: positioning that's too clever to file. If your homepage calls you an "AI-powered growth companion," which shortlist should a model put you on? Growth companions isn't a category anyone asks about. Buyers ask for the best content marketing service, the best email tool, the best CRM for startups — plain-language categories with established names.
A recommendation engine can only recommend you as something. That means picking a boring, recognizable category label and wearing it everywhere: your homepage, your directory listings, your roundup pitches, your comparison pages. It can feel like a demotion — you built something novel, and now you're filing it under a label two other companies share. But the label is the retrieval key. You differentiate inside the category ("the only one that's fully managed," "the one built for solo founders"), not by refusing to be in one. The clever positioning line can live in the second sentence; the first sentence belongs to the category.
What your own site contributes
Your site won't be the reason you're recommended — third-party consensus is — but it's where models go to confirm what you are, so it has one job: be effortlessly classifiable. Three pages carry most of the weight.
First, a homepage and about page that state, in plain declarative sentences, what you are, who you're for, and what problem you solve — the category label in the first line, not after the metaphors. Second, comparison pages that place you next to named competitors; these do double duty, helping buyers decide today and feeding tomorrow's recommendation answers with fresh co-occurrence. Third, answer-shaped resource pages that establish you as a source worth retrieving at all — that's the territory of the citation checklist and, in full depth, our guide to improving your GEO and SEO together, or our Search specialist if you'd rather it ran on its own. The two playbooks compound: cited pages make your domain familiar, and a familiar, clearly labeled brand is easier to shortlist.
How do you know if you're being recommended?
Ask ChatGPT, Claude, Gemini, and Perplexity once a month what they'd recommend in your category, and log whether you're named, how you're described, and who else appears.
Make it a ritual: five to ten prompts phrased the way your buyers would phrase them — "best [category] for startups," "[competitor] alternatives," "what should a small team use for [problem]" — run in fresh chats, results in a spreadsheet. Track three columns: named or not, the one-line description the assistant gives you, and the competitors listed alongside. The description column is the underrated one; if ChatGPT calls you something different from what you call yourself, your category label isn't landing, and you know exactly what to fix. The competitor column tells you whose co-mention footprint you're chasing.
How long does it take to get recommended?
Longer than page citations: think in months, not weeks, because a brand becomes recommendable only after mentions accumulate across many independent sources the models read and refresh.
A single page can be indexed and cited within weeks. A brand-level pattern is slower by nature — roundup authors update quarterly, directory reviews trickle in, community mentions build one thread at a time, and models fold it all in unevenly. There's no shortcut through that accumulation, which is precisely why it's defensible once you have it — and why waiting is expensive. Recommendation answers have a strong incumbency bias: the brands already on the shortlist keep getting mentioned because they're on the shortlist, and every month you're absent, that flywheel spins for someone else. With half of B2B buyers now starting research in an AI chatbot, the list is being written either way. Start casting votes for your own name now, while your category's shortlist is still soft.