How SaaS products get named when buyers ask ChatGPT, Claude or Perplexity for the best software: category clarity, comparison pages, G2, crawlable pricing.
Muhammet İLBAŞ 10 min readWhen a buyer asks ChatGPT, Claude or Perplexity for "the best project management software for a 20-person agency", the answer is a shortlist: three to six products, each with a one-line reason. A SaaS product gets onto that list when three things line up. The engine can say in one sentence what category you're in. Pages it trusts, mostly not yours, already list you in that category. And your own facts (pricing, docs, integrations) are on pages a crawler can actually read. This is the SaaS playbook for all three, and for measuring whether it's working.
It sits next to our guides to GEO for ecommerce, local businesses and agencies. Software has its own quirks: buyers compare before they buy, review platforms carry real weight, and so much of a SaaS website is rendered by JavaScript that crawlers often see an empty page.
Software buyers have moved their first research step into chat faster than most audiences. In G2's March 2026 survey of 1,076 B2B software buyers, 51% said they now start research with an AI chatbot more often than with Google, up from 29% in G2's 2025 report. 71% use a chatbot at some point in their research. G2 also reports that chatbots are now the top source shaping buyers' shortlists, ahead of review sites, analyst firms and vendor websites, and that 69% of buyers ended up choosing a different vendor than they first planned because of a chatbot's guidance.
Keep in mind that G2 sells visibility on review sites, so it has a stake in this story. Even so, the mechanism is plain: when the shortlist forms inside a chat window, a product that isn't named never gets a demo request.
Two layers feed every answer. Training memory is what the model absorbed about your category before its cutoff: years of roundups, reviews, docs and threads. Retrieval is what the engine reads live when it searches. It rewrites the buyer's prompt into a few web searches, reads the pages that come back, and writes the shortlist from them. Memory changes when a new model ships; retrieval can change within weeks. (Why the engines then disagree with each other is its own topic: why AI engines recommend different brands.)
For software, retrieval leans heavily on pages that already frame the market as a list. A June 2026 study by the agency DerivateX asked ChatGPT (with web search on) one buyer-style question in each of 40 SaaS categories, ten times each. When ChatGPT recommended a tool, it cited that tool's own website only 11.6% of the time. Of the cited pages, 81.9% were independent blogs and vendor-published content, 8.8% were major media and 8.4% were community pages (mostly Reddit). Every cited page used a list structure. It's a small sample (169 cited URLs, one engine), but it fits what we see in our own citation data: engines cite third parties far more often than the brands they recommend.
One finding looks contradictory at first. G2 says citations from review sites are buyers' top trust signal in AI answers. DerivateX found G2 and Capterra cited zero times. Both can be true, because a citation (a link to a source) and a mention (your name in the answer) are different things. Review platforms shape the words a model uses about you, through training data and through the roundups that quote them, even when the review page itself isn't the link shown. We go into that difference in brand mentions vs citations. For a SaaS team the practical point is simple: you need to be present on all four surfaces below, because each one feeds a different part of the answer.
An engine can't recommend you for "best X" if it can't file you under X. Many SaaS homepages open with a mission statement ("the operating system for modern teams") that gives a model nothing to work with. Write one plain sentence: [Product] is a [category buyers search for] for [audience], starting at [price]. Put it in your homepage hero or first paragraph, your meta description, your About page, your G2 and LinkedIn descriptions and your docs intro. When those surfaces agree, the model is confident about where you belong. Our free positioning sentence tool helps you draft it.
Don't invent a category nobody searches for. If buyers ask for "CRM for startups", the engine will answer that question, and a product describing itself as a "relationship intelligence platform" may not get filed under it. Mention your distinctive angle, but anchor it to the category name buyers actually use.
"[Competitor] alternatives" and "[A] vs [B]" are among the most common SaaS prompts, and engines answer them from pages already written in that shape. Publish your own, and make them honest. Say who each tool suits, give the price of each, and say where you lose. A comparison that only praises you reads as an ad, and models discount ads. The structure that gets lifted into answers is covered in the comparison page that wins AI citations. Our own comparison pages and alternatives pages follow it, if you want a working example.
Your own pages are only half the work. Since engines cite third-party roundups far more than vendor sites, find the "best [category] software" articles that rank today and ask to be included. Give the authors something worth writing about: a clear positioning line, public pricing, a free trial they can test, a short changelog. Getting into those lists is slower than publishing your own page, but it's the surface the DerivateX data says ChatGPT actually reads.
Review platforms do two jobs at once. They put you in a category (the G2 category page is itself a ranked list), and their review text gives the model its adjectives: "easy to set up", "support is slow", "pricey for small teams". Claim every profile, check that the category is right, keep screenshots and pricing current, and ask happy customers for reviews when things are going well for them (after onboarding, after a renewal). Recency matters as much as volume. A profile whose newest review is two years old tells the model the product may have stalled. The full playbook is in reviews and G2 as an AI citation engine. Never buy reviews, and follow each platform's own rules on incentives - fake review patterns are exactly what platforms and engines try to filter out.
This is where SaaS sites most often fail without anyone noticing. Many pricing tables, docs portals and integration directories are rendered in the browser by JavaScript. A December 2024 analysis by Vercel found that none of the major AI crawlers it observed (OpenAI's, Anthropic's, Perplexity's and others) executed JavaScript. They fetch the raw HTML, so anything that appears only after scripts run is invisible to them. Google's and Apple's crawlers, which render pages, were the exceptions. Check yours: open view-source on your pricing page and search for your price. If it isn't in the HTML, many AI crawlers can't see it either.
Community threads matter more in SaaS than in most categories, because they're where software categories get defined. A thread titled "what are you using instead of X?" is practitioners naming tools without being asked, and engines treat that as candid evidence. We cover how that works, and why astroturfing backfires, in why Reddit keeps showing up in AI answers. The useful version is unglamorous: have the founder and the team answer real questions in the communities where your buyers ask them, under their own names, and disclose who they work for.
LinkedIn is harder to judge. We haven't found public evidence on how much engines cite LinkedIn posts for software recommendations, and much of LinkedIn sits behind a login, out of reach of crawlers. Treat it as a way to reach the people who write the roundups, review the tools and start the threads, not as a citation source itself. The same goes for founder podcasts and guest posts: their value is that they put your category sentence into third-party pages engines can read.
Tick what's true today. Each item is a surface AI engines read before they name a product.
To an AI engine you're a homepage with no corroboration. Start with category clarity, pricing and reviews.
Skip "ask ChatGPT about us". Asking for your brand by name tells you whether the engine can describe you, not whether it recommends you. Track 10-20 real buyer prompts instead: category ("best X for Y"), alternatives ("[rival] alternatives"), comparisons ("A vs B") and integrations ("X that works with Y"). Run them on each engine on a schedule, with competitors in the same view. Four numbers carry most of the signal: visibility rate (share of prompts where you're named), share of voice (your share of all brand mentions), position (named first or fifth) and citation share (how often the engine's sources are your pages, or pages that name you). Definitions and pitfalls are in AI visibility metrics worth tracking. Our GEO for SaaS page shows live data on which products the engines name for common software prompts.
Read the numbers per engine. A SaaS product can be Perplexity's default and missing from ChatGPT in the same week, because the engines retrieve different pages. The gap between your best and worst engine usually tells you which surface to work on next.
Type your brand name or domain - we'll line up the questions buyers ask about it.
AI engines recommend software the way a well-read colleague would: from what the category's roundups, reviews and threads say, checked against facts they can read on your own site. Give them a clear category sentence, honest comparison pages, live review profiles, readable pricing and docs, and third-party lists that include you. Then measure per engine, so you know which part of that evidence is missing. Run a free Zene audit to see where your product stands on your real buyer prompts today.
Make it easy for the engine to file you under the right category and give it third-party evidence. Write one plain sentence that names your category, audience and starting price, and repeat it on your homepage, review profiles and docs. Publish honest comparison and alternatives pages, keep G2, Capterra and TrustRadius profiles current, get into the 'best X software' roundups that rank today, and make pricing, docs and integrations readable in plain HTML. Then track real buyer prompts per engine to see what moved.
Yes, though not always as a visible link. G2's 2026 buyer survey reports that review-site citations are buyers' top trust signal in AI answers, while a small June 2026 study of ChatGPT software recommendations found G2 and Capterra were almost never the linked source. Both fit: review platforms shape how models describe a product, through training data and the roundups that quote them, even when another page gets the citation.
Often because the prices are rendered by JavaScript. Vercel's December 2024 analysis found the major AI crawlers it observed, including OpenAI's, Anthropic's and Perplexity's, fetch HTML but don't execute JavaScript, so content that only appears after scripts run is invisible to them. Open view-source on your pricing page and search for your price; if it isn't there, put the tiers, prices and limits into the served HTML.
Track 10 to 20 real buyer prompts - category, alternatives, comparison and integration questions - on each engine on a schedule, with competitors in the same view. Measure visibility rate, share of voice, position and citation share, and read them per engine, because one engine can name you while another doesn't. Asking an engine about your brand by name only shows whether it can describe you, not whether it recommends you.
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