When you ask ChatGPT or Perplexity to name a good tool for a job, it isn't running your brand through a scoring formula and printing the winner. It's matching your question to products it can identify, that it can confirm from more than one place, and that it can explain and point to. Then it puts a short answer together from whatever it finds. That's the honest version of how AI recommends software, and it's worth sitting with, because neither OpenAI nor Perplexity has published a fixed formula that scores category fit, reviews, popularity, or page format and spits out a ranked list. What we do have is a fairly consistent, practical picture of how these answers get assembled, and that picture is what this article walks through.
For a SaaS founder, that matters because you can't buy your way onto page one of an AI answer the way you might buy a search ad. But you can influence the three things every recommendation actually depends on: whether the product is easy to place in a category, whether anyone besides you backs up what you say about it, and whether there's a page an engine can point to when it needs to support a claim. Get those three things right and you've done the real work behind how you get SaaS cited in AI answers, even without knowing the exact weights either engine uses. The rest of this piece breaks those three down and gives you a short list of things to go do.
How ChatGPT and Perplexity Build a Software Recommendation
Both tools are answering the same underlying question, "what should this person use," but they get there slightly differently, and the differences matter for what you build.
When someone asks ChatGPT for chatgpt tool recommendations, the system usually works through a few connected steps. It reads the question and figures out the category, the use case, and any constraints (team size, budget, industry). It then searches or retrieves pages that look relevant to that interpreted question, not necessarily the literal words typed. OpenAI's own documentation says ChatGPT can rewrite a user's question into one or more targeted search queries before it goes looking for sources, and that search results are ranked by a mix of factors meant to surface relevant and reliable information. OpenAI doesn't publish that factor list, and it says outright that placement isn't guaranteed. Once it has candidates, it checks whether each one is recognizable as a real, stable entity (does it have a consistent story about what it does and who it's for), then it writes an answer that may name a product, explain where it fits, compare it with alternatives, and cite one or more sources. The page it cites is not always the page that made it decide to recommend you in the first place. That's a subtle point and it trips a lot of people up: being cited and being recommended are related but separate outcomes.
Perplexity runs on a similar shape but describes its own process a bit more openly. Its Pro Search documentation talks about running multiple searches across a mix of sources, articles, academic material, forums, and more, then synthesizing what it finds into one answer. So a chunk of how Perplexity picks software to recommend is about which sources it can reach and trust, not just what's on your own site. Perplexity also separates two kinds of crawler access: PerplexityBot, which is meant to surface and link your pages in its search results, and Perplexity-User, which fetches a page live when someone asks a question that needs it answered from that exact page. If either is blocked by your robots file or a firewall rule, that specific path to being seen closes, even if everything else about your product is a great fit.
The practical takeaway from both is simple. A page that can't be reached can't become evidence in an answer. Being reachable doesn't guarantee you get named, but being unreachable guarantees you don't.
Category Association: Making Your SaaS Legible as an Option
Before an engine can recommend you, it has to be able to say, in effect, "this is a project management tool for design agencies" or "this is a note-taking app for students," and have that statement hold up no matter how the question gets asked. That's category association, and it's the first layer of AI assistant software selection worth working on. Get this layer wrong and the other two barely matter, because the engine never places you in the conversation to begin with.
The useful evidence here is boring in the best way: a consistent description of what the product does, the category and subcategories it sits in, the problem it solves, who typically buys it, and what makes it different from the products next door. What breaks this is contradiction. If your homepage calls you a "content operations platform," your G2 profile calls you an "AI writing assistant," and a review site calls you a "SEO tool," the engine has three different stories to reconcile, and reconciling stories isn't its job.
The content that does this work well tends to mirror how people actually ask software questions: best tool for a specific audience, alternatives to a well-known incumbent, head-to-head comparisons that explain when each option fits, and category explainers that lay out the market and how to think about it. None of this means repeating your category label on every page like a keyword. It means the same underlying facts show up everywhere someone might look, your site, your docs, your review profiles, your customer stories, so an engine pulling from any of them gets the same answer.
Third-Party Corroboration: Why Independent Evidence Carries Weight
An engine trusts a claim more when it's confirmed somewhere other than the company saying it about itself. That's third-party corroboration, and it's the layer most SaaS teams underinvest in because it isn't fully in their control.
The relevant sources include review profiles on platforms like G2, Capterra, TrustRadius, and GetApp, verified customer reviews that describe actual use cases rather than generic praise, independent comparison and category pages, named customer stories, integration and partner directories, and reputable coverage that talks about you in the same category language your buyers use. Review presence isn't a switch you flip to guarantee visibility. It's better thought of as part of the evidence environment surrounding your product, proof that it's actually used, by real people, for the things you say it's for.
There's a distinction worth holding onto here: being mentioned is not the same as being corroborated. A company can publish a great deal about itself and still lack any independent evidence backing it up. On the flip side, a product can get named in an answer because third-party sources establish its category and relevance clearly, even when the company's own page never gets cited at all. If your own pages are the only source of everything you claim, you're more fragile than you think.
Citable Pages: Giving the Engine Something to Point To
The third layer is having pages that are actually usable as evidence. A citable page is public, reachable, and makes a specific claim easy to check: what the product is, who it's for, what it does, how it's different, or what a customer used it for.
At a practical level, this means your site and key content are crawlable, your product and audience language is consistent from page to page, your key claims are stated directly rather than left for the reader (or the engine) to infer, and your pages get updated as the product and pricing actually change. It also means nothing important sits behind a login wall or depends on content a crawler can't render. And it means your own pages are backed up by outside evidence rather than standing alone as the only authority on your own product.
Structured data has a role here, but a limited one. Schema types like SoftwareApplication and Product can help a search engine understand your application's category, platform, or pricing, and can make a page eligible for certain rich results. Google is explicit that this kind of markup doesn't guarantee any particular appearance and doesn't guarantee a citation. Think of it as helping a machine classify what you already say clearly, not as a shortcut around saying it clearly in the first place.
Mentioned Is Not the Same as Cited (or Recommended)
This is the layer where a lot of SaaS teams get confused about their own results, so it's worth defining the terms plainly.
Mention rate is the share of tracked answers that name your brand at all. Citation rate is the share that link to your content as a source. Recommendation rate is narrower still, the share of answers that actively suggest your product for the person's stated need, which is different from just being listed as an example. Share of voice is your visibility relative to the competitors you're tracking against. And cited-page distribution tells you which of your pages are actually doing the work, since it's often not the page you'd expect.
You can have a high mention rate and a low citation rate at the same time. You can also get recommended in an answer while the citation itself goes to a review site, a comparison page, or a piece of documentation instead of your homepage. Neither of those is a failure on its own, but they call for different fixes. If you're absent from the category conversation entirely, that's a positioning and coverage problem. If you're mentioned but nobody backs you up, that's a corroboration problem. If you're recommended but nothing of yours gets cited, that's an accessibility and evidence problem on your own pages. Lumping all of this into one visibility score hides which fix you actually need.
One example of how this shows up in practice: G2 has reported that its own visibility inside AI answers rose sharply over a few months in 2025, and that category pages, best-of listicles, and user reviews were the pages doing most of that work. That's G2's own reported number about G2, not a universal benchmark for every category, but it's a reasonable illustration that review-driven pages can carry real weight in an answer even when the vendor's own site doesn't get the citation.
What to Do About It This Quarter
None of the three layers above respond to a single fix, but there are concrete moves that push on all of them at once.
Start with your positioning. Write one clear, consistent answer to four questions: what the product is, who it's for, what problem it solves, and what makes it different. Use the same facts on your homepage, product pages, docs, review profiles, comparison pages, and customer stories. Don't try to make every page rank for every category you could plausibly claim. If you genuinely serve more than one category, explain the relationship between them instead of presenting two disconnected identities.
Then build content around the way buyers actually ask questions, not just the keywords you'd pick for yourself. That means category-framing questions ("best tool for X team"), comparison questions ("X versus Y"), alternatives questions ("alternatives to X"), and best-for questions split by industry, company size, or role. A reasonable starting set runs 30 to 50 prompts covering all of those shapes, and it's worth revisiting every couple of weeks as your market's language shifts.
Go earn corroboration where software buyers already look. Claim and complete your profiles on the review platforms relevant to your category, and encourage detailed, honest reviews that describe real context and real use, not generic five-star praise. Keep your facts, pricing, category, and audience claims aligned across every one of those profiles and your own site. Conflicting details anywhere in that chain weaken the whole picture.
Publish the pages a third party or an engine can actually use: category explainers, alternatives pages, fair comparisons that say when you fit and when you don't, use-case pages, integration pages, and named customer stories. A comparison page that claims universal superiority is less useful here than one that gives an honest, defensible reason to pick you in a specific situation, because that's the kind of claim an engine can support with confidence.
Remove the access barriers. Check that OAI-Searchbot and PerplexityBot aren't blocked in your robots file, your CDN, or your firewall, if you want to be included in those systems at all. This is a technical prerequisite, not a visibility win in itself, so don't stop here and call it done. If your goal is specifically chatgpt tool recommendations rather than broad AI visibility, this step matters even more, since a blocked crawler removes you from that path entirely no matter how strong the rest of your evidence is.
Finally, measure the same prompt set across the engines that matter to you, on a repeating schedule, and track whether you're mentioned, whether you're recommended, whether your own site gets cited, which third-party domains get cited instead, and how the engine describes you. This is exactly the gap DeepSmith's AEO tracking is built to surface: it runs your prompt set across the AI engines you care about and reports mention rate, citation rate, and share of voice per platform, along with which pages are actually earning your citations. Knowing whether you have a positioning gap, a corroboration gap, or an accessibility gap is what tells you which of the moves above to prioritize first.
None of this happens in one pass. A team that runs this loop, positioning, corroboration, citable pages, measurement, and back to positioning, every few weeks will get SaaS cited in AI answers more consistently than a team that does a one-time audit and moves on. The engines keep re-reading the web, so the evidence you leave for them has to stay current too.
