AEO and GEO for SaaS: Getting Your Product Recommended by ChatGPT

AEO for SaaS means getting your product named when a buyer asks ChatGPT for the best software in your category. It takes open doors for AI search crawlers, pages that state real product facts, and third-party proof.

Try something before you read the rest of this. Open ChatGPT and type the prompt your best customer would have typed the week before they found you. Something like “best expense software for a 50-person company on NetSuite.” Count the products in the answer.

Is yours there?

If it isn’t, the reflex is to blame the model. Don’t bother. It didn’t decide you were bad. It searched, read whatever it could reach, summarized what it found, and your product simply wasn’t in the pile it was handed.

I say this to founders constantly, about sales teams, about CRMs, about hiring. AI scales whatever systems already exist, good or bad. An answer engine is no exception. Thin pages, blocked crawlers and no outside proof get scaled too. Just in the wrong direction.

What Happens When a Buyer Asks ChatGPT for the Best Software

Nobody outside these companies sees the full pipeline. Google is the one platform that has written its version down in detail, in its May 2026 guide to generative AI features on Google Search, and it names two steps.

Retrieval comes first. The system pulls relevant pages from its index and grounds the answer in them. Then comes query fan-out, where the model fires off a set of related searches to fill the gaps in the original question. Google’s own example turns a weedy lawn into separate searches about herbicides, chemical-free removal and prevention.

Now run that on a software prompt. “Best expense software for a 50-person company on NetSuite” doesn’t get answered as one search. It splits. What each candidate costs. Which ones sync with NetSuite, and how. Which ones fit a company that size. What users complain about. Every one of those sub-searches is a chance for one of your pages to be retrieved, or for a competitor’s page to be retrieved instead, and the answer your buyer reads gets assembled out of whichever pages won those small contests.

ChatGPT’s door is a crawler. OpenAI’s crawler documentation doesn’t hedge. Sites opted out of OAI-SearchBot “will not be shown in ChatGPT search answers, though can still appear as navigational links.” No crawl, no citation.

Are buyers really shopping this way? G2’s 2026 AI Search Insight Report, a March 2026 survey of 1,076 B2B decision makers across North America, EMEA and APAC, found 51% now start software research with an AI chatbot more often than with Google. 69% said a chatbot led them to a different vendor than they’d planned on. And 33% bought from a vendor they had never heard of before.

Read those with one caveat. G2 runs a software review site, so it has a stake in how this story gets told. The direction still lines up with what you’ll see when you run your own category prompt.

AEO for SaaS, Defined Without the Hype

Answer engine optimization (AEO) and generative engine optimization (GEO) describe the same work, which is making a product easy for AI assistants to find, understand and cite when someone asks a question it answers. For a SaaS company, that question is usually a recommendation prompt like “best X software for Y,” or a comparison like “X alternatives.”

Two acronyms. One job. Don’t pay for it twice.

Google goes further than that. Its guide says that, from Google Search’s point of view, “optimizing for generative AI search is optimizing for the search experience, and thus still SEO.” Then it lists what you can skip, at least for Google:

• An llms.txt file. Google Search ignores it, and Google says keeping one for other systems neither helps nor hurts you there.
• “Chunking” pages into tiny pieces for machines. Not required.
• Rewriting your copy in some special AI dialect.
• Special schema for AI features. There isn’t any, though structured data still earns rich results, so keep yours.
• Chasing inauthentic mentions around the web, which Google says “isn’t as helpful as it might seem.”

None of that surprises me. If you’ve read our explainer on what answer engine optimization is, it’s the same conclusion from a different angle. The foundation is ordinary, well-run search work.

But Google is one door. ChatGPT, Claude and Perplexity run their own search crawlers, and Microsoft says Copilot runs on Bing’s index. The foundation is shared. The doors aren’t.

Check the Door First: Can AI Search Crawlers Reach Your Site?

Start here, because it’s binary. Every one of these platforms separates the bot that feeds its search answers from the bot that collects training data, and robots.txt treats them as separate decisions.

AI Search Crawlers by Platform

Robots.txt is the easy part. Firewalls are where it quietly breaks.

Here’s a hypothetical, though a plausible one. A SaaS team reads about AI training in 2024, switches on a “block AI bots” setting in its CDN, and moves on. Robots.txt still says come in. The firewall says no. Nobody reads the logs, and a year and a half later the founder can’t work out why Perplexity keeps recommending three competitors and never them.

Perplexity’s crawler docs include a whole section on letting its bots through a web application firewall, with step-by-step rules for Cloudflare. Nobody writes that section for a problem that never happens. Anthropic’s help article on its bots adds a wrinkle worth knowing. Blocking its IP addresses may not work as an opt-out, because that stops the bot from reading your robots.txt at all. Say what you mean in the file.

Pull a week of server logs. Search them for OAI-SearchBot, Claude-SearchBot, PerplexityBot, Googlebot and Bingbot, and look at the status codes those requests got back, not just whether the requests showed up in the file at all. A wall of 403 errors is your first fix, and it costs nothing.

One more thing, so nobody misreads this. Blocking training bots like GPTBot or ClaudeBot is a legitimate business choice, and I’m not telling you to allow them; OpenAI treats search and training as independent settings. Block training if you want. Just don’t block search by accident.

Build the Pages a Buyer’s Prompt Fans Out Into

Access gets you read. It doesn’t get you recommended.

Go back to the fan-out. A recommendation prompt splits into questions about price, fit, integrations and alternatives, so the pages that get retrieved are the ones that answer those questions plainly. Answering them across dozens of integrations is the job of programmatic SEO for SaaS, which covers which page types a template should build first. That’s the core of AEO for SaaS. It isn’t glamorous.

• A pricing page with numbers on it. If your plans really are quote-only, say what drives the price, whether that’s seats, usage or modules. A prompt asking for something “under $15 a seat” has nothing to match on a page that never states a price.
• One page per real integration. “Integrates with Salesforce” is a logo. A page that says which objects sync, in which direction, how often, and what doesn’t sync at all is an answer.
• Who it’s for. And who it isn’t. Saying who you’re a bad fit for is the fastest way to be believed about who you’re a good fit for.
• Comparison pages against the names buyers actually put next to yours, HubSpot or whoever it is in your category. Fair ones. A comparison where you win every row reads like an ad, and buyers discount it like one.
• Public, crawlable docs, limits and a changelog, since a page behind a login can’t be cited.

Microsoft’s Bing team describes the parsing side more mechanically. Their October 2025 guidance on inclusion in AI search answers explains that assistants like Copilot break a page into smaller pieces, evaluate those pieces, and assemble an answer from several sources. Direct question-and-answer pairs, they write, can often be lifted “word for word.” Clean lists and comparison tables travel well. A wall of adjectives doesn’t travel at all.

Now the warning, because this is where SaaS teams overdo it. It’s tempting to spin up a page for every variation of every prompt you can imagine, and Google’s guide calls that out by name. Creating content for fan-out variations primarily to manipulate rankings or AI responses, it says, violates its scaled content abuse policy.

More pages aren’t a better system. They’re a bigger mess.

My rule is simple. Would a sales rep send this page to a buyer who asked the question? Build it. If the only reader you can picture is a crawler, don’t.

The Third-Party Layer: Earned, Not Seeded

Your own site can’t vouch for itself. Buyers know that.

Google’s guide says its AI features can show what’s being said about products and services across blogs, videos and forum discussions. Reviews, Reddit threads and real roundup articles about you are part of the evidence. Planted ones aren’t worth the effort, by Google’s own account.

Then there’s G2’s interview work. Thirty-nine software marketers talked to them, several said they’d watched chatbots pull from review sites while building recommendations, and some responded by asking customers for longer reviews (the problem, plus the tools they tried first). I’d do the same thing.

Why longer? “Great tool!” is noise. A reviewer saying their team “cut month-end close from nine days to four” (my example, not a real customer) wrote a sentence somebody can actually repeat, which is more than a star rating has ever done for a buyer trying to decide.

I tell MSP owners the same thing about referrals, by the way. Hoping doesn’t produce them. Asking does, at the same point in every account, with a name attached to the asking. Day 60 after go-live works. So does the first renewal call. Pick one, write it into onboarding, and check it monthly like any other number on the scorecard, right next to churn and expansion revenue, so it never depends on somebody remembering to ask.

The source mix for service firms is laid out in how AI platforms decide whom to recommend. Same machinery for software, different inputs, with review sites and product communities sitting where a service firm’s local directories would be.

Measure AEO for SaaS Against Pipeline

My problem with how this gets measured today? Everybody’s selling a “visibility score,” the screenshots look great in a board deck, and hardly anyone can tell you whether one buyer booked a demo because an assistant mentioned them.

1. Write a fixed panel of 20 prompts in your buyers’ words: the category, the company size, the integration they care about, the tool they’re leaving. Don’t edit them month to month. A moving panel measures nothing.
2. Run the panel in ChatGPT, Claude, Perplexity, Copilot and Google’s AI Mode. Log three things per prompt. Were you named? Which sources got cited? Who got named instead?
3. Pull the platform data that exists. Google points site owners to the Generative AI performance report in Search Console, and Bing Webmaster Tools added an AI Performance report for Copilot citations in February 2026. Add GA4 referral traffic from chatgpt.com and perplexity.ai.
4. Ask the buyer. Put “AI assistant” on the “how did you hear about us” field and have reps ask on the first call. Self-reported and imperfect, sure. It’s also the only number that ties a citation to a deal.

Do the last part even when it stings. Put the panel results beside pipeline every month. If mentions climb and demo requests don’t move, you’ve learned something real, and learning it in month three costs a lot less than learning it in month twelve.

What I’d Fix First at a SaaS Company Under $50M

I’d run it in this order at 15 people or at 150. Foundation first.

1. This week, check the door. Robots.txt, CDN and firewall rules for the five crawlers above. Free, fast and binary.
2. This month, rewrite the pricing page and your top three integration pages so they state the facts a buyer’s prompt asks for.
3. This quarter, publish two honest comparison pages and put a review request into onboarding with an owner’s name on it.
4. Every month after that, run the prompt panel, log it, and hold it up against pipeline.

There’s no trick anywhere on that list. That’s the point. It’s a boring system, and in my experience boring systems are the ones that compound, because they keep running on the weeks when nobody in the building is excited about them anymore.

C4 Solutions works with technology and SaaS companies up to $50M in revenue on the systems behind growth, and search is one of them. Our page on SEO, AEO and GEO for tech companies shows how we run that work. If you want a second set of eyes on whether AI assistants can find and trust your product, book a growth strategy call.

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