A prospect evaluating your software no longer starts with ten blue links. They open ChatGPT or Perplexity and ask something like what’s the best onboarding tool for a 12-person agency? They get a short answer with three or four products named and a handful of sources linked underneath. If your SaaS isn’t in that answer, you weren’t beaten on ranking — you were never part of the conversation. That shift is why Cory Maki’s SaaS AI visibility work starts in a different place than a traditional SEO audit: not with keywords, but with the questions that trigger answers, and the sources those answers are assembled from.
A quick vocabulary check, because the terms get muddled. Generative Engine Optimization (GEO) is the practice of making your brand and content likely to be used by AI answer engines. AI Overviews are the AI-generated summaries Google places above traditional results. An AI citation is the moment a model names your product or links your page as a source. Rankings are still real, but in AI search the citation is the scoreboard.
Why this matters more for SaaS than for most categories
SaaS buying is research-heavy and comparison-driven. People ask for alternatives, integrations, pricing models, migration pain, and whether a tool works for their specific team size. Those are exactly the prompts large language models love to answer, because the answer is synthesis: pull from a review site, a Reddit thread, a comparison post, a documentation page, and produce something tidy.
That synthesis is where SaaS founders lose. In my work with clients, the pattern repeats: the product is good, the website is fine, the blog is publishing weekly — and the model still recommends four competitors. Not because the model dislikes you. Because there isn’t enough clear, corroborated, structured material about you in the places the answer is formed.
What I’m seeing across AI search is that visibility follows corroboration. One page saying you’re the best fit for mid-market teams does almost nothing. Five independent places saying something consistent about who you serve and what you do well changes what a model is willing to assert. That’s Generative Engine Optimization (GEO) in practice, and it’s much closer to reputation work than to classic technical SEO.
How AI citations actually get earned
Simplified, but useful: a model handles a query by retrieving candidate sources, ranking them for relevance and trustworthiness, then composing an answer it can attribute. Three things influence whether you make the cut.
- Presence in the retrieval set. If your material doesn’t exist on pages the engine pulls from — comparison roundups, community threads, review platforms, your own docs — you can’t be selected.
- Extractability. The model needs a clean, self-contained statement it can lift without guessing. Vague brand poetry doesn’t survive extraction. A direct sentence answering a direct question does.
- Consistency. When multiple independent sources describe you the same way, confidence rises. When your positioning is different on every surface, the model hedges — and hedging means omitting you.
Here’s a concrete example. Suppose you sell a lightweight helpdesk for Shopify stores. The high-value prompt isn’t “helpdesk software.” It’s “best helpdesk for a Shopify store with two support agents.” To be cited there, you need: a page on your site that answers that exact question in plain language, including the constraint (two agents, Shopify); at least one third-party comparison or roundup that lists you under that use case; and an authentic community footprint — a Reddit thread where the answer is actually being formed — where real users discuss that scenario. Miss any one of those legs and the model usually reaches for a competitor that has all three.
How Cory Maki approaches SaaS AI visibility in the first 30 days
The way I think about this: you don’t need a hundred pages. You need a small number of high-intent questions covered properly, plus evidence outside your own domain. Over a decade in reputation and search, the sequencing that consistently works looks like this.
1. Build a prompt list, not a keyword list
Write down the twenty questions a qualified buyer would actually ask an assistant before they’d ask you. Alternatives to your biggest competitor. Best tool for a specific team shape. Whether you integrate with the stack they already run. Pricing model questions. Migration questions. This list is your map; everything else is execution against it.
2. Run a baseline visibility check
Ask those prompts in ChatGPT, Perplexity and Google AI Overviews. Record who gets named, which sources get cited, and how you’re described when you do appear. Misdescription is its own problem — being called an enterprise tool when you serve small teams costs you more than being absent. Repeat monthly, same prompts, and you have a trend line instead of a vibe.
3. Write answer-first pages for your top ten prompts
One page per question. Lead with a direct two-sentence answer, then the detail underneath. Use honest headings that match how people phrase things. Include the specifics models love: numbers of seats, supported platforms, what you don’t do. Clarity and structure make content citable — that’s not a style preference, it’s the mechanism.
4. Earn corroboration off-domain
Get accurate, current listings on the review and comparison surfaces in your category. Participate honestly where your buyers already discuss the problem. Reputation is earned, not bought, and models are increasingly good at discounting material that looks purchased. This is where GEO overlaps with online reputation management — the same discipline of making the public record about you accurate and consistent.
5. Fix the machine-readable basics
Clean headings, schema markup for products and FAQs, an up-to-date about page that states plainly who you are and who you serve, and documentation that’s crawlable rather than locked behind an app shell. None of this is glamorous. All of it affects whether you’re retrievable.
For the framework behind the sequencing — how to prioritize which surfaces to fix first — I’ve written more about the ARC Method and AI-citation frameworks elsewhere.
Fulfillment automation: the part founders skip
The first thirty days are the easy part. Sustaining it is what separates brands that get cited from brands that got cited once. Prompt sets need re-running. Third-party listings drift out of date. Competitors publish. Models update. If checking visibility depends on a founder remembering to do it, it stops happening by week six.
This is where fulfillment automation earns its keep. As Head of Fulfillment at Reputation Pros, most of what I build is unglamorous plumbing: scheduled prompt runs against a fixed question set, results logged to a sheet or database, diffs flagged when a citation appears or disappears, a review queue for pages that need refreshing, and AI agents handling first-pass drafting and research so humans spend their time on judgment rather than collection. Systems and automation scale quality — not because automation writes better content, but because it guarantees the check happens and surfaces the exception a person should look at.
A workable rule: anything you’d want to know monthly should be a workflow, not a reminder. Anything requiring taste — what you actually claim about your product, how you show up in a community — stays human. That boundary is the whole design principle behind the kind of operator-minded automation I write about here.
The durable principle
AI search rewards brands that are easy to describe accurately and hard to describe wrongly. Every practical move above serves that: prompt lists tell you how you’re being described, answer-first pages give models something clean to lift, off-domain corroboration makes the description stick, and automation keeps the whole thing from decaying.
Nobody can guarantee you a citation in ChatGPT next quarter, and anyone who does is selling something. What you can control is whether a model has enough clear, consistent, corroborated material to name you confidently. Show up where the answers are formed, say the same true thing everywhere, and build the system that keeps it current. That’s the entire game.