Generative engine optimization (GEO) is the practice of shaping your content, your brand’s presence across the web and your site’s technical setup so that AI answer engines such as ChatGPT, Claude, Gemini, Perplexity and Google’s AI Overviews name your product accurately when a buyer asks for recommendations. It sits next to search engine optimization, not in place of it. SEO aims to rank a page in a list of links. GEO aims to be included, correctly described and cited inside a written answer.
For a SaaS company the shift is easy to see in a buyer’s own behaviour. A head of marketing who once typed “best marketing automation software” into Google and opened six tabs now often asks an assistant, reads one synthesized answer and builds a shortlist from it. If your product is missing from that answer, the buyer may never visit your site, and your analytics will show nothing to explain the loss.
This guide explains how GEO works, what the research supports and what it does not, and how a SaaS team can build a program around it. It also marks which claims are documented, which are observed in practice and which are still guesses, because the field has attracted more confident advice than solid evidence. If you would rather see where your brand stands before reading further, you can check what AI says about your brand with a free audit.
What GEO Is and How It Differs From SEO
The term comes from a 2023 research paper by Pranjal Aggarwal and colleagues, later revised in 2024, which framed generative engine optimization as a way for content creators to raise their visibility in answers written by language models. The paper reported visibility gains of up to 40% for some techniques, and it found that results varied by subject area. It is a preprint, and its main experiments used a research setup built on GPT-3.5 that retrieved the top five Google results, so it shows what is plausible, not what every commercial engine does today.
Three related terms circulate, and they overlap more than most articles admit:
- SEO targets rankings in a list of results and the clicks that follow.
- AEO (answer engine optimization) targets direct answers, such as featured snippets and voice responses.
- GEO targets inclusion and accurate description inside AI-written answers, whether or not the user ever clicks.
The working differences matter more than the labels:
| Classic SEO | GEO | |
|---|---|---|
| Unit of success | A page ranks for a keyword | A brand is mentioned or cited for a question |
| Where the result appears | A list of ten links | One written answer, often with a few source links |
| Main inputs | Keywords, links, page experience | Clear facts, evidence, consistent descriptions across many sources |
| Typical measure | Position, clicks | Share of prompts that name you, accuracy of the description, referral traffic |
| Stability | Rankings move slowly | Answers can change between runs of the same prompt |
The last row deserves attention. Ask the same question twice and an assistant may name different vendors. That makes single screenshots of a good result close to meaningless, and it is why the measurement section below relies on repeated prompt tests.
GEO is also not separate from SEO in practice. Google states that there are no additional requirements to appear in AI Overviews or AI Mode, and that a page needs to be indexed and eligible to show with a snippet. Many of the other engines draw on search indexes as well. A site that cannot be crawled or does not rank for anything has a weak starting position in both worlds. That is why Voxturr’s SaaS SEO services treat Google rankings and AI visibility as one program.
How AI Engines Decide Which SaaS Brands to Name
No engine publishes its selection logic, so anyone describing it is partly inferring. What can be said with reasonable confidence is that an answer about software draws on three kinds of input, and a brand needs to be present in at least two of them.
1. What the model learned during training. A model absorbs descriptions of your product from the text it was trained on: your site, review profiles, press, forum threads, documentation. This is slow to change. A company that launched recently, or that has described itself in five different ways across the web, tends to be described vaguely or not at all.
2. What the engine retrieves at the moment of the question. Perplexity, ChatGPT with search, Gemini and Google’s AI features fetch live pages and then write an answer from them. Here ordinary SEO matters, since a page that is not indexed or not readable by the crawler cannot be retrieved. Fresh, specific pages can enter answers faster than they enter a model’s training data.
3. What other sources say about you. Buyers ask comparison questions, and the engine often leans on third-party material to answer them: review platforms, listicles, community threads and analyst-style roundups. Several vendor studies of citation patterns report that Reddit, YouTube, Wikipedia and review or comparison sites account for a large share of cited sources, and that the mix differs sharply between engines. One benchmark post summarizing earlier studies reports that only about 11% of cited domains overlap between ChatGPT and Perplexity. Treat these figures as directional. They come from vendors with something to sell, the samples differ, and the platforms change often.
The practical reading is a filter model. For an engine to recommend a SaaS product with confidence, it usually needs to be able to do four things:
- Place the product in a clear category.
- Match it to the buyer’s use case or company size.
- Point to specific evidence, such as numbers, customer names or documented features.
- Find that same story repeated by sources the brand does not control.
A product that fails the first step is rarely recommended at all. A product that passes the first three but has no independent confirmation is often mentioned with hedged language or left out when the question asks for the “best” option.
There is one finding from the original GEO paper worth keeping in mind here. Lower-ranked sites gained the most from the techniques tested. Citing sources raised visibility for sites in fifth position by more than 115% on average, while the top result lost some. The authors suggest that engines weigh content quality more than backlinks and domain authority. If that holds in commercial engines, smaller SaaS brands have a better chance here than they do in a link-driven ranking contest. It remains a research result, not a guarantee.
Building a GEO Program: Six Steps
A program works better than a one-off audit, because answers shift and each step feeds the next. This order suits a SaaS team of any size.
Step 1. Build a prompt library. Write 30 to 50 questions a real buyer would ask, grouped by stage. Include category questions (“what is the best tool for X”), comparison questions (“A vs B”), problem questions (“how do I reduce churn in a self-serve product”) and constraint questions (“alternatives to A for a 20-person team”). Pull the wording from sales calls, support tickets and search queries, not from guesses.
Step 2. Run a baseline. Put each prompt through ChatGPT, Claude, Gemini, Perplexity and Google AI Mode. Run each prompt at least three times, since answers vary. Record whether you are named, how you are described, which competitors appear and which sources are cited. A spreadsheet is enough.
Step 3. Fix the facts about you. The baseline usually exposes wrong or thin descriptions: an old pricing model, a missing integration, a category label you no longer use. Correct these at the source, meaning your own pages first and then profiles and listings you control.
Step 4. Close the content gaps. Compare the questions where you are absent with the pages you have. Build or rewrite pages that answer those questions directly, using the formats in the next section.
Step 5. Strengthen what others say about you. Review profiles, comparison roundups, community threads and press coverage carry weight with several engines. Plan this work on a quarterly basis, since it moves slowly.
Step 6. Re-measure monthly. Repeat the baseline with the same prompts. Change the library only to add new questions, so the trend stays comparable.
A reasonable first quarter looks like this:
| Weeks | Focus |
|---|---|
| 1 to 2 | Prompt library, baseline, crawler access check |
| 3 to 6 | Fix homepage, pricing and category descriptions, update review profiles |
| 7 to 10 | Publish or rewrite five to eight answer pages for the biggest gaps |
| 11 to 13 | Start third-party work (reviews, roundups, expert commentary), repeat the baseline |
Expect the first measurable movement in retrieval-based engines within weeks, since they read live pages. Changes that depend on model training can take months, and no one can promise a date.
Content That Earns Citations
The GEO paper tested nine ways of rewriting content. Adding quotations from credible sources performed best, followed by adding statistics and citing sources, each with gains of roughly 30% or more on the paper’s main visibility measure. Making text easier to follow and improving fluency also helped. Keyword stuffing, the old SEO habit, did worst and on Perplexity scored below the unmodified baseline. The pattern is consistent with how a language model builds an answer: it favors passages that are specific, attributed and easy to lift.
For a SaaS site, that translates into a few concrete formats.
Answer-first pages. Put the direct answer in the first two sentences under a heading that matches the question, then add the detail. A page that opens with three paragraphs of context before it answers makes extraction harder. Voxturr’s SaaS blog writing guide covers this structure in more detail.
Definition and category pages. State what the product is, who it is for and which category it belongs to, in plain words and in the same words used on your homepage, profiles and press kit. Consistency matters because the engine is trying to reconcile many descriptions into one.
Comparison and alternatives pages. Buyers ask these questions constantly. A fair page that names competitors, states where each fits and where yours does not earns more trust than a page that declares you the winner. Include a dated pricing comparison where pricing is public.
Use-case and industry pages. “CRM for 10-person agencies” is a question an engine can match. “The all-in-one growth platform” is not.
Pricing pages that show prices. Engines handle questions about cost often. A page that hides every number gives them nothing to quote, and they may quote an outdated third-party figure instead.
Original data. A survey, benchmark or analysis of your own customer base, with the method stated, gives other sites something to cite and gives engines a specific figure to attach to your name. Even a small sample works if the method is honest.
Case studies with numbers. Name the customer, the starting point, the change and the time frame. Vague praise is easy to skip.
A short checklist for every page:
- The question appears as a heading, and the answer follows immediately.
- Claims carry a number, a source or a named example.
- Dates are visible, and old figures are updated.
- The author has a real name and a credible profile.
- The page links to related pages, using descriptive anchor text.
The page-by-page version of this advice, covering homepage, feature, integration and documentation pages, is in Voxturr’s guide to AI-SEO for SaaS companies.
Off-Site Signals: What Other Sources Say About You
On your own pages you can say anything. Engines know that, so they look for confirmation elsewhere. For most SaaS brands the off-site work is the slowest part of GEO and the part competitors copy least.
Review platforms. G2, Capterra, TrustRadius and similar sites hold structured facts about your product: category, features, pricing tiers, review text. Keep the profile complete and current, and ask satisfied customers for reviews on a steady schedule rather than in a burst. The evidence on how directly these profiles feed AI answers is mixed, and some analyses treat them as a gate to be considered at all more than as a source that gets quoted. They also shape the comparison articles that engines do quote, so the effort is rarely wasted.
Community discussions. Reddit appears prominently in several citation studies, and Perplexity in particular has been reported to lean on it. Useful participation means answering questions with real experience, disclosing your affiliation and not posting links for their own sake. Moderators and readers remove promotional posts quickly, and a banned account does the opposite of what you intended.
Comparison and “best of” articles. Roundups of tools in your category are frequently retrieved and quoted. Find which ones rank for your category terms, then pitch accurate inclusion with facts the editor can verify. Paying for placement without disclosure is a poor idea, both ethically and because it can backfire if discovered.
Digital PR and expert commentary. Original data from your own customer base is the strongest asset here. A benchmark report gives journalists, newsletter writers and analysts a reason to cite you, and each citation adds another independent description of what you do.
Video. YouTube shows up often in Google’s AI features, according to several studies. Product walkthroughs and how-to videos with accurate titles, descriptions and transcripts widen the set of sources an engine can pull from.
Wikipedia and knowledge sources. Only pursue these where your company meets the notability rules, and never edit your own entry in a way that breaks those rules. A well-sourced entry, written by independent editors, can anchor how models describe an entity. A promotional one gets deleted.
Every item above has the same aim: the facts about your product should read the same wherever an engine looks. A one-sentence description that you can paste into each profile and each pitch, kept up to date, is more valuable than it sounds.
Technical Foundations
Technical work does not create citations, but a technical fault can prevent them. Check these in order.
Crawler access. Open your robots.txt and confirm it does not block the crawlers of the engines you care about. Each company publishes its own crawler names, and some separate the crawler used for training from the one used for live answers. Decide on purpose which you allow. Blocking everything by default, often through a security plugin or CDN rule, is a common accident.
Server-rendered content. If your key pages build their text with JavaScript after loading, some crawlers will see an empty shell. Pricing tables, feature lists and FAQs should appear in the HTML that the server sends.
Indexing and canonical hygiene. Duplicate URLs split signals, and pages that return errors or sit behind logins cannot be retrieved. For many sites the duplicate-URL cleanup is the highest-value technical task, for GEO and SEO alike. The wider technical checklist sits in our B2B SaaS SEO guide.
Structured data. Organization, Product, SoftwareApplication, FAQPage and Article markup help machines read a page, and Google notes that structured data should match the visible text. It is good practice. Google also states that no special schema is required to appear in its AI features, so do not expect markup alone to change an answer.
Page speed and stable rendering. Slow or unstable pages are fetched less reliably. This is basic hygiene, not a GEO trick.
llms.txt. This proposed file lists the pages you want language models to read. As of this writing, Google says it does not require new AI-specific files, and the evidence that llms.txt changes citations is thin. If your team can create it in an hour, it is harmless. It should not take priority over anything else in this section.
A note on honesty in this area: much of the advice sold as “GEO schema” or “AI-ready markup” has no published test behind it. A fair rule is to do what also helps normal search, and treat everything else as an experiment you measure before you repeat.
Measuring GEO and Avoiding Common Mistakes
GEO measurement is still crude, and any vendor that promises a precise “AI rank” is simplifying. Four measures give a usable picture.
- Prompt share. Of the prompts in your library, in what percentage of runs does the engine name you? Track this by engine and by prompt group, using the same prompts each month.
- Description accuracy. When you are named, are the category, pricing, features and audience stated correctly? Score each answer as correct, partly wrong or wrong.
- Citation sources. Which pages are cited when you appear, and which third-party pages are cited when a competitor appears instead? The second list is your off-site to-do list.
- Referral traffic and conversions. In Google Analytics, look at sessions from assistants such as chatgpt.com, perplexity.ai, gemini.google.com, claude.ai and copilot.microsoft.com, and at the AI assistant channel if your property has one. Expect these numbers to look small. Many answers are read without a click, and some AI visits arrive with no referrer and land in Direct. Judge them by engagement and lead quality, not volume alone.
Tools that automate prompt tracking exist, and they save time on the repeating work. Their outputs are samples of a changing system, so confirm any important finding by hand. For a first baseline without any tool, Voxturr’s free AI visibility audit is a quick starting point.
Common mistakes:
- Keyword stuffing for AI. The research that gave GEO its name found it the weakest method tested.
- Chasing one engine. Citation patterns differ between engines, and one benchmark found little overlap between them. Build for facts and evidence, which carry across all of them, then use engine-specific tactics second. Our guide to ranking on Claude shows one such case.
- Publishing volume without substance. Large batches of thin, AI-written pages give engines nothing specific to quote, and they weaken the pages that do.
- Ignoring accuracy. Being mentioned with the wrong price or the wrong category can cost more than not being mentioned.
- Faking evidence. Invented testimonials, undisclosed paid placements and sock-puppet forum accounts are easy to detect and hard to undo.
- Treating a single answer as a result. A good screenshot proves little, since the same prompt can return a different answer an hour later.
- Skipping the basics. Blocked crawlers, duplicate URLs and client-side rendering undo content work quietly.
Conclusion
GEO rewards the things good marketing already needs: a precise statement of what the product is and who it serves, evidence that can be checked, and consistent descriptions wherever a buyer or an engine looks. The tools and the vocabulary are new, but the discipline is familiar. Start with a prompt library and a baseline, fix what you find, and measure again in a month. A team that does that for one quarter will know more about its position in AI answers than most of its competitors.
Ready to see where your SaaS stands in AI answers? Voxturr’s SaaS SEO agency team runs technical SEO, content and generative engine optimization as one program, built around the questions your buyers actually ask. Start with a free AI visibility audit to see what ChatGPT, Claude, Gemini and Perplexity say about you today, or talk to our SEO team about a plan for your category.
Frequently Asked Questions
Is GEO different from SEO?
The goals differ, since SEO targets rankings and clicks while GEO targets inclusion and accurate description inside an answer. The work overlaps heavily. Crawlability, clear content and credible links help both, and Google says no special optimization is needed for its AI features beyond standard search fundamentals.
Does GEO replace SEO for SaaS?
No. Search remains a major discovery channel, several AI engines retrieve from search indexes, and the technical groundwork is shared. Most teams add GEO measurement and content changes to an existing SEO program.
How long does GEO take to show results?
Engines that retrieve live pages can reflect new or corrected content within weeks. Changes that depend on how models describe your brand from training data take months, and no reliable timeline exists. Plan on a quarter before judging a program.
Do I need an llms.txt file?
Not as a priority. Google says no new AI-specific files are needed for its features, and there is little published evidence that llms.txt changes citations. Create it if it costs little, and spend the real effort on content and third-party coverage.
Do reviews on G2 and Capterra affect AI answers?
They appear to help, mostly because they supply structured facts and feed the comparison articles that engines quote. The evidence on direct citation is mixed, so treat reviews as part of a wider off-site effort, not a guaranteed lever.
How can I check whether AI engines mention my SaaS?
Write a list of questions your buyers ask, run each in several engines at least three times, and record whether you are named, how you are described and which sources are cited. Repeat monthly with the same list.
Can small SaaS companies compete in AI answers?
Often more easily than in classic search. The original GEO research found that lower-ranked sites gained the most from content improvements, since engines weigh content quality more than link authority. Specific positioning and real evidence matter more than company size.
