The short answer.
A B2B SaaS company gets into AI answers by making its facts easy to find on the pages engines already read for software questions, and by making sure independent sources repeat those facts. Generative engine optimisation (GEO) is the name for that work. If the terms are new, our guide to how AEO, GEO and SEO overlap sorts them out; this article is about what is different for software.
Three things are different. Software buyers ask a narrow, predictable set of questions. Software has a mature layer of review sites that engines cite and buyers trust. And a SaaS company owns page types most businesses lack, such as documentation and an integrations directory, which answer the detailed questions buyers put to a chatbot.
- 01 · PROMPTSWrite down the category, comparison and alternatives questions your buyers ask
- 02 · FACTSPublish plain, crawlable facts on pricing, integrations and docs pages
- 03 · PROOFKeep review-site profiles accurate, recent and well reviewed
- 04 · COMPARISONPublish fair comparison and alternatives pages
- 05 · MEASURETrack how often you are named, then sign-ups and pipeline
Our generative engine optimisation services start the same way, with the questions buyers ask before choosing a provider.
Why SaaS is a special case.
Software buyers now research with AI chatbots, and they decide early. In a March 2026 survey of 1,076 B2B software buyers, G2 found that “half of B2B software buyers (51%) now start their research with an AI chatbot more often than with Google”, up from 29% in its 2025 report. In the same survey, 69% chose a different vendor from the one they had planned on, and one in three bought from a vendor they had never heard of.
The shortlist is also small and set early. 6sense’s 2025 B2B Buyer Experience Study of nearly 4,000 buyers, as summarised by CustomerThink, found that buying groups filled four of the five places on their shortlist on the first day, that 94% ranked the vendors before contacting any of them, and that they bought from the top-ranked vendor 77% of the time. TrustRadius, surveying 1,862 technology buyers in January 2026, found 83% shortlisted three or fewer products.
A product missing from the AI answer is often missing from the deal.
The first row, the chatbot, is built largely from the other five. Our article on how business intelligence buyers build shortlists works through one software category in detail.
The three prompt families.
Almost every commercial question a software buyer asks an AI engine belongs to one of three families: category (“best X for Y”), comparison (“X vs Y”) and alternatives (“alternatives to X”).
G2 asked buyers what kind of prompt they start with. A third began with the category, and almost as many began with a competitor’s name.
What each family needs from you
| Family | What the buyer is doing | What the engine needs to find |
|---|---|---|
| Category: “best X for Y” | Building the first list for a team size, industry or use case | Who you are for, stated on your pages and repeated on review sites and lists |
| Comparison: “X vs Y” | Cutting the list down to two or three | Specific, checkable differences: features, limits, integrations, pricing model, support |
| Alternatives: “alternatives to X” | Leaving, or doubting, a known product | Why people switch, who each alternative suits, what migration involves |
G2’s report ranks “comparing strengths & weaknesses across vendors” as the top thing buyers use chatbots for. Google describes the same shift from its side: in AI search, “users are asking longer and more specific questions”.
Category: “Best customer onboarding software for a 40-person B2B SaaS company that uses HubSpot and Slack.”
Comparison: “Compare the top two on implementation time, HubSpot sync and how pricing scales past 500 customers.”
Alternatives: “We find the first one too complex to set up. What are simpler alternatives with SSO on a mid-tier plan?”
Invented prompts, not real queries. Each can only be answered from integrations, pricing, documentation and review pages.
Your first job is to write down ten to twenty real questions of your own. The play on building a buyer question panel gives a method, and our guide on SEO for complex B2B products shows how technical buyers phrase their searches.
How a SaaS answer is assembled.
An AI engine answering a software question usually runs several searches, reads a handful of pages, and writes a short answer from them.
Google says AI Overviews and AI Mode “may use a ‘query fan-out’ technique”, issuing several related searches for one question. It also says there are no special requirements to appear: a page “must be indexed and eligible to be shown in Google Search with a snippet”. OpenAI runs a dedicated crawler for ChatGPT search and states that “sites that are opted out of OAI-SearchBot will not be shown in ChatGPT search answers.” The entry ticket is ordinary: be crawlable and indexed. Our piece on how ChatGPT decides which companies to recommend covers the mechanics.
What matters for SaaS is which pages get read. In December 2025 Ahrefs published a study of ChatGPT’s answers to 750 early-stage prompts across software, products and agencies. Across all source links, “best X” blog lists were 43.8% of the page types cited. When a recommended software brand’s own site was cited, this is what the engine picked:
General landing pages were cited far more often than ordinary blog posts. And documentation appeared for software at 7.6%, against 0.4% for agencies.
The same engines also lean on what others say. An Ahrefs analysis of 75,000 brands, published 12 December 2025, found that branded mentions across the web correlated with AI visibility at 0.66 to 0.71, while the number of pages on a site showed almost no relationship (about 0.19). More pages is not the plan. If you are absent today, read our diagnosis of why a brand goes missing from AI shortlists.
Review sites.
Review sites are the most important off-site source for a SaaS product, because both the engines and the buyers checking the engines use them.
The engines cite them directly. Profound’s analysis of 680 million citations between August 2024 and June 2025 put G2 among ChatGPT’s five most-cited domains, at 1.1% of all citations, and in Perplexity’s top ten at 0.6%.
Buyers then use them to check the answer. TrustRadius found that 94% of buyers who used AI “fact-check its responses at least some of the time”, and that 74% use reviews to inform their decision. G2 reported that a citation from a software review site was buyers’ top trust signal in an AI answer.
What a profile needs
- ✓The right category. In the wrong one, you are in the wrong list.
- ✓The same facts as your site: description, pricing model, integrations and customer size.
- ✓Recent reviews, requested steadily after onboarding or renewal.
- ✓Specifics. Ask customers what they use the product for and what it replaced.
- ✓Replies to criticism. A factual reply is also source material.
Stay inside the rules. In the United States, the Federal Trade Commission’s rule of 14 August 2024 bans fake reviews, including AI-generated ones, and prohibits “providing compensation or other incentives conditioned on the writing of consumer reviews” that express a particular sentiment. Ask every customer, and never tie a reward to a rating. We cover the UK rules in our guide to generative engine optimisation in the UK. The wider set of third-party sources is covered in our guide to the off-site sources AI engines trust, and the play on review directories has a short audit.
Documentation and help centres.
Your documentation is the best source you own for detailed questions, and it is often invisible to AI crawlers.
Comparison prompts turn on details: single sign-on, API rate limits, export options, setup time. Documentation states them; marketing pages rarely do.
The common failure is technical. Many documentation sites and help centres are JavaScript applications. Vercel’s analysis of crawler traffic on its network, published 17 December 2024, concluded that “none of the major AI crawlers currently render JavaScript”, with Google’s Gemini and Applebot as exceptions. If your docs only appear after scripts run, most AI crawlers see an empty shell. Vercel’s advice was to server-render critical content.
Four checks for a docs site
- View the page source of a docs article and search for a sentence from the middle. If it is missing, the page depends on JavaScript. The play on server-rendered content walks through the test.
- Check robots.txt on the docs subdomain. It is a separate file from your main site’s.
- Remove login walls from reference material that is not sensitive. A crawler cannot sign in.
- Add “overview” pages that state limits, security, compliance and setup steps in plain sentences.
An llms.txt file is reasonable for developer documentation, but it is not what gets you named. Our guide to llms.txt, schema and AI crawlers explains why.
Integrations pages.
Every integration deserves its own crawlable page, because “works with the tools we already have” is a hard filter in software buying.
In G2’s survey, 9% of buyers start with an ecosystem prompt, and the constraint appears inside many category prompts too. An engine can only place you in that answer if a page says, in text, that the integration exists and what it does.
What each integration page should state
- The name of the other product, written the way its own vendor writes it.
- What the integration does in one or two sentences, and which direction data moves.
- Whether it is native, built by a partner, or runs through a connector tool.
- Which of your plans include it.
- A link to the setup guide in your documentation.
- The date the page was last checked.
If your partner runs a marketplace, your listing there is an independent source that confirms the claim. Make it match your own page. The play on partner listings covers this, and a mismatch between the two is a common reason for AI describing your product wrongly.
Pricing pages.
If you do not publish how your pricing works, an AI engine will describe it from someone else’s page, and that page may be out of date or written by a competitor.
Buyers want this information and ask chatbots for it. TrustRadius reports that transparent pricing has been buyers’ top wish-list item for vendors for four years running, and G2 lists “understanding pricing/packaging options” among the uses of chatbots.
You need not publish every number, but do publish the model.
| If you… | Publish at least | Why an engine needs it |
|---|---|---|
| List prices publicly | Plans, what each includes, the unit you charge by, billing terms, as text in the HTML | Toggles and sliders that load by script are often unreadable to crawlers |
| Sell by quote | The pricing model, what drives the quote, the smallest sensible customer, what is always included | Lets the engine answer “how is it priced?” without guessing |
| Offer a free plan or trial | Limits, length, whether a card is needed, what happens at the end | “Free alternatives to X” is a common alternatives prompt |
| Gate features by plan | Which plan includes SSO, API access, audit logs and key integrations | Comparison prompts ask exactly this |
Add a visible “last updated” date. When you change plans, update the review-site profiles in the same week. The play on explaining your pricing approach has wording patterns for quote-based products.
Comparison and alternatives pages.
Publish your own “X vs Y” and “alternatives to X” pages, and make them fair enough that a buyer who checks them against a review site finds the same facts.
Self-promotional lists are everywhere in software. Ahrefs found that in 169 of 250 “best X software” search results (67.6%), a list’s own publisher ranked itself first. Peec AI, analysing about 232,000 citations from software review prompts between December 2025 and February 2026, found about one in ten came from self-promotional listicles: 3.6% on average in ChatGPT, 10.3% in Google AI Mode and 10.4% in Perplexity. Its researchers said the tactic “still ‘works’ for now” but did not recommend it.
We agree with the caution. When 94% of AI-using buyers fact-check answers, a page that says plainly where the other product is the better choice is more believable than one where you win every row.
The anatomy of a fair comparison page
- A two-sentence verdict at the top: who should choose each product.
- A table of checkable facts, each with a source link and a date: features, limits, integrations, pricing model, support.
- “Choose them if…” written honestly.
- Migration notes on alternatives pages: what moves across, what does not, how long it usually takes.
- A review date, and a named owner inside the company who rechecks it each quarter.
Freshness matters. Of 1,100 list articles Ahrefs examined, 79.1% had been updated in 2025. The plays on comparison pages and “best X for Y” pages give templates, and our guide to the page patterns AI engines quote shows how to write the passages themselves.
The 90-day plan by page type.
Ninety days is enough to fix access, publish the missing facts and ship a first set of comparison pages. Nobody can honestly promise a place in a particular answer.
The plan assumes one marketer, some developer time and someone in customer success who can ask for reviews.
| Page type | Days 1–30: baseline and access | Days 31–60: facts | Days 61–90: comparison and proof |
|---|---|---|---|
| Prompt panel | Write 10–20 prompts; record who is named and which sources are cited | Re-run | Re-run; compare how often you are named |
| Review sites | Audit category, description and facts on each profile | Correct profiles; start regular review requests | Reply to reviews; recheck profiles against new pages |
| Documentation | Test rendering, robots.txt and login walls | Fix access; add buyer-level overview pages | Cite docs as the source in comparison tables |
| Integrations | List every integration | One page each for the ten most asked about | Align partner marketplace listings |
| Pricing | Check what engines say about your pricing | Publish the model and plan gates as HTML text, dated | Correct third-party pages that state old pricing |
| Comparison and alternatives | Pick the three competitors named most often beside you | Gather sourced facts for each | Publish three “vs” pages and one alternatives page |
The order is deliberate: access, then facts, then the comparison pages built on those facts.
For the baseline itself, use the manual method in our guide to checking your AI search visibility, or ask us for a free AI visibility report on a sample of your buyers’ questions.
The page-type self-check.
Tick each statement that is true for your product today. You get a score out of 100 and the fixes to make first. Nothing you tick is sent anywhere.
SaaS GEO readiness: 0 / 100
Tick what is true today. If you have not checked, leave it unticked.
The weights are our judgement, not a measured effect.
Measuring it in sign-ups and pipeline.
Measure GEO for SaaS at three levels: how often you are named across repeated runs of your prompts, how many visits and sign-ups arrive from AI engines, and how much pipeline those sign-ups become. A separate guide explains how AI visibility scores are calculated.
Answers vary too much for a rank to mean anything. Research by SparkToro, reported by Search Engine Journal on 30 January 2026, ran 2,961 prompts across ChatGPT, Claude and Google’s AI search results and found less than a 1-in-100 chance of getting the same list of brands twice for the same prompt. How often a brand appears across many runs was more consistent, so run each prompt several times a month and count appearances.
AI traffic can be small and still matter, so give it its own line in the report. Ahrefs, itself a SaaS company, published its own figures on 16 June 2025: AI search was 0.5% of its traffic over 30 days and 12.1% of its sign-ups. Its author cautioned that this “might be somewhat unique to us”.
To get past sign-ups, the source has to follow the account into your CRM. Our guide to tracking AI referrals from GA4 to pipeline covers the reporting. Once CRM stages are recorded, offline conversion tracking for B2B SaaS shows how to send those stages back to your analytics and ad platforms.
If you would like this run for you, our GEO and AEO services cover a dated baseline of buyer questions, the content work and the tracking. If you are comparing providers, our SaaS SEO agency buyer’s guide includes a proposal scorecard.
Questions SaaS teams ask.
What is GEO for B2B SaaS?
It is the work of getting a software product named, described accurately and cited when buyers ask AI engines category, comparison and alternatives questions. In practice it means keeping review-site profiles, documentation, integrations, pricing and comparison pages factual, crawlable and consistent with each other.
Is GEO for SaaS different from SaaS SEO?
The foundations are shared: pages must be crawlable and indexed, and Google says there are no special requirements for its AI features. The emphasis differs. GEO puts more weight on what third parties say about you and on fact pages such as pricing, integrations and docs, because AI answers are built from several sources at once.
Which page type should we fix first?
Access first: make sure documentation and pricing text is in the page source and not blocked. Then review-site profiles. Comparison pages come last, once the facts they rely on are published.
Should we write our own “best X software” list and put ourselves first?
We advise against it. Peec AI found such lists are still cited, at about one in ten citations for software review prompts, but its own researchers did not recommend the tactic. A fair comparison page is the safer asset.
How long does GEO take for a SaaS product?
Fixes to access and facts can be made within 90 days. When engines reflect them is outside your control and varies by engine.
Sources: G2, 15 Apr 2026; G2, 2026 AI search insight report; CustomerThink on 6sense, 30 Nov 2025; TrustRadius, 15 Jul 2026; Ahrefs, 4 Dec 2025; Ahrefs, 12 Dec 2025; Ahrefs, 16 Jun 2025; Profound, Aug 2025; Peec AI, 3 Mar 2026; Google, AI features; Google, 21 May 2025; OpenAI, crawlers; Vercel, 17 Dec 2024; FTC, 14 Aug 2024; Search Engine Journal, 30 Jan 2026. All accessed 12 November 2026.