What buyers in saas ask AI
These are the prompts we track for SaaS companies. Each one fans out into several searches behind the scenes, and every search is a chance to be one of the sources the answer is built from.
- best CRM for a 20 person B2B sales team
- HubSpot alternatives that are cheaper for startups
- best project management software that integrates with Slack and GitHub
- Ahrefs vs Semrush for a small agency
- best SOC 2 compliance automation tool
- what scoreboard software do sports leagues use
- is it worth buying backlinks from a link building service
- best customer onboarding software for SaaS
Where AI engines look for saas answers
Category grids and review snippets give engines both the shortlist and the sentiment. They are among the most retrieved sources for 'best X software' prompts.
Fan-out queries return these pages first. If you are not on the lists that rank, you are rarely in the answer.
Buyers ask 'X alternatives' and 'X vs Y' constantly. Engines retrieve comparison pages from vendors and third parties alike.
Engines quote practitioner experience. The GetMoreBacklinks case shows a Reddit review being lifted almost verbatim into Google's AI answer.
Slack, HubSpot, Salesforce, Shopify and Zapier listings confirm what you connect to, which is how buyers phrase many prompts.
Specific capability questions ('does X support SSO') are answered from docs. Changelogs signal recency.
The subreddits that matter
01How do AI engines build a software shortlist?
A prompt like 'best CRM for a 20 person B2B team' is expanded into several searches: best CRM for small B2B teams, HubSpot alternatives, Pipedrive vs HubSpot, reddit best CRM. The engine retrieves the top results from Bing or Google, then assembles an answer from the passages that name tools and give a reason for each.
That means a SaaS company's AI visibility is mostly decided off its own site. Review grids, listicles, comparison pages and Reddit threads produce the names; your site confirms the details. The model is looking for agreement. If five sources describe you the same way and put you in the same category, you get named. If the sources disagree, or you are absent from the lists, you do not.
OBScoreboard is the clearest example in our work. It went from 0 to 129 AI answers, with 94 in Google AI Overviews, while its referring domains grew from 145 to 921 over two years. The links matter less as a ranking factor in themselves than as proof that the brand appears in many of the places engines read.
02Why does Google quote Reddit reviews of SaaS tools?Grey area
Because a Reddit thread is the closest thing on the open web to a candid review from someone with nothing to sell. Google has leaned into this in classic search and AI answers alike. The GetMoreBacklinks case is the extreme version: Google's AI answer quotes a Reddit review of it almost word for word. Whoever wrote the most specific, quotable sentence about the product in that thread effectively wrote Google's answer.
So the play is to make sure the threads that rank for your category contain accurate, specific descriptions of your product. That starts with comments, not posts. Find the 'what do you use for X' and 'is X worth it' threads that rank, and answer them properly: what the product does, what it costs, who it is wrong for. Disclose who you are. A disclosed founder reply that admits a limitation is trusted more than a stranger's praise.
Then run the four-post campaign: a teardown of how you built or grew the product, a data post from your own usage numbers, a comparison post that is genuinely fair, and a question post that seeds the discussion you want to see. Each post is written for the subreddit's culture, posted at its active hours and supported in the comments for the first two hours.
- Map the 20 to 30 Reddit threads that rank for your category and alternative queries.
- Write replies that include a specific number, a limitation and a use case. Those are the sentences engines lift.
- Use founder and team accounts with flair or disclosure, built up with real participation first.
- Revisit old, still-ranking threads when the product or pricing changes, with a useful update.
- Never ask customers to post coordinated reviews: moderators and Reddit's filters spot the pattern.
03Do G2 and Capterra still matter in the age of AI search?
More than ever, because engines retrieve them for nearly every 'best X software' prompt. The category you choose on G2 decides which grid you appear in, and the grid is what the engine reads. Pick the category your buyers search for, not the one where you rank highest.
Review volume and recency matter because engines summarise sentiment. A steady flow of genuine reviews from real customers, collected through an in-product prompt after a success moment, beats a single review drive. Reviews that mention specific use cases ('we use it for X with Y integration') get quoted.
Then make sure your profile text matches your site word for word on the category claim. Entity consistency is a quiet factor: the engine trusts a description it sees repeated across independent sources.
04Should SaaS companies publish competitor alternatives pages?Grey area
Yes, and most do it badly. An alternatives page that claims you beat the market leader on every row is ignored by buyers and treated as low trust by engines. The page that gets cited is the fair one: a clear statement of who each product is for, a feature and pricing table with sources, and an honest 'choose them if' section.
Build one page per meaningful competitor ('X alternatives') and one per head-to-head ('X vs Y'). Lead each with a two-sentence verdict the engine can lift. Keep pricing current, because outdated pricing is the fastest way to lose a citation to a third-party page.
Pair this with your own 'best X software' roundup. Including yourself in a ranked list on your own domain is a grey-hat tactic that works when the criteria are stated and you rank yourself honestly. It gets retrieved for the same fan-out queries as third-party roundups.
05How do integrations help SaaS get recommended by AI?
Buyers phrase prompts around their stack: 'project management tool that integrates with Slack and GitHub'. Engines answer those prompts with marketplace listings and integration pages. A listing on the Slack, HubSpot, Salesforce, Shopify, Atlassian or Zapier marketplace is both a high-authority link and a fact the model can use.
Build an integration page on your site for every meaningful integration, with what syncs, in which direction, and a short setup guide. These pages win long-tail prompts that generic feature pages never will, and partner marketplaces often link back to them.
06What kind of link building actually moves SaaS AI visibility?
Links that put your name on pages engines already read. That means listicle inclusions, integration partner pages, podcast and webinar pages, data studies picked up by trade blogs, and unlinked mentions reclaimed into links. Raw link counts matter less than the overlap between your referring domains and the domains cited in AI answers for your category.
OBScoreboard's growth from 145 to 921 referring domains came alongside the jump in AI answers because the links were placed in the category's citation substrate, not bought in bulk on sites nobody reads.
- Pull the cited domains for your top prompts and prioritise links from those first.
- Reclaim unlinked brand mentions in reviews, roundups and partner pages.
- Publish one data asset per quarter built to be cited by trade blogs.
- Avoid link farms and PBNs: they add nothing to AI visibility and carry real risk.
Running AI visibility for saas companies?
Book 30 minutes with Prithvi. We run your buyers' prompts first, so the call starts with where you stand.
One story, four channels
How we run each channel for saas companies. Reddit leads, because it is where AI goes for recommendations.
RedditLeads
- Reply to the ranking 'what do you use for X' threads with specific, disclosed answers.
- Four-post campaigns: build teardown, usage data, fair comparison, seeded question.
- Founder AMAs in the subreddit of your buyers once the account has history.
- Revive old, still-ranking threads with genuinely useful updates when pricing or features change.
- Track which threads the AI answers cite and prioritise those.
YouTube
- 'X vs Y' and 'X alternatives' videos with honest verdicts and timestamps.
- Setup and integration walkthroughs that match how buyers phrase prompts.
- Customer story videos with the customer's own numbers, transcripts published.
- Launch videos boosted modestly on day one to reach a meaningful first-day audience.
- Founder posts with real growth or product data, linking to the full write-up.
- LinkedIn articles that restate your comparison verdicts on a high-authority domain.
- Employee advocacy at launches so posts reach buyers in several networks at once.
- Short carousels summarising your data studies, which get reshared by operators.
X (Twitter)
- Build-in-public threads with metrics that journalists and bloggers quote.
- Replies to category conversations with useful specifics, not links.
- Changelog posts that show the product is alive, a recency signal engines read.
Link bait that works in this market
Assets built to be cited: by journalists, by other sites, and by the AI engines that read both.
Benchmark report from your own product data
Aggregated, anonymised usage numbers ('median time to close by deal size'). Trade blogs and engines cite original data.
Self-published 'best X software' roundupGrey area
A ranked, criteria-led list that includes you. Retrieved for the same fan-out queries as third-party roundups.
Free calculator or grader
A tool that answers a buyer's sub-question (cost, ROI, readiness). Earns links from Reddit and resource pages.
Pricing comparison trackerGrey area
A dated, sourced table of competitor pricing. Recency makes it a frequent citation for 'how much does X cost'.
Embeddable badge or widgetGrey area
A 'Powered by' badge or public scoreboard widget that customers embed, each one a mention and a link.
White hat, grey hat, and the line
AI visibility rewards whoever shows up in the sources first. Some of the fastest moves sit in a grey zone. Here is exactly where we stand.
White hat
What we do every time
- Answer-first use-case, integration, pricing and docs pages
- Earned placements on ranking 'best X software' lists
- Genuine customer reviews collected after success moments
- Original data studies with stated methodology
- Disclosed founder and team participation on Reddit
Slightly grey
What we do with care, in the open
- Alternatives and 'vs' pages for every competitor
- Your own 'best X software' list that includes you
- Parasite-style posts on LinkedIn, Medium and Substack
- Seeding the recommendation question you want answered
- Embeddable badges and widgets that carry a link
Never
The line we do not cross
- Fake or incentivised reviews on G2, Capterra or Reddit
- Undisclosed sockpuppet accounts
- Vote manipulation or bought upvotes
- PBNs and bulk paid links
- Comparison pages with invented competitor weaknesses
Questions from saas companies
What is AEO for SaaS?
Answer engine optimisation for SaaS is making sure your product is named, accurately, in the answers ChatGPT, Gemini, Perplexity, Claude and Google AI Overviews give to buyer prompts. It covers your own pages, review sites, listicles, Reddit and the links that tie them together.
Which matters more for SaaS AI visibility, G2 or Reddit?
They do different jobs. G2 and Capterra supply the shortlist and sentiment; Reddit supplies the candid detail engines quote. Most SaaS categories need both.
Do backlinks still matter if AI is answering the question?
Yes. AI answers retrieve from search indexes, so pages with links still rank and get retrieved. OBScoreboard's AI answers grew from 0 to 129 while referring domains grew from 145 to 921.
How do we know if we are being cited?
Track a fixed set of buyer prompts across engines over time, and use tools such as Ahrefs' AI responses reports to count mentions and cited pages. Measure share of answer against named competitors, not just presence.
Can a Reddit comment really end up in Google's answer?
Yes. In the GetMoreBacklinks case, Google's AI answer quotes a Reddit review of the product almost word for word.
Is it risky to publish competitor comparison pages?
Not if they are fair and sourced. Pages that misstate competitors lose trust with buyers and engines, and can create legal exposure. We write them to be quotable by a neutral reader.