Own the comparison
Alternatives and vs prompts convert best in SaaS, and your rivals write the pages engines read. Publish fair comparison hubs AI can quote a verdict from.
Create Content →Solutions · By industry
Software buying now starts with an AI shortlist.
Buyers ask ChatGPT for the best tool in your category, and the answer names three vendors. Prefer shows which prompts you are missing from, which sources decide the shortlist, and what to publish to get named.
“best expense management software”Category prompt
Per engine
ChatGPTAbsent
PerplexityAbsent
ClaudeNamed
ChatGPTAbsentPerplexityAbsentAI OverviewsAbsent
ClaudeNamedNamed in the answer
Ramp
Brex
ExpensifyWho decides it
g2.comThe G2 category page is the most-quoted source
71% of category answers name 3 or fewer vendors. The answer is the shortlist.
The category math
Forty credible products. Three names in the answer.
How AI engines answer software questions, across 420 tracked SaaS domains.
71%of category prompts name 3 or fewer vendors
The shortlist is compressed before you ever see the buyer.
3.4×review sites and communities out-cite vendor pages
G2, Capterra and Reddit carry more weight than your site.
44%of SaaS brands are named but never cited
AI talks about them from other people's pages.
2-6 wksfrom shipped fix to measurable movement
One re-crawl cycle per engine, then the answers shift.
Prefer benchmark · 420 B2B SaaS domains · 6 engines · Q2 2026.
The shortlist · 01
If AI skips you, the buyer never sees you.
AI does not rank forty vendors. It names three, and those three are the only ones your buyer evaluates. Nothing tells you when you were left out.
“best expense management software”
ChatGPT, answered this morning.
For most companies, the strongest options are Ramp, which is free with 1% cashback and strong spend controls, Brex, best for venture-backed startups that want cards plus banking, and Expensify, the simplest choice for receipt scanning and reimbursements.
Sourcesg2.com
nerdwallet.comramp.com
3vendors named3vendors the buyer evaluates0times Ledgerly appears
Who decides · 02
The shortlist is written on six other websites.
Engines build software answers from review sites, listicles and community threads, not from vendor pages. Here is one tracked category, last 30 days.
Where citations come from
One tracked category, 240 prompts, last 30 days.
- Review sites
- 27%
- Earned listicles
- 21%
- Community
- 16%
- Competitor-owned
- 15%
- Reference & docs
- 12%
- Your owned pages
- 9%
DomainTypeCitation share
1g2.comListed 7th in category, no feature tagsReview site12.4%
2
reddit.comr/accounting and r/startups threadsCommunity10.1%
3nerdwallet.comNot listed, pitch draftedListicle8.6%
4ramp.comTheir /vs pages get quoted verbatimCompetitor7.9%
5
forbes.comListicle6.2%
6
capterra.comListed, profile 60% completeReview site5.4%
7
ledgerly.coYour comparison page, risingYour domain4.8%
8
trustradius.comReview site3.6%
Domains cited across 240 tracked prompts. Every row links to the exact answers that quoted it.
The prompt map · 03
Six prompt families decide a software deal.
Most teams watch two: category and competitor. The other four close deals, and most SaaS brands are absent from all of them.
Ledgerly, per family per engine
Status of the best prompt in each family.
CitedNamedAbsent
FamilyExample promptChatGPTPerplexityAIOClaudeWhat wins it
Categorybest expense management softwareG2 placement + a citable category page
CompetitorRamp vs Brex vs LedgerlyYour own honest /vs pages
Pricingcheapest corporate card for a startupA pricing page that states numbers
Integrationexpense tool that syncs with XeroDocs with setup steps per integration
SecuritySOC 2 expense platform with NetSuiteA public trust center engines can quote
Brandis Ledgerly any goodReview volume + the criticism fixed at source
Live from the Ledgerly demo workspace. Prefer generates the set from your category, rivals and personas, then runs it daily.
The playbook · 04
Six plays win software categories.
Each play maps to the Prefer tool that runs it, so this is a queue, not a PDF.
Fix the review-site record
G2, Capterra and TrustRadius are the most-cited sources in software. Your placement, feature tags and review count there go straight into answers.
Action Center →Answer pricing plainly
AI will not recommend what it cannot price. Name your tiers, limits and what triggers a sales call, and pricing prompts stop skipping you.
Optimise Content →Make the docs citable
Docs are the most trusted pages you own. Clean structure and schema turn them into the cited source for integration and limits questions.
Optimise Content →Show up in the threads
Reddit and Hacker News get read heavily for software questions. Prefer finds the exact threads deciding your answers so your team can join them.
Answer Engine Insights →Watch the shortlist move
Every rival named, per prompt, per engine, per week. When someone enters an answer you own, you hear about it that day.
Competition →PLG vs sales-led · 05
PLG and sales-led lose different answers.
Prefer ships a prompt set for each motion, so you defend the questions your revenue runs through.
Product-led
One buyer, deciding alone, often in a single session. Where you are exposed:
Free tier and pricing“cheapest, free plan, worth paying for”
Alternatives“X alternatives, switch from X”
Integration“works with Xero, Slack, QuickBooks”
Persona fit“for a 20-person startup, for freelancers”
Exposure: a rival's free tier named where yours is absent.
Sales-led
A committee buys over a quarter, and AI briefs every member. Where you are exposed:
Security and compliance“SOC 2, GDPR, HIPAA-ready”
Platform fit“works with NetSuite, SAP, Workday”
Company-size persona“for mid-market, for 500 employees”
Brand diligence“is X any good, X reviews, X pricing”
Exposure: the CFO's assistant repeats one bad review theme.
Proof · 06
One quarter. Measured, not guessed.
“We were ranking fine and still losing deals we never saw. Prefer showed us the three pages ChatGPT was actually reading about our category, none of them ours, and told us exactly what to publish.”
11% → 38%mention share on category prompts4th → 1stmost-cited vendor domain6 weeksto first measurable move+22%assisted signups from AI referrals
Questions
Asked plainly.
How do B2B SaaS companies get recommended by AI assistants?
By winning the sources those assistants read for software prompts: review-site category pages, community threads, listicles, and their own comparison, pricing and documentation pages. Engines answer best-software questions by reading a handful of pages, not ranking a results list, so the vendors named on those pages become the shortlist.
Why does AI visibility matter more for B2B SaaS?
Because software buying starts with a shortlist request. Answers to best-tool prompts name about three vendors, so a category of forty credible products compresses to three names, and vendors outside that set are never evaluated at all.
Which prompts should a SaaS company track?
Six families: category (best X software), competitor (X vs Y, X alternatives), pricing, integration, persona or company-size fit, and security or compliance. Prefer generates the set from your category and rivals, then adds the questions your sales team hears on calls.
Which sources do AI engines cite for software answers?
Review sites first, then listicles and community threads, then vendor pages. In Prefer's Q2 2026 benchmark of 420 SaaS domains, review sites took 27% of citations, listicles 21%, community 16%, and brands' own pages 9%.
Does this work for product-led and sales-led SaaS?
Yes, with different prompt sets. PLG wins or loses on free-tier, alternatives and integration prompts; sales-led on security, compliance, ERP fit and company-size prompts. Prefer tracks visibility per motion so you can see which one is exposed.
How does AEO fit with our SEO program?
It extends it. The same pages often serve both, but SEO earns a rank while AEO, also called generative engine optimization or GEO, earns a citation. Citations reward specificity, clean structure, schema and third-party corroboration more than keyword coverage.
How long until AI answers change?
Two to six weeks after changes ship, because each engine has to re-crawl the source and refresh its index. The first Prefer report takes about ten minutes, and technical fixes usually show within one re-crawl cycle.
What if our category is new or has no name?
New categories are the cheapest AI visibility to win, because no source is the authority yet. Prefer tracks the descriptive prompts buyers use before a label exists, the tool-that-does-X phrasings, and finds the pages engines fall back on.
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