PlaybookJune 17, 2026 · 9 min read

AEO for SaaS & B2B: How to Get Your Software Recommended by AI

Short answer: To get your SaaS recommended by AI, you need to be described consistently and verifiably across the sources AI trusts when buyers ask "best [category] software" or "alternatives to [competitor]." That means maintained profiles and reviews on G2, Capterra, and TrustRadius; clear comparison, "alternatives," and use-case content AI can cite; clean documentation; and structured product and pricing pages. AI synthesizes these into a multi-source consensus — your job is to make sure that consensus describes you accurately. AEO shapes the public signals AI reads; it doesn't edit the model's internals, and no one can guarantee a ranking.

How AI picks software to recommend

When someone types "what's the best project management tool for agencies?" into ChatGPT, Claude, Gemini, or Perplexity, the model isn't pulling from a single ranking. It draws on what it's indexed plus live retrieval, and for B2B software it leans heavily on a recognizable set of sources:

Third-party review platforms — G2, Capterra, TrustRadius, and similar directories that carry category lists, ratings, and detailed user reviews.
Comparison and "alternatives" content — "X vs Y" pages, "best [category] tools" roundups, and "alternatives to [competitor]" articles.
Community discussion — Reddit threads, Hacker News, and niche forums where practitioners compare tools candidly.
Your own documentation and product pages — docs, pricing, and feature pages that state plainly what the product does, who it's for, and what it costs.

The engine synthesizes these into a consensus. If three independent sources describe you the same way — a review site, a comparison article, and your own pricing page — the model treats that as a reliable, citable fact. Inconsistency or silence is what gets you left out. This is the core of answer engine optimization, applied to software buying.

This is a non-local playbook

If you run a local service business, the levers are different — Google Business Profile, location pages, and city-specific signals. (We cover that separately in our local guide.) For SaaS and B2B, geography barely matters. Buyers ask category and competitor questions, and AI answers from review platforms, comparison content, and docs. So that's where this playbook focuses.

1. Own your category and "alternatives / vs" queries

The highest-intent AI queries in B2B are comparative: "best [category] software," "alternatives to [competitor]," and "[competitor] vs [you]." If you don't publish credible content addressing these, AI fills the gap with whatever else it finds — usually your competitors' pages and roundups that may not even mention you.

Publish honest comparison pages ("[You] vs [Competitor]") that lay out real differences, including where the competitor is a better fit. AI rewards balanced, verifiable framing over marketing spin.
Create an "alternatives to [competitor]" page where you're a legitimate option — and earn mentions on third-party versions of the same.
Write use-case pages for specific buyer questions ("[category] for agencies," "for remote teams," "for HIPAA-regulated data") so the model can match you to the exact query.

For why this beats a traditional keyword-and-backlink approach, see AEO vs SEO.

2. Get and maintain reviews on G2, Capterra, and TrustRadius

Review platforms are among the most heavily weighted sources AI uses for software recommendations — they're structured, independent, and updated by real users. A thin or stale profile is a missed opportunity that often correlates with being left out of AI shortlists.

Claim and complete your profiles on G2, Capterra, and TrustRadius — accurate category, description, features, and pricing.
Run a steady review motion. A modest, recent stream of reviews tends to read as more credible than a burst of old ones. Ask happy customers at natural moments (renewal, milestone, support win).
Keep details consistent with your own site. If your pricing tiers or positioning differ across sources, you weaken the consensus the model is trying to form.

3. Publish content AI can actually cite

AI engines favor sources that are clear, specific, and verifiable. Vague "we're the leading platform" copy gives a model nothing to cite. Concrete, structured content does.

Comparison and roundup content with real feature tables and plain-language tradeoffs.
Use-case and integration pages that answer "does it work for my situation?" directly.
Clean documentation. Good docs aren't just for existing users — they're a rich, citable description of exactly what your product does. Make them crawlable and well-organized.

4. Add Product / SoftwareApplication schema

Structured data helps engines parse what you are without guessing. For SaaS, the most relevant types are SoftwareApplication (or Product) plus Offer for pricing and AggregateRating where you have legitimate ratings to reference. Add it to your homepage, product pages, and pricing page so the category, what the software does, and what it costs are machine-readable.

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "SoftwareApplication",
  "name": "Acme Analytics",
  "applicationCategory": "BusinessApplication",
  "operatingSystem": "Web",
  "offers": {
    "@type": "Offer",
    "price": "49.00",
    "priceCurrency": "USD"
  }
}
</script>

Schema doesn't guarantee placement — it makes the facts you want cited easier to extract accurately.

5. Add an llms.txt for your docs

An llms.txt file is a simple, plain-text map that points AI crawlers to your most important, citable pages — docs, key product pages, pricing, and comparison content. For a SaaS company with extensive documentation, it helps engines find the authoritative explanation of a feature instead of stitching together an answer from scattered or outdated pages. Pair it with not blocking reputable AI crawlers in your robots rules.

6. Be consistent everywhere AI looks

Consensus is the whole game. Your name, category, core value proposition, and pricing model should read the same across your site, your review profiles, comparison articles, press mentions, and community discussion. When a model cross-references three sources and they agree, you become the obvious, well-described option. When they conflict, the model hedges — or names a competitor whose story is cleaner.

Reddit and forums: you can't fabricate organic discussion, but you can earn it with a genuinely useful product and show up honestly when relevant.
Press and roundups: getting included in credible "best [category]" lists reinforces the consensus.
Your own site: keep positioning and pricing current so you don't contradict the third-party record.

A realistic expectation

None of this edits the model. AEO shapes the public signals AI reads, and engines change how they retrieve and weight sources over time — so think of this as improving your odds of being accurately described and recommended, not buying a ranking. The teams that win are the ones described consistently and verifiably across reviews, comparisons, docs, and their own pages.

Want to know if AI already recommends you? Run a free AI visibility scan → and see how ChatGPT, Gemini, and Perplexity describe your software in your category.