SaaS AEO: How to Get Your Software Recommended by AI Search Engines

Why SaaS Companies Are Losing to AI Search

SaaS AEO — Answer Engine Optimization for software companies — is the practice of optimizing your software product's online presence so that AI search engines like ChatGPT, Claude, Perplexity, and Google AI Overviews recommend your tool when users ask for software recommendations. It is the SaaS-specific application of Answer Engine Optimization (AEO), and it is quickly becoming the most important marketing channel most SaaS companies are ignoring.

For over a decade, SaaS discovery followed a predictable path. Someone would search "best project management software," land on a G2 or Capterra comparison page, read reviews, and eventually click through to vendor websites. SEO, paid ads, and review site placement drove the funnel. That model is breaking down.

G2 reported declining organic traffic throughout 2025 as more software discovery queries moved to AI-powered answers. Capterra and similar review aggregators are experiencing the same trend. The reason is simple: when you can ask ChatGPT "what is the best CRM for a 20-person sales team with Slack integration?" and get a specific, contextualized answer in seconds, there is no reason to browse through 47 listings on a review site.

This shift hits SaaS companies differently than other industries. Software purchasing decisions are high-consideration, high-research activities. Buyers compare features, pricing, integrations, and use cases across multiple tools. AI search engines are increasingly becoming the first stop in that research process — and for many buyers, the only stop. When ChatGPT recommends three CRMs for a specific use case, buyers often go straight to those three vendor websites without ever visiting G2.

The problem is that most SaaS companies have optimized exclusively for traditional search and review sites. Their websites are full of vague marketing copy ("powerful, intuitive, scalable") that tells AI nothing useful. Their pricing is hidden behind "contact sales" buttons. Their feature pages are designed for human visitors clicking through tabs, not for AI crawlers extracting structured information. And almost none of them have SoftwareApplication schema markup that gives AI the structured data it needs to make confident recommendations.

The result: when someone asks AI for a software recommendation in your category, AI either skips your product entirely or describes it incorrectly based on incomplete data. Meanwhile, competitors who have invested in AEO are getting cited accurately and consistently. Every AI recommendation your competitor gets is a high-intent prospect you lose — and unlike a Google ranking, there is no page two. Either your software is in the AI's recommendation or it is not.

8 Steps to Get Your SaaS Recommended by AI Search Engines

Step 1: Implement SoftwareApplication Schema

SoftwareApplication schema is the most impactful technical change you can make for SaaS AEO. It is a specific type of structured data markup designed for software products. While most SaaS websites have either no schema or basic Organization schema, implementing SoftwareApplication schema tells AI exactly what your software does, what it costs, which platforms it runs on, and how users rate it — all in a machine-readable format that AI models prioritize over unstructured marketing copy.

What to include in your SoftwareApplication schema:

• Application name — the exact name users would search for, not a tagline or marketing phrase.
• Application category — use standard categories like "ProjectManagementApplication," "BusinessApplication," or "FinanceApplication." This helps AI match your tool to category-level queries.
• Operating system — Web, iOS, Android, Windows, macOS, or any combination. Critical for queries like "best Mac project management app."
• Pricing and offers — include every pricing tier with the exact price, currency, and billing cycle. AI needs this to answer "best X under $50/month" queries.
• Aggregate rating and review count — the most powerful trust signal. A tool with 4.6 stars from 2,400 reviews is far more likely to be cited than one with no rating data.
• Feature list — enumerate your core features as structured data. AI uses this to match your tool to specific feature requirements.
• Screenshot or image — a representative product screenshot URL helps with visual citation contexts.

Here is a complete SoftwareApplication schema template you can adapt for your SaaS product. You can also generate this with our free schema generator tool:

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "SoftwareApplication",
  "name": "ProjectFlow",
  "applicationCategory": "ProjectManagementApplication",
  "operatingSystem": "Web, iOS, Android",
  "description": "ProjectFlow is a project management tool designed for remote teams of 10-200 people. Features include task management, time tracking, Gantt charts, Slack and Jira integrations, and real-time collaboration. Used by 5,000+ teams.",
  "url": "https://projectflow.io",
  "screenshot": "https://projectflow.io/images/dashboard.png",
  "featureList": [
    "Task management with subtasks and dependencies",
    "Built-in time tracking",
    "Gantt charts and timeline views",
    "Slack, Jira, and GitHub integrations",
    "Real-time collaboration and comments",
    "Custom workflows and automation",
    "Resource allocation and workload management",
    "Reporting and analytics dashboard"
  ],
  "offers": [
    {
      "@type": "Offer",
      "name": "Starter",
      "price": "0",
      "priceCurrency": "USD",
      "description": "Free for up to 5 users. Includes task management and basic reporting.",
      "availability": "https://schema.org/InStock"
    },
    {
      "@type": "Offer",
      "name": "Pro",
      "price": "12",
      "priceCurrency": "USD",
      "description": "Per user/month billed annually. Includes time tracking, Gantt charts, and integrations.",
      "availability": "https://schema.org/InStock"
    },
    {
      "@type": "Offer",
      "name": "Enterprise",
      "price": "29",
      "priceCurrency": "USD",
      "description": "Per user/month billed annually. Includes SSO, advanced permissions, priority support, and custom integrations.",
      "availability": "https://schema.org/InStock"
    }
  ],
  "aggregateRating": {
    "@type": "AggregateRating",
    "ratingValue": "4.6",
    "reviewCount": "2847",
    "bestRating": "5",
    "worstRating": "1"
  },
  "author": {
    "@type": "Organization",
    "name": "ProjectFlow Inc.",
    "url": "https://projectflow.io"
  }
}
</script>

Notice the level of detail. This schema includes the software name, category, specific description with measurable facts, platform availability, complete pricing for every tier, feature list, and aggregate ratings. Every field gives AI another data point to cite your software confidently. The difference between having this schema and not having it is the difference between AI saying "ProjectFlow is a project management tool for remote teams, starting at $12/user/month with a free tier for up to 5 users" and AI not mentioning your product at all.

Step 2: Build Comparison Pages That AI Can Extract

Comparison queries are the bread and butter of SaaS discovery. "Asana vs Monday," "alternatives to Salesforce," "best Slack alternatives for small teams." These are among the highest-intent queries in SaaS — and AI search engines answer them constantly. If you do not have comparison content on your own site, you are leaving this entire query category to competitors and third-party review sites.

Types of comparison pages to build:

• "X vs Y" pages: Create direct comparison pages for your product versus each major competitor. "ProjectFlow vs Asana: Which Is Better for Remote Teams?" Include specific feature comparisons, pricing breakdowns, use-case recommendations, and honest assessments of where each tool excels.
• "Alternatives to Z" pages: These capture users actively looking to switch. "Best Alternatives to Monday.com in 2026" should list your tool alongside other alternatives with clear differentiators. AI heavily references this format.
• Category comparison pages: "Best Project Management Tools for Remote Teams" — position your product within the broader category. Include 5-7 competitors with honest pros, cons, pricing, and best-for-whom recommendations.
• Feature comparison tables: Build detailed feature-by-feature comparison tables with checkmarks and specific values. AI extracts tabular data efficiently and uses it to make feature-specific recommendations.

The key principle is honesty and specificity. AI trusts balanced comparison content far more than content that only promotes your product. Acknowledge where competitors are stronger. Include real pricing for all tools. State specific use cases where each tool excels. This builds the kind of authority that AI models use when choosing which sources to cite. When writing content for AI, comparison pages are among the highest-value content types you can create.

Step 3: Make Pricing Transparent and Schema-Rich

Pricing is one of the most common qualifiers in SaaS recommendation queries. "Best CRM under $50/month." "Free project management tools." "Affordable email marketing for startups." If your pricing is hidden behind a "contact sales" or "request a demo" button, AI literally cannot recommend you for any price-qualified query. And price-qualified queries make up a substantial portion of all SaaS recommendation searches.

How to optimize your pricing for AI visibility:

• Publish exact prices on your website. If you have per-user pricing, state the exact per-user price. If you have tiered pricing, show every tier with its exact price. "Starting at $12/user/month" is infinitely more useful to AI than "Contact us for pricing."
• Include pricing in your SoftwareApplication schema. Use the Offer type to mark up each pricing tier with price, currency, billing cycle description, and what is included. This is the structured data AI uses to answer price-comparison queries.
• Create a dedicated pricing page with structured data. Your pricing page should be crawlable, server-rendered, and include schema markup for every plan. Do not build pricing as a JavaScript-only interactive component — AI crawlers may not execute JavaScript.
• Document what each tier includes. "Pro plan — $29/month per user. Includes: unlimited projects, time tracking, Gantt charts, Slack integration, priority support, 100GB storage." The more specific you are about what each tier includes, the better AI can match your plans to specific user requirements.
• Mention free tiers and trials prominently. "Free tier for up to 5 users" or "14-day free trial, no credit card required" are strong signals AI uses when recommending tools for budget-conscious users or startups.

The SaaS companies that resist pricing transparency are increasingly being left out of AI recommendations entirely. When ChatGPT is asked "best CRM under $50/month," it can only recommend tools where it knows the price. Your "contact sales" page does not make the cut. This is one of the biggest SaaS AEO advantages available right now — most enterprise and mid-market SaaS tools still hide their pricing, creating an enormous opportunity for transparent competitors.

Step 4: Document Every Feature in Detail

SaaS feature pages are typically written for marketing — vague benefit statements, aspirational language, and stock photos. AI cannot work with "Empower your team with our powerful collaboration suite." It needs "Real-time document co-editing with version history, @mentions, inline comments, and 50+ file format support including PDF, Word, and Markdown."

How to document features for AI visibility:

• Create a dedicated page for every major feature. Not a feature section on your homepage — a full page. A page titled "Time Tracking in ProjectFlow" with 800+ words of specific, factual content about how time tracking works, what it integrates with, and who it serves gives AI exactly the data it needs.
• Use specific, measurable language. "Supports up to 500 team members per workspace" is citable. "Scales with your team" is not. "Integrates with 40+ tools including Slack, Jira, GitHub, Salesforce, and HubSpot" is citable. "Connects with your favorite tools" is not. AI recommends specific capabilities, not marketing promises.
• Include technical specifications. API rate limits, storage limits, supported file types, export formats, user permission levels — these details matter when AI is answering specific technical queries about your software.
• Add FAQ schema to feature pages. Every feature page should have FAQ schema answering the three to five most common questions about that feature. "Does ProjectFlow time tracking integrate with QuickBooks?" "Can I set billable rates per team member?" These are the exact questions people are asking AI.

Think of each feature page as an answer to the question: "Does [your software] have [specific feature] and how does it work?" When someone asks ChatGPT that question, your feature page should be the definitive source AI cites. Check how your content performs for AI with our free content checker tool.

Step 5: Create Use Case Pages by Industry and Role

SaaS recommendation queries are frequently qualified by industry or role. "Best project management tool for marketing agencies." "Best CRM for real estate agents." "Best accounting software for SaaS companies." If you serve multiple industries or roles, each one needs its own dedicated page — because AI matches content to queries with high specificity. For example, if your SaaS serves the education sector, understanding how schools and course creators approach AI search can help you speak their language — see our Education AEO guide for that perspective. Similarly, if your SaaS serves the cryptocurrency or blockchain space, our cryptocurrency AEO guide covers the unique trust signals and compliance requirements that crypto audiences and AI models expect.

How to build use case pages:

• Create a page for every industry you serve. "ProjectFlow for Marketing Agencies" should explain exactly how your tool solves marketing agency challenges: campaign tracking, client management, creative review workflows, deadline management. Include specific examples and, if possible, customer quotes or case study data.
• Create a page for every role you serve. "ProjectFlow for Product Managers," "ProjectFlow for Engineering Teams," "ProjectFlow for Freelancers" — each role has different needs, and AI needs content that maps to those specific needs.
• Create a page for every team size. "ProjectFlow for Small Teams (2-10)," "ProjectFlow for Growing Companies (50-200)," "ProjectFlow for Enterprise (500+)" — team size is a frequent query qualifier. Each page should explain pricing, features, and suitability for that team size.
• Include specific use cases and workflows. Do not just say "great for marketing agencies." Describe specific workflows: "Marketing agencies use ProjectFlow to manage campaign timelines with Gantt charts, track billable hours per client with built-in time tracking, and run creative reviews with inline commenting on uploaded designs." This level of specificity is what AI needs to make confident recommendations.

The more use case pages you build, the more query surface area you cover. A SaaS product with a single homepage and a features page has a tiny AEO footprint. A product with 20 use case pages, 10 comparison pages, and detailed feature pages has an enormous footprint that matches a wide range of AI queries.

Step 6: Build a Public Knowledge Base

Your help documentation is one of the most underrated AEO assets in SaaS. A comprehensive, publicly accessible knowledge base serves as a massive corpus of structured, factual content about your software that AI models can crawl and reference. Every help article is a potential source AI can cite when answering specific questions about your product.

Knowledge base optimization for AI:

• Make your knowledge base public. If your help docs are behind a login wall, AI cannot access them. Make all product documentation publicly crawlable. This is one of the easiest and highest-impact changes — many SaaS companies gate their docs unnecessarily.
• Structure articles as questions and answers. "How to set up Slack integration in ProjectFlow" is a title that maps directly to an AI query. Structure each article with a clear question-as-title, a direct answer in the first paragraph, and step-by-step details below.
• Add HowTo schema to tutorial articles. Articles that walk through a process ("How to create a Gantt chart in ProjectFlow") should include HowTo schema markup with named steps. AI models use this schema to answer how-to questions about specific software.
• Include integration documentation. Dedicated pages for each integration ("ProjectFlow + Slack Integration") answer one of the most common SaaS query types: "does [X] integrate with [Y]?" AI needs a clear, crawlable page for each integration.
• Keep documentation current. Outdated documentation is worse than no documentation for AI. If AI cites incorrect information from your help docs, it damages both AI trust in your source and user trust in the AI recommendation. Review and update docs with every product release.

Companies like Notion, Linear, and Stripe have excellent public documentation that AI models frequently cite. If your docs are behind a login, paywalled, or sparse, you are giving AI no factual content to cite — and it will recommend competitors whose documentation is comprehensive and accessible.

Step 7: Collect and Structure Reviews

Reviews are the social proof backbone of AI software recommendations. When ChatGPT recommends a CRM, it is not randomly picking tools — it is evaluating review data across multiple platforms to determine which tools are genuinely well-regarded by users. Review volume, ratings, recency, and consistency across platforms all factor into whether AI cites your software.

Your SaaS review strategy should include:

• G2 and Capterra presence. These remain primary data sources for AI when evaluating SaaS products. Ensure your G2 and Capterra profiles are complete with accurate feature lists, pricing, screenshots, and an active review collection program. Aim for at least 100 reviews on each platform.
• On-site testimonials with schema. Collect customer reviews and testimonials on your own website and mark them up with Review schema. Include the reviewer's role, company size, and use case — AI uses these details to match reviews to specific recommendation queries.
• Case studies as structured content. Full case studies with quantifiable results ("Marketing agency reduced project delivery time by 35% using ProjectFlow") give AI citable success data. Structure case studies with clear problem/solution/results sections.
• Trustpilot and Product Hunt. Depending on your market, these platforms contribute to AI's review aggregation. Product Hunt is particularly valuable for developer tools and startup-focused SaaS.
• Respond to reviews. Active engagement with reviews on G2, Capterra, and other platforms signals an active, responsive company. AI factors in recency and vendor engagement when evaluating review credibility.

The compounding effect matters enormously. A SaaS tool with 500 G2 reviews, 300 Capterra reviews, 50 on-site testimonials, a 4.5+ rating across platforms, and 10 detailed case studies creates an overwhelming body of evidence that AI will cite with confidence. A competitor with 12 reviews on one platform simply cannot compete.

Step 8: Add llms.txt for AI Context

An llms.txt file is a plain text file at your website root that gives AI models a structured overview of your software product. For SaaS companies, this is your chance to hand AI a concise, well-organized product brief that covers everything it needs to recommend your software accurately.

Here is an llms.txt template for a SaaS product:

# ProjectFlow

> ProjectFlow is a project management platform built for remote
> teams of 10-200 people. It combines task management, time tracking,
> Gantt charts, and real-time collaboration in a single tool.
> Used by 5,000+ teams including remote-first companies, marketing
> agencies, and software development teams.

## Core Features
- Task management with subtasks, dependencies, and custom fields
- Built-in time tracking with billable hours and client reporting
- Gantt charts and timeline views with drag-and-drop scheduling
- Real-time collaboration: comments, @mentions, file sharing
- Custom workflows and automation rules
- Resource allocation and workload management
- Reporting and analytics dashboard with export

## Integrations
- Slack (two-way sync, notifications, slash commands)
- Jira (import, sync, bidirectional updates)
- GitHub (PR linking, commit tracking, issue sync)
- Salesforce, HubSpot (CRM deal-to-project conversion)
- Google Workspace, Microsoft 365 (calendar, drive, docs)
- Zapier (1,000+ app connections)
- REST API (full CRUD, webhooks, rate limit: 1,000/min)

## Pricing
- Free: Up to 5 users. Task management, basic reporting.
- Pro: $12/user/month (annual). Time tracking, Gantt, integrations.
- Enterprise: $29/user/month (annual). SSO, audit log, priority
  support, custom integrations, 99.9% SLA.

## Best For
- Remote teams needing async collaboration tools
- Marketing agencies managing multiple client projects
- Software teams wanting Jira alternative with time tracking
- Growing companies (50-200) needing resource management

## Key Pages
- [Features](https://projectflow.io/features): Full feature overview
- [Pricing](https://projectflow.io/pricing): All plans and comparison
- [Integrations](https://projectflow.io/integrations): 40+ integrations
- [Docs](https://docs.projectflow.io): Full help documentation
- [API](https://api.projectflow.io): Developer documentation
- [Security](https://projectflow.io/security): SOC 2, GDPR, encryption
- [Customers](https://projectflow.io/customers): Case studies

## Company
- Founded 2022, San Francisco (remote-first)
- 45 employees
- SOC 2 Type II certified, GDPR compliant
- 99.9% uptime SLA
- 4.6 stars on G2 (2,847 reviews)
- 4.5 stars on Capterra (1,203 reviews)

This gives AI a complete product brief in under 60 lines. It covers features, integrations, pricing, ideal customers, key pages, and company credentials. Most SaaS competitors do not have an llms.txt file, so adding one creates an immediate advantage. The file takes about an hour to create and sits at your website root (yourdomain.com/llms.txt). It is one of the highest-ROI AEO activities for SaaS companies.

Also verify that your robots.txt allows AI crawlers like GPTBot, ClaudeBot, and PerplexityBot to access your marketing site, documentation, and pricing pages. If you are blocking these crawlers — which many SaaS companies do by default — none of your other AEO optimizations matter.

What AI Engines Look for in SaaS Recommendations

Understanding how AI models evaluate SaaS products helps you prioritize your optimization work. Based on how AI search engines synthesize their recommendations, three factors consistently determine which software gets cited.

Specificity Over Marketing Language

AI models are trained to distinguish between factual, specific claims and vague marketing copy. When your website says "powerful project management for modern teams," AI extracts almost zero useful information. When it says "task management with subtasks, dependencies, and custom fields for teams of 10-200," AI has specific features and a target audience it can match to queries.

Every claim on your website should pass the "specificity test": can AI cite this statement as a factual answer to a specific user question? "Built-in time tracking with billable hours, client reporting, and QuickBooks export" passes. "Streamline your workflow with our time management suite" fails. Review every page on your site and replace vague statements with specific, citable facts.

Pricing Clarity

Pricing is arguably the single most important data point AI needs for SaaS recommendations, because price is the most common qualifier in software queries. "Best X under $50/month," "free X for startups," "most affordable X" — these queries are impossible for AI to answer if it does not know your price.

AI models assess pricing clarity across multiple dimensions: Is the price visible on the website? Is it marked up in structured data? Are all tiers documented with specific inclusions? Is the billing cycle clear (monthly vs annual)? Is there a free tier or trial? SaaS companies that publish transparent, schema-marked pricing have a massive advantage over those that hide pricing behind demo requests.

Feature Depth and Documentation

The depth of your feature documentation directly correlates with how confidently AI can recommend your software for specific use cases. A SaaS product with 50 detailed feature and integration pages gives AI an enormous corpus of factual content to draw from. A product with a single features page listing bullet points gives AI almost nothing.

AI also cross-references your feature claims against third-party sources. If your website says you integrate with Slack, AI looks for confirmation in your help docs, G2 reviews, and integration directories. Consistent feature information across multiple sources builds the kind of confidence AI needs to make a recommendation. This is why keeping your G2 profile, help docs, and marketing site aligned is so important.

Common SaaS AEO Mistakes

Even SaaS companies that invest heavily in marketing make specific mistakes that destroy their AI visibility. Here are the most common ones and how to fix them.

Hiding Pricing Behind "Contact Sales"

This is the number one SaaS AEO mistake. If AI does not know your price, it cannot recommend you for any price-qualified query — which eliminates the majority of SaaS recommendation searches. Every competitor with visible pricing gets recommended instead. Even if you have custom enterprise pricing, publish starting prices for your self-serve tiers and include them in schema markup.

Vague Feature Descriptions

"Powerful automation," "seamless integrations," "intuitive interface" — these phrases are invisible to AI because they contain zero specific, citable information. Replace every vague benefit statement with a specific capability description. "Automation engine with 50+ trigger types, conditional logic, and integrations with Slack, email, and webhooks" is what AI needs.

No Structured Data

The vast majority of SaaS marketing sites have no SoftwareApplication schema. Many do not even have basic Organization schema. Without structured data, AI has to parse your marketing copy to extract product information — and marketing copy is designed to persuade humans, not inform AI. Implement SoftwareApplication schema on your homepage or product page, and Offer schema on your pricing page. Use our free AEO scanner to audit your current structured data.

Gated Documentation

If your help docs, API documentation, or knowledge base requires a login to access, AI cannot crawl it. This eliminates one of the largest and most valuable content sources for AI recommendations. Make all product documentation publicly accessible. There is almost no competitive risk — anyone can sign up for a free trial and see your docs anyway — and the AEO benefit is enormous.

No Comparison Content

SaaS companies are often afraid to mention competitors on their website. This is the wrong instinct for AEO. Comparison queries ("X vs Y," "alternatives to Z") are among the highest-intent SaaS searches, and AI needs comparison content to form its recommendations. If the only comparison content about your software lives on G2, you have no control over how your product is positioned. Create your own honest, detailed comparison pages.

Client-Side Only Rendering

Many SaaS marketing sites are built with heavy JavaScript frameworks where content loads client-side. AI crawlers do not always execute JavaScript. If your pricing page, feature pages, or documentation render content only via JavaScript, AI crawlers may see empty pages. Ensure critical content is server-rendered. Test by viewing your page source — if you do not see your content in the HTML, neither does AI.

Frequently Asked Questions

The SaaS Companies That Start Now Will Win

AI-powered software recommendations are not a future trend. They are happening right now, at massive scale. Decision-makers at every level — from startup founders to enterprise procurement teams — are asking ChatGPT, Claude, Perplexity, and Google AI Overviews to recommend software for their specific needs. And those AI models are pulling from product websites, review platforms, documentation, comparison content, and structured data to form their answers.

The vast majority of SaaS companies have done zero AEO work. They are relying on vague marketing copy that tells AI nothing useful. They are hiding pricing behind demo request forms. They are gating their documentation behind login walls. They have no SoftwareApplication schema, no comparison content, and no llms.txt file. They are optimized for a world where G2 and Google AdWords drive software discovery — a world that is rapidly ending.

Every step you implement from this guide puts distance between you and your competitors. Start with the highest-impact changes: implement SoftwareApplication schema with your full pricing and feature data (Step 1), publish transparent pricing with schema markup (Step 3), and create an llms.txt file describing your product (Step 8). These three changes alone can dramatically increase your AI visibility.

Then build out the remaining steps: comparison pages, detailed feature documentation, use case pages by industry and role, a public knowledge base, and a systematic review collection strategy. Each step compounds. Together, they create a software discovery channel that grows as AI search grows — and AI search is growing faster than any distribution channel in SaaS history.

Want to see how AI-visible your SaaS product is right now? Scan your site free with Vida AEO and get your AI visibility score in under 60 seconds. See exactly which optimizations you need and where to start.