How to Build an AEO Strategy for Your SaaS

This is the playbook. Not theory — a concrete, phase-by-phase plan for building AI visibility for your SaaS product over 90 days.

We've structured this as a 90-day plan because that's roughly how long it takes to go from "we should probably think about AI visibility" to "we have a measurable, repeatable AEO practice." Each phase builds on the previous one.

Whether you're a two-person startup or a growth-stage team, the playbook scales. Smaller teams just move through it more slowly.

Phase 1: Audit (Weeks 1-2)

Before you optimize anything, you need to know where you stand. This phase is about establishing a clear baseline.

Week 1: Check Your Current AI Visibility

Day 1-2: Run the Prompt Test

Open ChatGPT, Claude, Perplexity, and Gemini. Run these exact prompts (replacing [category] with your space):

• "What are the best [category] tools?"
• "Recommend a [category] tool for small businesses"
• "Compare [your product] to [top competitor]"
• "[Top competitor] alternatives"
• "Best [category] tool for [your primary use case]"
• "What is [your brand name]?"

Record every response. Note:

• Did your brand appear? In which platforms?
• If mentioned, what was the sentiment?
• Which competitors appeared most frequently?
• What specific attributes did AI highlight about each brand?

Day 3: Score Your Content

Run your homepage, top 3 landing pages, and documentation root through the AEO Score tool. Record each score and note the specific recommendations.

Common findings at this stage:

• Missing or incomplete JSON-LD structured data
• No LLMs.txt file
• Content that's marketing-heavy and fact-light
• Poor answer extractability (no clear, direct answers to common questions)

Day 4-5: Map the Competitive Landscape

Using your prompt test results, build a competitive matrix:

This matrix becomes your reference point for measuring progress.

Week 2: Identify Gaps

Content Gap Analysis

Compare what AI models say about you versus what you want them to say. Common gaps:

• AI doesn't know your key differentiators
• AI describes your product using outdated information
• AI mentions competitors' features but not yours
• AI doesn't associate your brand with specific use cases you serve

Structural Gap Analysis

Check for missing infrastructure:

• Do you have JSON-LD schema markup on key pages?
• Do you have an LLMs.txt file at your domain root?
• Is your content structured with clear headers, direct answers, and factual statements?
• Do you have comparison pages for key competitors?
• Do you have comprehensive FAQ content?

Document everything. This gap analysis drives your action plan for the next 10 weeks.

Phase 2: Foundation (Weeks 3-4)

Now you build the technical infrastructure that makes your content AI-accessible.

Week 3: Structured Data

Day 1: Generate and Deploy JSON-LD

Use the JSON-LD Generator to create schema markup for your key pages:

• Homepage: Organization schema with your name, description, founding date, social profiles, and key products
• Product pages: Product or SoftwareApplication schema with features, pricing, and reviews
• Blog posts: Article schema with author, date, and topic information
• FAQ pages: FAQPage schema (this directly feeds AI models' understanding of your brand)

Deploy all JSON-LD to your production site. Validate with Google's Rich Results Test.

Day 2-3: Create Your LLMs.txt

Use the LLMs.txt Generator to create a machine-readable summary of your brand. This file goes at yourdomain.com/llms.txt and tells AI crawlers what your product is, what it does, and who it's for.

Then create an LLMs-ctx file for richer context. This complements LLMs.txt with more detailed information about your product, features, use cases, and positioning.

Day 4-5: Technical Crawlability Check

Ensure AI crawlers can access your content:

• Check your robots.txt — are you blocking AI crawlers (GPTBot, ClaudeBot, PerplexityBot)?
• Verify your sitemap.xml is current and includes all important pages
• Ensure key pages aren't behind JavaScript-only rendering that crawlers can't parse
• Check that your content loads without requiring authentication or JavaScript execution

Week 4: Content Structure Cleanup

Go through your top 20 pages and restructure them for AI extractability:

• Add direct answer paragraphs. For every question your page addresses, include a clear 1-2 sentence answer near the top.
• Use descriptive headers. Replace "Our Solution" with "How [Product] Solves [Specific Problem]."
• Add factual specifics. Replace "trusted by thousands" with "used by 2,400+ teams including [Notable Customer]."
• Create FAQ sections. Add 3-5 relevant FAQs to key landing pages with schema markup.

Key takeaway: AI models extract information from your content. Make extraction easy by being clear, specific, and structured.

Phase 3: Content (Weeks 5-8)

This is where the heavy lifting happens. You need to create content that gives AI models reasons to recommend you.

Week 5-6: Comparison and Alternative Content

Create comparison pages for your top 5 competitors.

Each comparison page should include:

• A clear, factual summary of both products
• Feature-by-feature comparison table
• Honest assessment of where each product is stronger
• Specific use cases where your product is the better choice
• Pricing comparison
• JSON-LD schema markup

This content is critical because AI models reference comparison content heavily when making recommendations. If the only comparison between you and Competitor X is on Competitor X's website, you lose.

Create an "[Your Brand] Alternatives" page.

This sounds counterintuitive, but it works. When someone asks AI for alternatives to your product, you want to control the narrative. Create a page that honestly lists alternatives while positioning your product's unique strengths. AI models will reference your own alternatives page as a data source.

Week 6-7: FAQ and Knowledge Base Content

Build comprehensive FAQ content that covers every question a potential customer might ask AI:

• "What is [your product]?"
• "How much does [your product] cost?"
• "Is [your product] good for [use case]?"
• "What are the pros and cons of [your product]?"
• "[Your product] vs [each competitor]"
• "How to get started with [your product]"

Each FAQ should have a clear, direct answer in the first sentence, followed by supporting detail. Mark up everything with FAQPage JSON-LD schema.

Week 7-8: Technical Documentation and Ungated Resources

AI models love technical documentation because it's factual, specific, and structured. If you have an API, publish comprehensive docs. If you have integrations, document each one.

Critical principle: ungate everything that doesn't need to be gated. Content behind login walls is invisible to AI crawlers. Your getting-started guide, feature documentation, and educational content should all be publicly accessible.

Create at least:

• A comprehensive getting-started guide (public)
• Feature documentation for your top 5 features (public)
• Integration guides for your top integrations (public)
• A technical architecture overview (public)

Phase 4: Authority (Weeks 9-12)

Your own content is necessary but not sufficient. AI models weight third-party mentions heavily. This phase focuses on building external signals.

Week 9-10: Review Sites

Get listed and reviewed on major platforms:

• G2 — the most influential for B2B SaaS AI recommendations
• Capterra — broad reach, especially for SMB-focused products
• TrustRadius — valued for detailed, verified reviews
• Product Hunt — strong signal for newer products

Don't just create a listing — actively solicit reviews from happy customers. A product with 200 G2 reviews has dramatically more AI visibility than one with 15.

As we've shown in our research on hidden biases in AI recommendations, comparison site rankings directly influence what AI platforms recommend.

Week 10-11: Third-Party Content

Guest posts and contributed articles. Write for industry publications, not about your product, but about the problems your product solves. When you're known as a thought leader in your space, AI models associate your brand with expertise.

Target publications your audience reads. If you're a developer tool, write for dev.to, Hacker News (via blog posts worth sharing), and relevant Medium publications. If you're a marketing tool, target Marketing Brew, HubSpot's blog (guest posts), or Search Engine Journal.

Get included in listicles and roundups. Reach out to publishers who write "best [category] tools" articles. These articles are heavily referenced by AI models.

Week 11-12: Community Presence

Build presence where AI training data comes from:

• Stack Overflow (for developer tools) — answer questions related to your product category
• Reddit — participate authentically in relevant subreddits
• Industry forums and Slack communities
• GitHub (for technical products) — open-source tools, contribute to ecosystem

Each authentic mention in these communities creates a data point that AI models can reference.

Ongoing: Monitor, Iterate, Report

After the initial 90 days, AEO becomes an ongoing practice, not a project.

Weekly Monitoring

Re-run your prompt test suite weekly. Track changes in:

• Mention frequency across platforms
• Competitive share of voice
• Sentiment and positioning
• New prompts where you appear (or disappear)

Use the AI Visibility Index for category-level benchmarking.

Monthly Content Cadence

Publish at least 2-4 pieces of AEO-optimized content monthly:

• 1 comparison or alternative page
• 1 FAQ-rich educational piece
• 1-2 thought leadership pieces that build topical authority

Quarterly Review

Every 90 days, re-audit:

• Run full AEO Score checks on key pages
• Update your competitive matrix
• Review which content had the most impact on AI visibility
• Adjust strategy based on what's working

The 90-Day Checklist

Phase 1: Audit (Weeks 1-2)

• Run prompt tests across 4+ AI platforms
• Score top pages with AEO Score
• Build competitive visibility matrix
• Complete content gap analysis
• Complete structural gap analysis

Phase 2: Foundation (Weeks 3-4)

• Deploy JSON-LD schema on all key pages
• Create and deploy LLMs.txt
• Create and deploy LLMs-ctx
• Verify AI crawler accessibility
• Restructure top 20 pages for extractability

Phase 3: Content (Weeks 5-8)

• Publish 5 competitor comparison pages
• Create "[Brand] alternatives" page
• Build comprehensive FAQ content (30+ questions)
• Publish technical documentation (ungated)
• Create getting-started guide (ungated)

Phase 4: Authority (Weeks 9-12)

• Get listed on G2, Capterra, TrustRadius
• Solicit 50+ customer reviews
• Publish 3+ guest posts in industry publications
• Get included in 5+ "best of" listicles
• Build community presence (Reddit, Stack Overflow, etc.)

Ongoing

• Weekly prompt monitoring
• Monthly content publishing (2-4 pieces)
• Quarterly full re-audit
• Monthly stakeholder reporting

Why This Works

This playbook works because it addresses all the signals that AI models use to decide which brands to recommend:

• Structured data makes your brand machine-readable
• Comparison content puts you in the consideration set
• FAQ content gives AI models direct answers to reference
• Third-party mentions build the authority signals models weight heavily
• Monitoring ensures you catch and respond to changes

The brands that AI isn't currently recommending can change that outcome. It takes consistent effort over 90 days, but the compounding nature of AI visibility means the work you do now pays dividends for months and years.

Start with Phase 1 this week. Run the audit. See where you stand. Everything else follows from there.