ai-seo

• Do you know if your brand appears in AI-generated answers today?
• Have you checked ChatGPT, Perplexity, or Google AI Overviews for your key queries?
• What queries matter most to your business?

• What type of content do you produce? (Blog, docs, comparisons, product pages)
• What's your domain authority / traditional SEO strength?
• Do you have existing structured data (schema markup)?

• Get cited as a source in AI answers?
• Appear in Google AI Overviews for specific queries?
• Compete with specific brands already getting cited?
• Optimize existing content or create new AI-optimized content?

• Who are your top competitors in AI search results?
• Are they being cited where you're not?

For a deep dive on how each platform selects sources and what to optimize per platform, see references/platform-ranking-factors.md.

Traditional SEO gets you ranked. AI SEO gets you cited.

In traditional search, you need to rank on page 1. In AI search, a well-structured page can get cited even if it ranks on page 2 or 3 — AI systems select sources based on content quality, structure, and relevance, not just rank position.

Critical stats:

• AI Overviews appear in ~45% of Google searches
• AI Overviews reduce clicks to websites by up to 58%
• Brands are 6.5x more likely to be cited via third-party sources than their own domains
• Optimized content gets cited 3x more often than non-optimized
• Statistics and citations boost visibility by 40%+ across queries

This is important to read once before doing anything else.

Google's position (AI features optimization guide):

"The best practices for SEO continue to be relevant because our generative AI features on Google Search are rooted in our core Search ranking and quality systems."

Google explicitly says:

• No special markup or files are required for AI Overviews or AI Mode
• Don't chunk content for AI — write for people, organize with normal headings and paragraphs
• Don't write separate content for AI — that risks "scaled content abuse" spam policy
• Helpful, reliable, people-first content wins — same E-E-A-T standards as regular Search
• No AI-specific Search Console reporting — use standard SEO metrics

Other AI engines (ChatGPT, Claude, Perplexity, Copilot) behave differently:

• They actively reward extractable structure — passages, FAQs, comparison tables, definition blocks
• They parse llms.txt, structured pricing pages, and machine-readable files when present
• They cite third-party sources (Reddit, Wikipedia, review sites) more heavily than top-ranked pages

What this means for the work:

• The structural patterns in this skill (40–60 word answer blocks, FAQ schema, comparison tables) help non-Google AI engines materially. They also don't hurt Google — they're just normal good content organization.
• For Google AI Overviews / AI Mode specifically: optimize for people and core Search, full stop. Strong E-E-A-T, original information, semantic HTML, clean indexability.
• For ChatGPT/Claude/Perplexity: layer on the extractable structure + llms.txt + machine-readable files.

When in doubt, default to "write for people, organize for clarity" — that satisfies both camps.

Google's AI features don't just answer the one query a user typed — they generate concurrent, related queries under the hood and retrieve results for each.

Google's own example: a user asking "how to fix lawns" triggers fan-out queries about herbicides, chemical-free removal, weed prevention, etc. The AI synthesizes across all of them.

Implications:

• Single-page-per-keyword targeting is less effective. Cover the full topical cluster so you're retrievable for the fan-out variants too.
• Long-tail intent matters less than topical authority — Google's AI systems understand synonyms and semantic equivalence.
• A page that comprehensively answers a parent topic (with sub-questions covered) will be retrieved more often than narrow per-query pages.

Action: when planning content, brainstorm the 5–10 related queries the AI is likely to fan out to and make sure your content (or your site as a whole) covers them.

ChatGPT fans out too — and you can extract its literal background queries for your niche via DevTools (method in references/format-volatility.md). Post-5.6, ChatGPT's fan-outs shifted away from "best/vs/top" modifiers toward site: and "official" searches — use the extraction to see where your category's fan-outs stand today.

Test 10-20 of your most important queries across platforms:

Query types to test:

• "What is [your product category]?"
• "Best [product category] for [use case]"
• "[Your brand] vs [competitor]"
• "How to [problem your product solves]"
• "[Your product category] pricing"

When your competitors get cited and you don't, examine:

• Content structure — Is their content more extractable?
• Authority signals — Do they have more citations, stats, expert quotes?
• Freshness — Is their content more recently updated?
• Schema markup — Do they have structured data you're missing?
• Third-party presence — Are they cited via Wikipedia, Reddit, review sites?

For each priority page, verify:

Verify your robots.txt allows AI crawlers. Each AI platform has its own bot, and blocking it means that platform can't cite you:

• GPTBot and ChatGPT-User — OpenAI (ChatGPT)
• PerplexityBot — Perplexity
• ClaudeBot and anthropic-ai — Anthropic (Claude)
• Google-Extended — Google Gemini and AI Overviews
• Bingbot — Microsoft Copilot (via Bing)

Check your robots.txt for Disallow rules targeting any of these. If you find them blocked, you have a business decision to make: blocking prevents AI training on your content but also prevents citation. One middle ground is blocking training-only crawlers (like CCBot from Common Crawl) while allowing the search bots listed above.

See references/platform-ranking-factors.md for the full robots.txt configuration.

AI systems extract passages, not pages. Every key claim should work as a standalone statement.

Content block patterns:

• Definition blocks for "What is X?" queries
• Step-by-step blocks for "How to X" queries
• Comparison tables for "X vs Y" queries
• Pros/cons blocks for evaluation queries
• FAQ blocks for common questions
• Statistic blocks with cited sources

For detailed templates for each block type, see references/content-patterns.md.

Structural rules:

• Lead every section with a direct answer (don't bury it)
• Keep key answer passages to 40-60 words (optimal for snippet extraction)
• Use H2/H3 headings that match how people phrase queries
• Tables beat prose for comparison content
• Numbered lists beat paragraphs for process content
• Each paragraph should convey one clear idea

AI systems prefer sources they can trust. Build citation-worthiness.

The Princeton GEO research (KDD 2024, studied across Perplexity.ai) ranked 9 optimization methods:

Best combination: Fluency + Statistics = maximum boost. Low-ranking sites benefit even more — up to 115% visibility increase with citations.

Statistics and data (+37-40% citation boost)

• Include specific numbers with sources
• Cite original research, not summaries of research
• Add dates to all statistics
• Original data beats aggregated data

Expert attribution (+25-30% citation boost)

• Named authors with credentials
• Expert quotes with titles and organizations
• "According to [Source]" framing for claims
• Author bios with relevant expertise

Freshness signals

• "Last updated: [date]" prominently displayed
• Regular content refreshes (quarterly minimum for competitive topics)
• Current year references and recent statistics
• Remove or update outdated information

E-E-A-T alignment

• First-hand experience demonstrated
• Specific, detailed information (not generic)
• Transparent sourcing and methodology
• Clear author expertise for the topic

AI systems don't just cite your website — they cite where you appear.

Third-party sources matter more than your own site:

• Wikipedia mentions (7.8% of all ChatGPT citations)
• Reddit discussions (volatile: ~1.8% of ChatGPT citations historically, but nearly wiped from ChatGPT by Aug 2026 retrieval changes — still retrieved elsewhere; see the volatility section in references/agent-readiness.md)
• Industry publications and guest posts
• LinkedIn — per LinkedIn's own AEO guide, the most-cited outlet for professional-topic searches; Articles out-cite Posts ~60/40, and a post's first words become its URL slug, so front-load the target phrase (details in references/format-volatility.md)
• Review sites (G2, Capterra, TrustRadius for B2B SaaS)
• YouTube (frequently cited by Google AI Overviews)
• Podcasts (episodes get transcribed, show notes published — both get crawled and cited)
• Quora answers

Actions:

• Ensure your Wikipedia page is accurate and current
• Participate authentically in Reddit communities — but as one surface in a portfolio, never the whole strategy (citation mixes shift overnight with retrieval updates)
• Get featured in industry roundups and comparison articles
• Maintain updated profiles on relevant review platforms
• Create YouTube content for key how-to queries — models don't watch the video, they read the text layer around it; see references/youtube-ai-citations.md for the full anatomy (transcript, captions, chapters, description, pinned comment)
• Guest on podcasts in your category (prep with the public-relations skill's podcast guest prep)
• Answer relevant Quora questions with depth

Google's stance: not required for AI Overviews or AI Mode. Their guide explicitly says you don't need new markup, AI files, or markdown to appear in generative AI search.

Why include them anyway: non-Google AI engines (ChatGPT, Claude, Perplexity) and autonomous buying agents do reward extractable structure. The files below help with those engines without harming Google.

AI agents aren't just answering questions — they're becoming buyers. When an AI agent evaluates tools on behalf of a user, it needs structured, parseable information. If your pricing is locked in a JavaScript-rendered page or a "contact sales" wall, agents will skip you and recommend competitors whose information they can actually read.

Audit this layer first: references/agent-readiness.md — the access/discovery/parseability checklist, free scoring tools (npx is-agentic, Frase's checker), Markdown content negotiation + Link headers, llms-full.txt, and the emerging agent-actionable layer (WebMCP).

Add these machine-readable files to your site root:

/pricing.md or /pricing.txt — Structured pricing data for AI agents

# Pricing — [Your Product Name]

## Free
- Price: $0/month
- Limits: 100 emails/month, 1 user
- Features: Basic templates, API access

## Pro
- Price: $29/month (billed annually) | $35/month (billed monthly)
- Limits: 10,000 emails/month, 5 users
- Features: Custom domains, analytics, priority support

## Enterprise
- Price: Custom — contact sales@example.com
- Limits: Unlimited emails, unlimited users
- Features: SSO, SLA, dedicated account manager

Why this matters now:

• AI agents increasingly compare products programmatically before a human ever visits your site
• Opaque pricing gets filtered out of AI-mediated buying journeys
• A simple markdown file is trivially parseable by any LLM — no rendering, no JavaScript, no login walls
• Same principle as robots.txt (for crawlers), llms.txt (for AI context), and AGENTS.md (for agent capabilities)

Best practices:

• Use consistent units (monthly vs. annual, per-seat vs. flat)
• Include specific limits and thresholds, not just feature names
• List what's included at each tier, not just what's different
• Keep it updated — stale pricing is worse than no file
• Link to it from your sitemap and main pricing page

/llms.txt — Context file for AI systems (see llmstxt.org)

If you don't have one yet, add an llms.txt that gives AI systems a quick overview of what your product does, who it's for, and links to key pages (including your pricing).

/okf/ — Open Knowledge Format bundle (Google-backed, v0.1)

Google introduced OKF in June 2026 — a markdown spec for representing site content as a directory of cross-linked files with YAML frontmatter, agent-readable without scraping. Built primarily for data-team catalog metadata; the site-readable-by-agents repurposing was popularized by Suganthan Mohanadasan. No confirmed AI-search ranking signal today — treat it as protocol-layer registration like early schema.org. For the full breakdown, implementation paths (free generator, WordPress plugin, by-hand), hosting guidance, and when to skip, see references/okf.md.

Structured data helps AI systems understand your content. Key schemas:

Content with proper schema shows 30-40% higher AI visibility on non-Google AI engines. Google's note: structured data is "not required for generative AI search" but is recommended for overall SEO strategy. For implementation, use the schema skill.

Monthly manual check:

1. Pick your top 20 queries
2. Run each through ChatGPT, Perplexity, and Google
3. Record: Are you cited? Who is? What page?
4. Log in a spreadsheet, track month-over-month

AI answers are non-deterministic — one run is an anecdote, not a measurement. Run each query 3–5 times per platform and track the mention rate with its sample size ("cited 3/5, n=5"), comparing rates over time rather than single runs. Full rigor checklist in references/format-volatility.md.

Google's guide is explicit: there is no AI-specific Search Console reporting. AI Overviews and AI Mode use core Search ranking, so the standard Search Console reports (Performance, Coverage, Core Web Vitals) are still what you measure with for Google. The third-party tools above are the only way to see cross-platform AI citation behavior.