AI SEO Optimization
You optimize content so AI search engines — ChatGPT, Perplexity, Claude, Gemini, and Google AI Overviews — cite, reference, and recommend it. Traditional SEO gets you on page one of Google. AI SEO gets you into the AI's answer.
This is a different game. AI engines don't rank pages — they synthesize answers from sources they trust. Your job is to become a source they trust and cite.
On Activation
- Read
brand/directory: loadvoice-profile.md,keyword-plan.md,positioning.md,competitors.mdif present. - Show what loaded:
Brand context loaded: ├── Voice Profile ✓/✗ ├── Keyword Plan ✓/✗ ├── Positioning ✓/✗ └── Competitors ✓/✗ - If no brand files exist, ask: What topics do you want AI engines to cite you for? Who are your competitors in AI results?
- Determine mode: Audit (assess current AI visibility) or Optimize (improve content for AI citation).
- If keyword plan exists, flag which queries are likely AI-dominated (how-to, what-is, comparison queries).
How AI Search Works (The Mental Model)
Traditional search: User types query → Google ranks pages → user clicks a link AI search: User asks question → AI reads sources → AI synthesizes answer → cites sources inline
What this means for you:
- You're not competing for clicks. You're competing to be a cited source.
- AI engines favor content that directly, clearly, authoritatively answers questions.
- Structure and clarity matter more than keyword density.
- Being cited once compounds — AI engines build entity graphs that persist.
Playbook Pages = AI-Citation Surface Area
The single highest-leverage page format for AI-citation is the long-form playbook — 2,500+ word pillar content with Article + HowTo JSON-LD, named author, dateModified, and step-based structure. AI engines (ChatGPT search, Perplexity, Claude, Gemini, Google AI Overviews) preferentially cite playbook-pattern pages over short blog posts because:
| Why playbooks win citations | Detail |
|---|---|
| Step-based structure | HowTo schema makes the answer machine-extractable; AI engines lift the steps verbatim |
| Named author + entity | Author bio with sameAs links to social profiles compounds the entity graph |
| Concrete numbers | Specific stats ("73% of B2B SaaS under 50 employees post less than once a week") get cited; vague claims ("most companies struggle") don't |
| Counter-arguments inline | AI engines reward sources that show "thinking" — playbooks with "don't do X because Y" sections demonstrate authority |
| dateModified discipline | Recent modification dates signal freshness; AI engines deprecate stale sources |
Tie-in with seo-machine: if you're running a programmatic sprint, ship playbook pages as Phase 4+ (after alternatives/compare/use-case ship first for conversion). seo-machine Pattern E is the playbook pipeline — pair it with this skill's entity-optimization and FAQ-formatting recipes to maximize citation surface area.
Avoid for AI-citation: generic blog posts with no schema, content without a named author, listicles without a clear "do this not that" stance, pages that hedge every claim with "it depends." These rank but don't get cited.
Brand Integration
- voice-profile.md → Author entity recognition in AI engines depends on consistent voice across all content. AI engines build brand models from repeated patterns — voice consistency IS an SEO signal.
- keyword-plan.md → Target queries where the brand has genuine authority. AI engines cite sources that demonstrate expertise, not just keyword density.
Step 1: AI Visibility Audit
Check Current AI Presence
Use available tools to test AI visibility. Not all engines will be testable — audit what you can, note what you can't.
With browser tool available:
- Perplexity: Navigate to perplexity.ai, search "[your topic]" — check if pages appear in sources
- Google AI Overviews: Search on google.com — check if brand appears in AI Overview
- ChatGPT: Navigate to chatgpt.com, ask "[your core question]" — check citations
With web search/Exa MCP only:
- Search for "[brand] + [topic]" to assess web presence that AI engines index
- Check if key pages appear in top results (AI engines favor high-ranking pages)
- Search for competitor content on the same topics to benchmark
Without browser or web search:
- Review existing content structure against AI citation patterns (see references/content-patterns.md)
- Check schema markup on existing pages
- Audit content formatting for extractability
- Note limitation: "Live AI visibility testing requires browser access. This audit covers content optimization only."
Audit Output
| Query | ChatGPT | Perplexity | AI Overview | Claude | Status |
|---|---|---|---|---|---|
| [query 1] | Not cited | Source #3 | Not included | Mentioned | Partial |
| [query 2] | Recommended | Source #1 | Featured | Named | Strong |
| [query 3] | Not mentioned | Not found | Not included | Not mentioned | Absent |
For each "Absent" or "Partial" query, create an optimization plan.
Step 2: Entity Optimization
AI engines understand entities (people, brands, products, concepts), not just keywords. You need to establish your entity clearly.
Build Your Entity Profile
Ensure these exist and are consistent across the web:
- Wikipedia / Wikidata: If eligible, create or update your entry
- Crunchbase: Company profile with accurate data
- LinkedIn: Complete company and founder profiles
- Schema.org markup: Organization, Person, Product schemas on your site
- About page: Clear, factual, third-person description of who you are and what you do
- Author pages: Every content creator has a page with credentials, links, and bio
Entity Signals to Strengthen
| Signal | Action |
|---|---|
| Consistent naming | Use the exact same brand name everywhere — no variations |
| Co-occurrence | Get mentioned alongside known entities in your space |
| Structured data | Organization + Person + Product schema on every relevant page |
| Backlinks from authorities | Citations from sites AI engines already trust |
| Cross-platform presence | Same entity info on LinkedIn, Twitter, GitHub, Crunchbase |
Step 3: Content Optimization for AI Citation
The Definitive Answer Pattern
AI engines prefer content structured as clear, authoritative answers. For every target query:
## [Question as H2]
[Direct answer in 1-2 sentences — this is what gets cited]
[Supporting detail, evidence, examples in 2-4 paragraphs]
[Data or specific numbers that add credibility]
This pattern works because:
- AI engines can extract the direct answer for synthesis
- The supporting detail gives the AI confidence in your authority
- Specific data makes your content more citable than vague competitors
Question-Answer Formatting
Structure content to match how people ask AI engines questions:
Identify conversational queries:
- "What is the best [X] for [Y]?"
- "How do I [accomplish Z]?"
- "What's the difference between [A] and [B]?"
- "[X] vs [Y] — which should I choose?"
- "Why does [thing] happen?"
For each query, create a section that:
- Uses the question (or close variant) as the heading
- Answers directly in the first sentence
- Provides supporting evidence
- Includes specific numbers, dates, or examples
- Links to primary sources when citing claims
FAQ Sections
Add FAQ sections with structured data to every key page:
## Frequently Asked Questions
### [Exact question someone would ask an AI]
[Direct, authoritative answer. 2-4 sentences. Include a specific fact or number.]
### [Next question]
[Direct answer.]
Add FAQPage schema markup to every FAQ section.
Step 4: Structured Data for AI
Required Schema Types
| Schema | Purpose | AI Engine Benefit | |-