Joint SEO + AI Visibility Plan: “From Crawl to Citation”

Phase 1. Research & Discovery

Lead: SEO
Support: AI Visibility

SEO Responsibilities:

  • Conduct keyword and search intent research to understand what users type into Google and Bing.

  • Map those keywords into topic clusters around business objectives.

  • Analyze existing rankings, backlinks, and competitive SERP landscape.

AI Visibility Responsibilities:

  • Conduct prompt-based research to identify how users phrase similar queries in LLMs (ChatGPT, Perplexity, Gemini).

  • Compare LLM responses to search engine results to find citation gaps — where the brand is visible in search but invisible in models.

  • Identify entities and topics that LLMs associate with the brand (or competitors).

Intersection Point:

➡ Merge SEO keyword data with AI Visibility prompt data to create a Keyword–Prompt Map, showing how search intent translates into model queries.
This forms the foundation for both on-page and structured content planning.

Phase 2. Content Planning & Architecture

Lead: Joint
Support: Equal collaboration

SEO Responsibilities:

  • Build a content architecture (URL structure, internal links, pillar pages).

  • Plan on-page optimizations: titles, meta descriptions, headings, and internal anchor text.

  • Identify cornerstone content that attracts backlinks and satisfies search intent.

AI Visibility Responsibilities:

  • Define structured content types: FAQs, Wikis, product schemas, and knowledge hubs.

  • Align content topics with LLM-friendly structures — question/answer formats, glossary entries, or structured comparisons.

  • Create entity maps linking people, places, products, and organizations.

Intersection Point:

➡ Integrate SEO keyword targets with AI schema planning.
For example, every blog or pillar page should include:

  • An FAQ schema aligned with prompt queries.

  • A Product or HowTo schema where relevant.

  • Links to authoritative sources (Wikipedia, official datasets).

This makes each page both rankable (for Google) and citable (for LLMs).

Phase 3. Content Creation & Optimization

Lead: SEO (content production)
Support: AI Visibility (structuring and validation)

SEO Responsibilities:

  • Write engaging, high-quality content that meets E-E-A-T standards (Experience, Expertise, Authoritativeness, Trustworthiness).

  • Optimize for keywords, readability, and conversion intent.

AI Visibility Responsibilities:

  • Add schema markup and structured Q&A data.

  • Insert internal links that form a semantic graph — enabling LLMs to infer relationships.

  • Ensure the content uses consistent entity names and canonical identifiers (e.g., linking “Mars Inc.” to Wikidata Q208702).

Intersection Point:

➡ SEO content writers deliver narrative flow; AI Visibility ensures machine comprehension.
Together, they produce pages that humans and LLMs can both read fluently.

Phase 4. Technical Infrastructure

Lead: SEO
Support: AI Visibility

SEO Responsibilities:

  • Optimize crawlability and performance: robots.txt, sitemap.xml, site speed, and Core Web Vitals.

  • Ensure canonical tags, hreflang, and noindex directives are correct.

AI Visibility Responsibilities:

  • Verify that schema is valid, accessible, and consistent across all pages.

  • Test data retrievability using LLM prompt evaluations — e.g., asking ChatGPT to “summarize” or “explain” the brand’s products to verify inclusion.

  • Prepare LLM.txt or AI index feeds (where applicable) to signal preferred crawl paths.

Intersection Point:

➡ Every SEO audit includes an AI-Visibility Readability Check — testing how both search crawlers and AI models interpret your data.

Phase 5. Off-Page Strategy

Lead: SEO (link building)
Support: AI Visibility (citation building)

SEO Responsibilities:

  • Build backlinks from high-authority domains.

  • Secure mentions in media, guest posts, and directories.

AI Visibility Responsibilities:

  • Identify and strengthen LLM data sources that shape brand perception:

    • Wikipedia / Wikidata entries.

    • Reddit and Quora discussions.

    • Product listings and public datasets.

  • Encourage content that LLMs can reference directly.

Intersection Point:

➡ A backlink that improves SEO can also serve as an AI citation anchor if the referring domain is used in model training (e.g., Wikipedia, news sites, .edu sources).

Phase 6. Measurement & Analytics

Lead: Joint

SEO Metrics:

  • Organic traffic

  • Impressions and rankings

  • Click-through rate (CTR)

  • Backlink growth

AI Visibility Metrics:

  • Mentions or citations in LLMs (ChatGPT, Perplexity, Gemini)

  • Share of voice in generative answers vs. competitors

  • Structured data validation and schema coverage

  • LLM response accuracy (brand sentiment, factual correctness)

Intersection Point:

➡ Combine both into a unified “Discoverability Dashboard”, showing:

  • Traditional SEO KPIs (traffic, ranking)

  • AI Visibility KPIs (LLM mentions, citation authority, structured completeness)

This holistic view shows how well your brand is represented across both search engines and AI systems.

Phase 7. Continuous Optimization

Lead: AI Visibility (insight-driven)
Support: SEO (execution-driven)

SEO Focus:

  • Refresh outdated content and maintain site health.

  • Adjust keyword targets as search trends evolve.

AI Visibility Focus:

  • Monitor how models’ answers evolve over time.

  • Identify new opportunities for structured data enrichment.

  • Ensure factual consistency across models through citations and canonical data sources.

Intersection Point:

➡ Insights from LLM responses feed back into SEO strategy — if a model misrepresents the brand, SEO can adjust content architecture or FAQs to correct it at source.

The Takeaway

SEO gets you seen.
AI Visibility gets you cited.

The future of digital discovery belongs to brands that do both — treating search engines and large language models not as separate worlds, but as a single, interconnected visibility ecosystem.

SEO, AI VisibilityFrancesca Tabor