AEO/GEO Intelligence

When to Use This Skill

Activate this module when the user's request involves any of the following:

• AI Visibility: Questions about how a brand, product, or person appears in AI-generated answers (ChatGPT, Perplexity, Google AI Mode, Google AI Overviews, Copilot, Gemini, Claude)
• Answer Engine Optimization (AEO): Optimizing content so it gets selected as a source for AI-generated answers
• Generative Engine Optimization (GEO): Structuring content and entities so generative AI platforms accurately represent a brand
• Citation Tracking: Monitoring which sources AI models cite when answering queries related to a brand or industry
• Entity Consistency: Ensuring brand information is uniform across all knowledge sources that AI models train on or retrieve from
• Knowledge Graph Optimization: Improving how a brand is represented in Google Knowledge Graph, Wikidata, and other structured knowledge bases
• Structured Data for AI: Implementing schema markup and structured data specifically to improve AI comprehension and citation likelihood

Trigger phrases: "AI visibility," "how does ChatGPT describe my brand," "Perplexity results," "AI Mode optimization," "AI Overview optimization," "answer engine," "generative engine," "LLM optimization," "AI citations," "entity consistency," "Knowledge Graph"

Google AI Mode (May 2026 — treat as a distinct surface): At Google I/O on 19 May 2026 AI Mode became the default search experience for opted-in users, crossed ~1B MAUs, and switched to Gemini 3.5 Flash as the base model. AI Mode is not the same as AI Overviews — it is a separate conversational tab with deeper reasoning, multi-turn follow-ups, and a citation pattern that frequently diverges from AI Overviews for the same query. Brands must audit AI Mode independently. Practical implication: an AEO program that only tests AI Overviews + ChatGPT + Perplexity now has a measurable blind spot.

Additional I/O 2026 announcements that change AEO scope (source: blog.google/products-and-platforms/products/search/search-io-2026 (https://blog.google/products-and-platforms/products/search/search-io-2026/)):

• AI Overview → AI Mode follow-up flow is live worldwide (desktop + mobile) — users can ask a follow-up directly from an AI Overview and flow into a conversational AI Mode session. AEO implication: the first impression in an AI Overview is now also a gateway to multi-turn citation. Optimize for being the foundational citation, not just the brief snippet.
• Personal Intelligence in AI Mode is expanding to ~200 countries and 98 languages, no subscription required, with Gmail / Photos / Calendar connections. AEO implication: AI answers are increasingly personalized — generic brand-search results will be reweighted against the user's own context. Brand schema completeness and entity consistency (NAP, services, hours) matter even more.
• AI Information Agents (user-created, monitoring blogs/news/social 24/7) launch for AI Pro & Ultra subscribers in summer 2026. AEO implication: brands that publish structured, dated updates on owned channels will be more legible to user-configured agents than those relying on third-party PR pickup.

Official Google guidance on AI search optimization (updated 15 May 2026 — Google AI Optimization Guide (https://developers.google.com/search/docs/fundamentals/ai-optimization-guide)):

• No llms.txt file is needed. Google's official position: "You don't need to create new machine readable files, AI text files, markup, or Markdown to appear in generative AI search." Do not waste time generating llms.txt for Google AI Features. (Other AI search engines may or may not consume it; current Anthropic / OpenAI / Perplexity public positions are also that they do not require it. Document any client pressure to ship llms.txt as a low-priority deliverable with no measurable upside.)
• No special AI-specific schema is needed. "Structured data isn't required for generative AI search, and there's no special schema.org markup you need to add." Schema continues to matter for classic SEO and rich results.
• Eligibility is standard Search. "To be eligible to be shown in generative AI features on Google Search, a page must be indexed and eligible to be shown in Google Search with a snippet, fulfilling the Search technical requirements."

Opt-out and AI training controls (Google AI Features doc (https://developers.google.com/search/docs/appearance/ai-features)):

• For AI Overviews and AI Mode (inside Google Search): use existing snippet directives — nosnippet, data-nosnippet, max-snippet, noindex. Robots.txt for Googlebot is the canonical control. There is no AI-specific robots/meta directive.
• For Google's other AI systems (Gemini app training, Vertex AI grounding outside Search): use the Google-Extended user agent in robots.txt. This is a distinct control from Googlebot.
• NEW (3 June 2026): Search Console now ships an opt-out toggle at the property level — flip it to exclude the site from grounding AI Overviews / AI Mode responses without editing robots.txt. See /digital-marketing-pro:gsc-ai-performance for the decision framework on when to use it.

EU AI Act Article 50 (applicable 2 August 2026) — for AI-generated marketing content surfaced in EU markets, see skills/context-engine/eu-code-of-practice.md for the voluntary Code of Practice (WG1 providers / WG2 deployers) and the C2PA c2pa.ai-disclosure assertion path. Compliance is plugin-level and applies to c2pa-metadata outputs.

Brand Context (Auto-Applied)

Before producing any marketing output from this module:

1. Check session context — The active brand summary was output at session start. Use the brand name, industry, voice settings, channels, goals, compliance, and competitors shown there.
2. If you need the full profile, read: ~/.claude-marketing/brands/{slug}/profile.json
3. Apply brand voice — Formality, energy, humor, authority levels must shape all content tone and word choices
4. Check compliance — Auto-apply rules for brand's target_markets and industry using skills/context-engine/compliance-rules.md
5. Reference industry benchmarks — Consult skills/context-engine/industry-profiles.md for the brand's industry
6. Use platform specs — Reference skills/context-engine/platform-specs.md for character limits and format requirements
7. Check campaign history — Run python "${CLAUDE_PLUGIN_ROOT}/scripts/campaign-tracker.py" --brand {slug} --action list-campaigns before planning new work
8. If no brand exists, say: "No brand profile found. Use /digital-marketing-pro:brand-setup to create one, or I can proceed with general best practices."
9. Check brand guidelines — If ~/.claude-marketing/brands/{slug}/guidelines/_manifest.json exists, load and enforce: restrictions.md for banned words, restricted claims, and mandatory disclaimers; channel-styles.md for channel-specific tone overrides (may differ from base voice); messaging.md for approved key messages, taglines, and positioning language; voice-and-tone.md for detailed voice rules beyond the 4 numeric scores. If producing content for a specific channel, channel style rules take precedence over base voice settings.

Do not ask the user for information that already exists in their brand profile.

Required Context

Before executing AEO/GEO work, gather:

1. Brand Identity: Official brand name, key products/services, unique value propositions, and brand positioning
2. Current AI Footprint: Ask the user if they have tested how AI platforms currently describe their brand (or offer to audit)
3. Target Queries: The questions and topics the brand wants to be cited for in AI-generated answers
4. Existing Content Assets: Website URL, blog, knowledge base, Wikipedia presence, schema markup status
5. Competitive Landscape: Key competitors who may already have strong AI visibility
6. Industry Vertical: Needed to assess YMYL (Your Money Your Life) sensitivity and trust signal requirements

If the user cannot provide all context, proceed with what is available and flag gaps as recommendations.

Minimum viable context: Brand name and website URL. Everything else can be inferred or discovered during the audit process.

Capabilities

• AI Visibility Audit: Systematic testing of how a brand appears across the 6 canonical surfaces — Google AI Mode, Google AI Overviews, ChatGPT, Perplexity, Gemini, and Copilot — for target queries (scored with the standard defined in /digital-marketing-pro:aeo-audit)
• Citation Optimization: Restructuring content to maximize the probability of being cited as a source in AI-generated responses
• Entity Consistency Audit: Cross-referencing brand information across Google Knowledge Graph, Wikidata, Wikipedia, Crunchbase, LinkedIn, and industry databases to identify inconsistencies
• LLM Content Strategy: Creating content specifically designed to be ingested and accurately represented by language models
• AI Answer Monitoring Framework: Setting up systematic tracking of AI mentions and citations over time
• Structured Data for AI Citation: Implementing Organization, Product, FAQ, HowTo, and other schema types that improve AI comprehension
• Knowledge Graph Optimization: Improving entity representation in structured knowledge bases
• Topical Authority Mapping: Identifying content gaps that prevent a brand from being recognized as an authority by AI models
• AI-First Content Formatting: Restructuring existing content with clear definitions, factual statements, and citation-worthy snippets
• Competitive AI Visibility Benchmarking: Comparing brand AI presence against competitors across platforms

Process

Primary Workflow: AI Visibility Audit & Optimization

1. Discovery & Baseline

   • Collect brand details, target queries (10-25 queries), and competitor list
   • Document current schema markup, Knowledge Graph presence, and Wikipedia/Wikidata status
   • Identify the business model to determine which AI platforms matter most
   • Catalog existing authoritative content assets (whitepapers, research, data, expert bios)
   • Assess YMYL classification — brands in health, finance, or legal face higher authority thresholds

2. AI Platform Testing

   • For each target query, document how the brand appears (or fails to appear) on:
     • Google AI Mode (default conversational surface, Gemini 3.5 Flash backbone — May 2026)
     • Google AI Overviews (classic SERP summary block)
     • ChatGPT (latest model, web-search mode on)
     • Perplexity
     • Gemini (gemini.google.com)
     • Microsoft Copilot
   • Score each result: Cited (direct mention with link), Referenced (mentioned without link), Absent, Misrepresented
   • Capture exact AI-generated text for each query as a baseline

3. Entity Consistency Check

   • Audit brand name, founding date, leadership, product descriptions, and key claims across all knowledge sources
   • Flag inconsistencies between sources (e.g., different founding years on Crunchbase vs. Wikipedia)
   • Prioritize fixes by source authority weight

4. Gap Analysis & Strategy

   • Identify patterns: Which query types yield citations? Which don't?
   • Map content gaps: What authoritative content is missing that AI models need?
   • Assess structured data gaps: What schema markup is missing or incorrect?
   • Benchmark against competitors who ARE getting cited

5. Optimization Execution Plan

   • Prioritized list of content to create or restructure
   • Schema markup implementation plan
   • Knowledge Graph correction/enhancement steps
   • Entity consistency fix checklist
   • Content formatting guidelines for AI-first optimization

6. Monitoring & Iteration

   • Define monitoring cadence (weekly for priority queries, monthly for full audit)
   • Set up tracking framework to detect citation changes
   • Establish KPIs: citation rate, accuracy score, query coverage percentage
   • Track competitor citation changes as part of ongoing monitoring
   • Re-test after major content updates or schema implementations to measure impact
   • Log all AI platform model updates that may affect visibility (new model releases, retrieval changes)

Secondary Workflow: Citation-Optimized Content Creation

1. Identify a target query cluster where the brand should be cited but currently is not
2. Analyze what sources ARE being cited for those queries — study their content structure, authority signals, and formatting
3. Create or restructure content that surpasses cited sources in:
   • Factual accuracy and specificity (include precise data, dates, numbers)
   • Clear definitional statements (AI models favor content with unambiguous definitions)
   • Structured formatting (clear headings, bullet points, tables that AI can parse)
   • Source credibility signals (author credentials, citations to primary research, organizational authority)
4. Implement supporting schema markup (FAQ, HowTo, Article, Organization as appropriate)
5. Build inbound authority signals (internal links from high-authority pages, external citations)
6. Re-test AI platform responses 2-4 weeks after publication to measure citation pickup

Reference Files

• ai-visibility-audit.md — Step-by-step audit methodology, scoring rubric, and platform-specific testing protocols
• citation-optimization.md — Content restructuring techniques, citation-worthy formatting patterns, and source authority building
• entity-consistency.md — Cross-platform entity audit checklist, Knowledge Graph optimization, Wikidata editing guidelines
• llm-content-strategy.md — AI-first content creation framework, topical authority mapping, and structured data implementation guide

Output Formats

Edge Cases

Brand with Negative AI Perception

• Situation: AI platforms are generating inaccurate or negative information about the brand
• Approach: Prioritize entity consistency fixes and authoritative source correction before any content optimization. Create factual correction content on high-authority owned properties. Do NOT attempt to manipulate AI outputs directly — focus on fixing the underlying source material. Flag potential reputation management needs to the user.

New Brand with Zero AI Visibility

• Situation: Brand does not appear in any AI-generated answers
• Approach: Start with foundation-building — create a Wikipedia-worthy web presence (not necessarily Wikipedia itself), establish Wikidata entry, implement comprehensive schema markup, and build topical authority content. Set realistic timelines: AI model knowledge has lag times (weeks to months depending on platform).

Common-Word Brand Names

• Situation: Brand name is a common word (e.g., "Apple," "Slack," "Monday")
• Approach: Entity disambiguation is critical. Emphasize co-occurring terms, use full official names in structured data, ensure Knowledge Graph correctly disambiguates, and optimize content with entity-clarifying context. Always include industry/product qualifiers in target queries.

Multi-Brand Companies

• Situation: Parent company with multiple sub-brands needing separate AI identities
• Approach: Audit each brand entity separately. Ensure clear parent-child relationships in structured data. Avoid cannibalization where sub-brands compete with each other in AI answers. Create distinct topical authority for each brand.

Regional AI Engines (Baidu, Yandex)

• Situation: User needs visibility on non-Western AI platforms
• Approach: Acknowledge that optimization strategies differ significantly for Baidu (China) and Yandex (Russia). These require localized content, platform-specific structured data standards, and different knowledge bases. Recommend specialized regional expertise if the request goes deep. Provide general framework but flag limitations in specific platform knowledge.

YMYL Brands (Health, Finance, Legal)

• Situation: Brands in Your Money Your Life categories face elevated trust requirements from AI platforms
• Approach: AI platforms apply stricter source quality thresholds for YMYL topics. Prioritize: (1) Expert authorship with verifiable credentials on all content. (2) Citations to primary research, government sources, and peer-reviewed studies. (3) Medical/legal/financial review disclosures. (4) Comprehensive E-E-A-T signals (link to Digital PR module for authority building). (5) Schema markup that explicitly declares author qualifications and organizational credentials. Test AI outputs carefully for accuracy — misrepresentation in YMYL categories carries higher reputational risk.

Rapidly Evolving AI Landscape

• Situation: AI platforms frequently update their models, retrieval methods, and citation behavior
• Approach: Treat all AEO/GEO strategies as living processes, not one-time optimizations. Build monitoring into every engagement. When a major platform update occurs (new model release, retrieval system change, AI Overview format change), re-run the visibility audit for priority queries. Document observed behavior changes and update the workflow accordingly. Maintain a changelog of platform updates and their observed impact on brand visibility.

Tips & caveats

• Google's official position (15 May 2026): no llms.txt, no AI-specific schema, no separate AI eligibility gate. Don't manufacture work around fictional ranking factors — schema + entity consistency + citation-worthy formatting are what works.
• AI Mode citation patterns frequently differ from AI Overviews on the same query (internal observation, 05/2026 — the "40-60%" figure is a rough estimate, re-verify against your own probe set). Audit and optimise for both, treating them as distinct surfaces.
• Entity consistency across Knowledge Graph, Wikidata, Wikipedia, LinkedIn, Crunchbase is the single highest-leverage AEO investment — more impactful than schema tweaks.
• AI citations are stickier than blue-link rankings but slower to win. Expect 3-6 months of consistent work before measurable shift.
• Don't try to "trick" AI into citing you with stuffed content or fake authority signals. AI platforms detect and demote this faster than traditional search.
• Google-Extended (robots.txt) opts out of Google's other AI systems (Gemini training, Vertex grounding) — distinct from the in-Search-Console toggle for AI Overviews/AI Mode (rolled out 3 Jun 2026 via /digital-marketing-pro:gsc-ai-performance).
• EU markets require Article 50 disclosure on AI-generated content (applicable 2 Aug 2026) — see skills/context-engine/eu-code-of-practice.md.

Related Skills

• Content Engine — For creating and optimizing the actual content that drives AI citations
• Analytics & Insights — For measuring AI visibility performance and tracking citation changes over time
• Digital PR & Authority — For building the E-E-A-T signals and earned media that strengthen AI trust in a brand
• Audience Intelligence — For understanding which queries your target audience is asking AI platforms

---

Supporting file: ai-visibility-audit.md

AI Visibility Audit — Methodology & Scoring

Overview

A systematic process for auditing how a brand appears across AI-generated answers on 6 platforms: ChatGPT, Perplexity, Google AI Mode, Google AI Overviews, Gemini, and Microsoft Copilot.

Why 6, not 5 (changed May 2026): Google split its AI search surfaces. AI Mode (Gemini 3.5 Flash, conversational, default for opted-in users since I/O 2026) and AI Overviews (classic SERP summary block) now select different citations for the same query 40–60% of the time. Audit both independently — a brand cited in AI Overviews is not necessarily cited in AI Mode.

---

Step 1: Query Selection

Select 15-25 queries across four categories:

Query Categories

Query Selection Rules

1. Include 4-6 queries per category
2. Mix head terms (high volume) with long-tail (specific intent)
3. Include at least 3 queries where the brand SHOULD be cited but may not be
4. Include branded queries (brand name) and non-branded (category/problem)
5. Prioritize queries that match the brand's target customer JTBD

---

Step 2: Platform-by-Platform Testing

Testing Protocol

For each query, test on all 6 platforms and record:

Platform-Specific Notes

• ChatGPT: Test with latest model. Note that responses vary by session — test 2-3 times. Web-search mode on.
• Perplexity: Check both the answer AND the cited sources list.
• Google AI Mode (May 2026, default): Test from the AI Mode tab (or directly via the conversational entry point that appears for opted-in users). Gemini 3.5 Flash backbone. Capture the full conversational thread including any follow-up clarifiers — citations evolve across turns. AI Mode often selects different sources than AI Overviews for the same query.
• Google AI Overviews: Not all queries trigger AI Overviews — document which do. This is the SERP summary block, separate from AI Mode.
• Gemini: Test via gemini.google.com, note any "I don't have enough info" responses.
• Copilot: Test in Bing chat mode for web-grounded responses.

---

Step 3: Scoring Rubric

Per-Query Scoring

Aggregate Scoring

AI Visibility Score = (Sum of per-query scores across all platforms) / (Max possible score) × 100

• Max possible per query: 5 points × 6 platforms = 30
• Max possible total: 30 × number of queries

When comparing scores against pre–May 2026 baselines (which used 5 platforms / max 25 per query), normalise by scaling the older baseline ×1.2 — or rerun the historical query set in AI Mode and reuse the original baseline. Don't compare 5-platform totals to 6-platform totals directly.

Score Interpretation

---

Step 4: Competitive Benchmarking

Run the same query set for 3-5 key competitors. Compare:

1. Visibility Score: Side-by-side total scores
2. Citation Rate: % of queries where each brand is cited (score ≥ 3)
3. First-Mention Rate: % of queries where brand appears first
4. Platform Strength: Which platforms favor which brands
5. Query Category Gaps: Where competitors win vs where you win

---

Step 5: Gap Analysis

Identify patterns:

• Query gaps: Which query categories have the lowest scores?
• Platform gaps: Which AI platforms under-represent the brand?
• Competitor advantages: What are cited competitors doing differently?
• Content gaps: What authoritative content is missing from the brand's ecosystem?
• Structured data gaps: What schema markup is missing?
• Entity gaps: Are there knowledge graph or Wikipedia/Wikidata issues?

---

Step 6: Monitoring Cadence

Triggers for Immediate Re-Audit

• New AI model release (GPT update, Gemini update, etc.)
• Major content publish or site restructure
• Significant schema markup implementation
• Competitor AI visibility change detected
• Brand entity correction on Wikipedia/Wikidata

---

Supporting file: citation-optimization.md

Citation Optimization — Content Restructuring for AI Citation

Overview

Techniques for restructuring content so AI platforms are more likely to cite it as a source in generated answers.

Google AI Mode note (mid-2026): AI Mode (the conversational search tab, default for opted-in users since I/O on 19 May 2026, Gemini 3.5 Flash backbone) is a distinct surface from AI Overviews and frequently selects different citations for the same query. Its multi-turn follow-up flow rewards content that can be drilled into, not just skimmed for a one-line snippet: put a clean definitional answer up top (wins the first citation) and layer genuine "why / how / compared-to" depth beneath it (wins the follow-up citations). Optimize for both AI Mode and AI Overviews; never treat them as one surface.

---

Citation-Worthy Content Patterns

1. Definitive Statements

AI models favor content that makes clear, unambiguous claims backed by evidence.

Pattern: "[Subject] is [definitive claim]. According to [source], [supporting data]."

Example: "Customer acquisition cost (CAC) for B2B SaaS companies averages $205 for organic channels and $341 for paid channels. According to a 2024 FirstPageSage study, this represents a 15% increase from 2023."

2. Data-First Paragraphs

Lead paragraphs with specific numbers, dates, or statistics.

Weak: "Email marketing is very effective for businesses." Strong: "Email marketing delivers an average ROI of $36 for every $1 spent (Litmus 2023), making it the highest-ROI digital marketing channel across industries."

3. Structured Definitions

AI models frequently pull definitional content for "what is" queries.

Format:

[Term] is [concise definition in one sentence]. It [expanded explanation].
Key characteristics include: [list of 3-5 defining features].

4. Comparison Tables

Structured comparisons are high-value citation targets.

| Feature | Option A | Option B | Option C |
|---------|----------|----------|----------|
| Price   | $X/mo    | $Y/mo    | $Z/mo    |
| Best for| [use case]| [use case]| [use case]|

5. Step-by-Step Processes

Numbered processes are frequently cited for "how to" queries.

---

Content Formatting Rules for LLM Ingestion

1. Concise paragraphs: 2-4 sentences max. AI models parse shorter paragraphs more reliably
2. Entity-rich text: Include full proper names, specific numbers, and verifiable facts
3. Clear heading hierarchy: H1 → H2 → H3 with descriptive headings (not clever/vague)
4. Front-load key info: Put the most citation-worthy statement in the first sentence of each section
5. Avoid hedging language: "Arguably" and "some experts say" reduce citation likelihood
6. Include recency signals: Dates, "as of [year]," version numbers
7. Use lists and tables: Structured data formats are easier for AI to parse and cite
8. Attribute claims: Link to primary research, not secondary sources

---

Schema Markup for AI Citation

FAQ Schema (JSON-LD)

{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [{
    "@type": "Question",
    "name": "What is [topic]?",
    "acceptedAnswer": {
      "@type": "Answer",
      "text": "Concise, definitive answer here."
    }
  }]
}

HowTo Schema

{
  "@context": "https://schema.org",
  "@type": "HowTo",
  "name": "How to [achieve outcome]",
  "step": [{
    "@type": "HowToStep",
    "name": "Step name",
    "text": "Step description"
  }]
}

Organization Schema

{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "Brand Name",
  "description": "One-sentence brand description",
  "url": "https://brand.com",
  "foundingDate": "YYYY",
  "founder": {"@type": "Person", "name": "Founder Name"},
  "sameAs": ["LinkedIn URL", "Twitter URL", "Crunchbase URL"]
}

Article Schema

{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "Article title",
  "author": {
    "@type": "Person",
    "name": "Author Name",
    "url": "Author profile URL",
    "jobTitle": "Title"
  },
  "datePublished": "YYYY-MM-DD",
  "dateModified": "YYYY-MM-DD",
  "publisher": {"@type": "Organization", "name": "Brand"}
}

---

Source Authority Building

Tier 1: Owned Authority

• Publish original research with proprietary data
• Create definitive guides (3,000+ words with unique frameworks)
• Maintain expert author profiles with verifiable credentials
• Keep content updated (dated content loses citation preference)

Tier 2: Earned Authority

• Get cited in industry publications
• Earn Wikipedia references (do NOT edit Wikipedia directly)
• Build Wikidata entity with proper sourcing
• Secure .edu or .gov backlinks

Tier 3: Structured Authority

• Implement comprehensive schema markup
• Ensure Knowledge Graph accuracy
• Maintain consistent NAP (Name, Address, Phone) across directories
• Cross-reference brand info across Crunchbase, LinkedIn, About page

---

Citation Testing Process

1. Publish optimized content with all formatting and schema
2. Wait 2-4 weeks for indexing and AI model retrieval refresh
3. Test target queries across all 5 AI platforms
4. Document results with exact AI text and source attribution
5. Compare to baseline (pre-optimization audit)
6. Iterate: If not cited, analyze what the cited source has that yours doesn't
7. Re-test after each significant update

---

Supporting file: entity-consistency.md

Entity Consistency — Cross-Platform Audit & Optimization

Overview

Ensuring brand information is consistent across all knowledge sources that AI models reference. Inconsistencies confuse AI platforms and reduce citation confidence.

Why this matters more for Google AI Mode (mid-2026): AI Mode — Google's conversational search surface, default for opted-in users since I/O on 19 May 2026 — adds "Personal Intelligence" that reweights answers against each user's own context (Gmail / Calendar / Photos connections). That personalization runs on top of the same entity graph, so gaps or contradictions in your NAP, services, hours, founding date, and leadership degrade AI Mode answers even harder than they degrade AI Overviews. Entity completeness and consistency is the highest-leverage AI Mode investment. AI Mode is a distinct surface from AI Overviews — verify entity representation in both.

---

Entity Audit Checklist

Check the following data points across all platforms:

Mark each cell: Correct / Incorrect / Missing / Outdated

---

Common Inconsistency Types

---

Knowledge Graph Optimization

Google Knowledge Graph

1. Claim your Knowledge Panel via Google Search Console entity verification
2. Suggest edits for incorrect information using the "Claim this knowledge panel" flow
3. Strengthen signals: Ensure your website, Wikipedia, Wikidata, and authority sources all agree
4. Use Organization schema on your homepage with sameAs links to all official profiles
5. Monitor changes: Set up alerts for Knowledge Panel modifications

Knowledge Graph Triggers

Google builds Knowledge Graph entries from:

• Wikipedia/Wikidata (highest weight)
• Schema markup on official website
• Crunchbase, LinkedIn, and other authority databases
• News mentions and citations
• Google My Business (for local entities)

---

Wikidata Editing Guidelines

Notability Requirements

Wikidata is more permissive than Wikipedia. An entity can have a Wikidata entry if:

• It has been covered in at least one independent reliable source
• It has a unique identifier in at least one external database
• It represents a notable organization, person, product, or concept

Creating a Wikidata Entry

1. Go to wikidata.org → Create new item
2. Set label (brand name), description (concise identifier), and aliases
3. Add properties:
   • P31 (instance of): Q4830453 (business enterprise) or appropriate type
   • P856 (official website)
   • P571 (inception date)
   • P112 (founded by)
   • P159 (headquarters location)
   • P452 (industry)
   • Add social media identifiers (P2002 Twitter, P4003 Facebook, P4264 LinkedIn)
4. Add references for every claim (link to source)
5. Add sameAs equivalent properties linking to other databases

Editing Rules

• Every claim MUST have a reference (URL to a reliable source)
• Do NOT add promotional content or subjective claims
• Use the talk page for disputed edits
• Disclose conflicts of interest (if editing your own company's entry)
• Focus on verifiable, factual properties only

---

Brand Name Disambiguation

For brands with common-word names:

1. Use full official name in all structured data: "Monday.com" not "Monday"
2. Add entity type qualifiers: "Slack (software)" pattern in descriptions
3. Build co-occurrence signals: Always mention brand + product category together
4. Wikidata disambiguation: Add P1889 (different from) property linking to other entities with similar names
5. Schema markup: Use disambiguatingDescription property
6. Content optimization: Include "[Brand] [category] [description]" in title tags and headers

---

Entity Monitoring Schedule

---

Supporting file: llm-content-strategy.md

LLM Content Strategy — AI-First Content Framework

Overview

A content strategy specifically designed to build brand authority in AI-generated answers. Focuses on creating content that AI models trust, cite, and accurately represent.

Design for Google AI Mode, not just AI Overviews (mid-2026): AI Mode — the conversational search surface that became the default for opted-in users at I/O (19 May 2026) on a Gemini 3.5 Flash backbone — is a distinct surface that cites differently than AI Overviews and supports multi-turn follow-ups. Structure each authority piece so it answers the headline query cleanly and holds up to the obvious follow-ups (definitions, comparisons, edge cases, "how do I actually do this"). Publishing structured, dated updates on owned channels also makes a brand more legible to the user-configured AI Information Agents launching for AI Pro / Ultra in summer 2026. Audit AI Mode and AI Overviews separately and reconcile actual impressions via /digital-marketing-pro:gsc-ai-performance.

---

Topical Authority Mapping

Methodology

1. Define core topics (3-5 primary topics the brand should own in AI answers)
2. Map subtopics for each core topic (10-20 subtopics per core)
3. Audit existing coverage (which subtopics have quality content vs gaps)
4. Check AI citation status (which subtopics generate AI answers citing brand content)
5. Prioritize gaps (high-value subtopics where brand has no coverage)

Authority Score Per Topic

For each core topic, score:

Authority Score = Weighted average × 10 (0-100 scale)

---

Content Gap Analysis for AI Visibility

Gap Types

1. Coverage gap: Topic exists but brand has no content on it
2. Depth gap: Content exists but is too shallow vs what AI cites
3. Freshness gap: Content is outdated (AI prefers recent, dated sources)
4. Authority gap: Content lacks expert signals (no author credentials, no data)
5. Structure gap: Content isn't formatted for AI parsing (no schema, poor headings)
6. Entity gap: Content doesn't connect to the brand's knowledge graph entity

Prioritization Matrix

Score each gap: Impact (how much AI visibility would improve) × Effort (how hard to fix)

---

LLM-Optimized Content Templates

Template 1: Definitional Content

Best for: "What is [X]?" queries

# What Is [Term]?

[Term] is [one-sentence definition]. [Expanded explanation in 2-3 sentences].

## Key Characteristics
- [Characteristic 1]: [Brief explanation]
- [Characteristic 2]: [Brief explanation]
- [Characteristic 3]: [Brief explanation]

## How [Term] Works
[Clear, step-by-step explanation]

## [Term] vs [Related Concept]
[Comparison table or brief differentiation]

## Examples of [Term]
[2-3 real-world examples with specifics]

Template 2: Comparative Content

Best for: "[A] vs [B]" queries

# [Option A] vs [Option B]: [Year] Comparison

## Quick Comparison

| Feature | Option A | Option B |
|---------|----------|----------|
| Best for | [use case] | [use case] |
| Price | [range] | [range] |
| Key strength | [strength] | [strength] |

## Detailed Comparison
[Section-by-section analysis]

## Verdict
[Clear recommendation with reasoning]

Template 3: Procedural Content

Best for: "How to [do X]" queries

# How to [Achieve Outcome] (Step-by-Step Guide)

[One paragraph summary of what this guide covers and who it's for]

## Prerequisites
- [Prerequisite 1]
- [Prerequisite 2]

## Step 1: [Action]
[2-3 paragraphs with specific instructions]

## Step 2: [Action]
[Continue for each step]

## Common Mistakes to Avoid
- [Mistake 1]: [Why it's a problem and what to do instead]

Template 4: Statistical/Data Content

Best for: "[topic] statistics" and benchmark queries

# [Topic] Statistics and Benchmarks ([Year])

## Key Statistics
- **[Stat 1]**: [Number] ([Source, Year])
- **[Stat 2]**: [Number] ([Source, Year])

## Benchmarks by [Category]

| Category | Metric 1 | Metric 2 | Source |
|----------|----------|----------|--------|
| [Cat A]  | [value]  | [value]  | [source] |

---

90-Day AI Authority Building Plan

Days 1-30: Foundation

• Complete AI visibility audit (baseline scores)
• Fix all entity consistency issues
• Implement Organization, Article, FAQ schema on key pages
• Update or create Wikidata entry
• Identify top 5 content gaps for priority queries
• Publish 2-3 definitional/comparative pieces on gap topics
• Ensure all content has expert author attribution

Days 31-60: Building

• Publish 4-6 pillar content pieces on core topics
• Create original research or data content (survey, analysis, benchmark)
• Implement HowTo schema on procedural content
• Build internal linking between topic cluster pages
• Secure 2-3 external citations (guest posts, press mentions)
• Re-test priority queries on all AI platforms (measure improvement)

Days 61-90: Scaling

• Fill remaining content gaps across all core topics
• Publish thought leadership content (unique frameworks, predictions)
• Expand schema markup to all eligible pages
• Build FAQ content targeting AI platform questions
• Conduct second full AI visibility audit
• Document score changes and identify next priority actions
• Set up ongoing monthly monitoring cadence