kai-topical-map

name

kai-topical-map

description

Build an AEO-first topical map optimized for AI search citation — entity clusters, query fan-out coverage, information gain scoring, and multi-platform distribution. Produces entity map, content node architecture, schema blueprint, and 90-day publishing calendar. Use when "topical map", "content architecture", "site structure", "topic clusters", "pillar content plan", "AEO map", "AI search architecture", "entity map", "what content should we build", or any request to plan a site's topical structure for AI search visibility.

Build a topical map optimized for AI search engines and traditional search together. Every decision is driven by retrievability, entity clarity, source quality, and measurable user demand, not by promises that a specific AI engine will cite a page.

Why This Matters

AI search visibility is sampled, volatile, and engine-specific. Treat any traffic, citation, or conversion benchmark as context until it is verified for the client with a source, retrieval date, evidence tier, and confidence label.

This skill builds the map that can be measured: pages to create, entities to clarify, passages to make retrievable, sources to cite, and follow-up checks to run.

---

Phase 0: Load Product Context

Check if MARKETING.md exists in the project root (same directory as CLAUDE.md, README.md, package.json).

If it exists: Read it — skip product discovery questions. It has the product name, ICP, value prop, monetization, brand voice, current channels, and competitive landscape.

If it does NOT exist: Auto-explore the codebase to create it in the project root (next to CLAUDE.md). Do NOT ask the user what the product is. Read CLAUDE.md, README.md, PROJECT.md, package.json, landing pages, and any project files. Search for email/ad/analytics config. Then create MARKETING.md using the template from /kai-email-system. Present draft to user for confirmation.

---

Phase 1: Topic Space Discovery

Load before starting:

• knowledge/frameworks/aeo-ai-search/aeo-ai-search-playbook-2026.md

Read from MARKETING.md. Only ask about things not covered there:

1. Brand/product — what entity should AI engines associate with this topic space?
2. Topic space — the broad domain (e.g., "AI receptionist software", "personal injury law", "B2B cold email")
3. Existing content inventory — blog post URLs, podcast episode titles, landing pages, guides already published
4. Competitor URLs — 2-3 sites currently winning AI citations in this topic space
5. Target queries — what questions should AI answer with YOUR brand? (e.g., "What's the best AI phone answering service?")
6. Current AI presence — sample 3-5 category queries in ChatGPT, Perplexity, Bing/Copilot, and Google AI surfaces where available. Record prompt, location, date, engine, account state, citations, mentions, and missing-data caveats.

Output: workspace/topical-map/_discovery.md

Baseline AI Presence Scorecard

Query	ChatGPT	Perplexity	Google AI	Brand Mentioned?	Who IS Mentioned?
[category query 1]				Y/N	[competitors]
[category query 2]				Y/N	[competitors]
...

This scorecard becomes the "before" measurement. Run it again at 30/60/90 days to track progress.

---

Phase 2: Entity Map

Load before starting:

• knowledge/frameworks/aeo-ai-search/entity-seo-knowledge-graph-deep-dive.md

Map the Entity Landscape

Identify every entity the brand needs to establish, strengthen, or associate with in the Knowledge Graph. Organize into three tiers:

Tier 1 — Own: Entities the brand must be the canonical authority for. These get Entity Home pages.

• Brand name, product name, founder/CEO, proprietary methodology or framework names
• Each Tier 1 entity needs: Entity Home URL, Wikidata QID (or submission plan), Schema.org markup

Tier 2 — Associate: Entities the brand should appear in context with. These become content cluster topics.

• Industry terms, use cases, methodologies, problem categories, competitor categories
• Appearing alongside these entities builds "context vectors" (Bill Slawski's patent analysis) that signal expertise

Tier 3 — Reference: Authoritative entities to cite for credibility amplification.

• Research institutions, industry standards, regulatory bodies, recognized experts
• Cite these entities to improve provenance and passage usefulness. Do not promise a fixed citation or visibility lift from citations.

Entity Map Output

| Entity | Type | Current KG Status | Tier | Entity Home URL | Wikidata QID | Schema Types | Action |
|--------|------|-------------------|------|-----------------|--------------|--------------|--------|

Schema.org Blueprint

Generate a @graph structure for the site showing:

• Organization or Person as the root entity with sameAs links (Wikidata, LinkedIn, Crunchbase, social profiles)
• knowsAbout properties listing Tier 2 entities
• mentions and about properties connecting content pages to entities
• sameAs links for every entity that has a Wikidata or Wikipedia entry

Evidence requirement: Treat Knowledge Graph, Wikidata, and vector-search claims as source-dependent. Add source URL, retrieval date, evidence tier, and confidence before using any quantitative claim in a client-facing map.

Gate: Every Tier 1 entity must have a proposed Entity Home URL and a Wikidata action plan.

Output: workspace/topical-map/_entity-map.md

---

Phase 3: Query Fan-Out Coverage Matrix

Load before starting:

• knowledge/frameworks/aeo-ai-search/query-fan-out-guide.md
• knowledge/frameworks/content-copywriting/qdp-qdh-qds-content-architecture.md

How AI Search Decomposes Queries

Google's AI Mode uses "Query Fan-Out" (Liz Reid, Google I/O 2025): complex queries are broken into ~8 simultaneous sub-queries, results are retrieved in parallel, then synthesized into a single answer with layered citations. To rank for a pillar topic, your content must satisfy multiple sub-intents.

Build the Matrix

For each pillar topic identified from Phase 2's entity clusters:

1. Identify the primary query — the question a user asks AI about this topic
2. Decompose into ~8 sub-queries using PAA mining (3-4 levels deep):
   • Mine "People Also Ask" for the primary query
   • Click through PAA 3-4 levels to discover recursive sub-topics
   • Categorize each sub-query by intent facet:

Facet	Example for "AI phone answering"
Definition	"What is an AI phone answering service?"
Cost	"How much does AI phone answering cost?"
Process	"How does AI phone answering work?"
Comparison	"AI phone answering vs live receptionist"
Safety/Risk	"Are AI phone answering services reliable?"
Timeline	"How long to set up AI phone answering?"
Alternatives	"Best AI phone answering services 2026"
Technical	"AI phone answering integrations with CRM"

3. Map existing content against sub-queries:

| Pillar | Primary Query | Definition | Cost | Process | Comparison | Safety | Timeline | Alternatives | Technical | Coverage |
|--------|--------------|------------|------|---------|------------|--------|----------|-------------|-----------|----------|
| [topic] | [query] | [covered/partial/gap] | ... | ... | ... | ... | ... | ... | ... | X/8 |

4. Apply QDP/QDH/QDS to each sub-query:
   • QDP (Query Deserves Page): High demand + distinct intent → dedicated URL
   • QDH (Query Deserves Heading): Moderate demand → section within hub or spoke page
   • QDS (Query Deserves Sentence): Low demand → inline mention

Gate: Every pillar must have ≥6/8 sub-queries identified. Pillars with <4/8 currently covered = priority gaps.

Output: workspace/topical-map/_fan-out-matrix.md

---

Phase 4: Information Gain Audit

Load before starting:

• knowledge/frameworks/aeo-ai-search/patent-information-gain-US12013887B2.md
• knowledge/frameworks/aeo-ai-search/hidden-aeo-edges.md

Why Information Gain Matters

Google's Information Gain patent (US12013887B2) calculates content novelty using word2vec embeddings. Content that paraphrases what already exists — even in different words — scores LOW. Content must be "orthogonal to consensus" to earn citations.

The math: IG Score = f(V_new, V_history) where high KL-divergence from existing content = high score.

Audit Process

For each pillar topic:

1. Analyze top 5-10 ranking results — list the facts, angles, data points, and framing that ALL of them share. This is the "consensus content."
2. Identify Information Gain opportunities in five categories:

IG Category	Description	Citation Multiplier
Proprietary Data	Original research, internal metrics, case studies you own	3.2x more citations (Perplexity study)
Contrarian Position	Evidence-backed views contradicting consensus	Triggers Perplexity entropy diversity signal
Experience Gap	First-person specifics AI cannot fabricate	Required for E-E-A-T "Experience" (QRG 4.6.6)
Novel Framing	Unique terminology, frameworks, mental models	Creates semantic distance from competitors
Second-Click Content	Optimized for the follow-up query after user views #1 result	Captures recursive fan-out queries

3. Score each opportunity:
   • High: Brand has proprietary data or unique experience ready to publish
   • Medium: Angle exists but requires research/data collection first
   • Low: Theoretical advantage but no current evidence to support it

Information Gain Map

| Pillar | Consensus Content (what everyone says) | IG Angle | IG Category | Novelty Score | Source/Evidence Available |
|--------|----------------------------------------|----------|-------------|---------------|--------------------------|

Gate: Every pillar must have ≥2 "High" novelty opportunities. Pillars with no proprietary data or unique experience = flag for research/data collection before content creation.

Output: workspace/topical-map/_information-gain-audit.md

---

Phase 5: Content Node Architecture

Load before starting:

• knowledge/frameworks/aeo-ai-search/geo-academic-research-synthesis.md
• knowledge/frameworks/aeo-ai-search/perplexity-ranking-reverse-engineered.md

Synthesize Into Hub-and-Spoke Architecture

Combine Phases 2-4 into the actual pages to create:

• Each entity cluster (Phase 2) becomes a Hub
• Each QDP sub-query (Phase 3) becomes a Spoke
• Each QDH sub-query becomes a section within a Hub or Spoke
• Each QDS item becomes a sentence within the relevant page

Content Node Specification

For each node, define:

Identity:

• URL (clean, entity-descriptive)
• Title
• Node type: Hub / Spoke / Entity Home

AEO Signals:

• Primary entity served (from Phase 2)
• Fan-out queries answered (from Phase 3)
• Information Gain angle (from Phase 4)

Citation Signal Requirements (evidence-safe operating targets):

• Statistics: at least 3 sourced data points per page when the topic benefits from data
• Expert quotes: at least 1 permissioned quote or attributed expert source when claims need authority
• External citations: at least 5 primary or high-quality secondary sources for research-heavy pages
• Atomic fact density: 2-3 verifiable facts per paragraph
• Paragraph length: 60-100 words when it improves scannability and passage retrieval
• Sentence length: 15-20 words maximum
• Answer position: direct answer in first 30-50 words after H2

Schema Prescription (eligibility and clarity, not guaranteed citation lift):

Content Node Type	Primary Schema	Why Use It
FAQ / Q&A content	FAQPage where eligible	Clarifies question-answer structure
How-to / Process guides	HowTo where eligible	Clarifies steps, tools, and prerequisites
Data / Research / Stats pages	Dataset where eligible	Clarifies dataset ownership and fields
All informational content	Article or BlogPosting	Clarifies authorship, dates, and subject
Entity Home pages	Organization or Person + sameAs	Clarifies canonical entity identity

Rule: All schema must have EVERY relevant attribute populated. Generic/incomplete schema produces an 18% citation penalty vs having no schema at all (Growth Marshal, Feb 2026).

Citation Impact Score

Score each node to prioritize publishing order:

Citation Impact Score (1-10) =
  (Fan-Out Coverage × 0.3)           # How many sub-queries does this page answer?
+ (Information Gain Novelty × 0.3)   # How novel is this content vs consensus?
+ (Entity Authority × 0.2)           # Does this page strengthen the entity graph?
+ (GEO Signal Density × 0.2)         # How citation-dense is this page?

Where:

• Fan-Out Coverage: (sub-queries answered / total sub-queries for pillar) × 10
• IG Novelty: Low (3), Medium (6), High (10)
• Entity Authority: Entity Home (10), Hub with Schema (7), Spoke (4), QDH section (2)
• GEO Signal Density: (planned stats + quotes + citations per page, normalized to 10)

Content Node Output

## Hub: [Entity Cluster Name]

### Hub Page: [Title]
- URL: /[slug]
- Primary Entity: [entity]
- Fan-Out Queries Answered: [list]
- Information Gain Angle: [description]
- Citation Signals: { stats: 5, quotes: 2, citations: 8 }
- Schema: FAQPage + Article
- Citation Impact Score: 8.4/10

### Spoke 1: [Title]
- URL: /[slug]
...

Approval Gate

Present the full content node architecture to the user. Confirm:

• Entity clusters make strategic sense for the brand
• Hub-and-Spoke groupings are correct
• Information Gain angles are achievable (brand has the data/experience)
• No critical sub-queries are missing
• Priority order by CIS is correct

Output: workspace/topical-map/_content-nodes.md, workspace/topical-map/_schema-blueprint.json

---

Phase 6: 90-Day Publishing Calendar + Distribution Plan

Load before starting:

• harness/brief-schema.md
• harness/skills/kai-content-calendar/SKILL.md (for format compatibility)

AEO Sequencing Rules

Sort content nodes by Citation Impact Score, then apply sequencing:

1. Entity Home pages first (Week 1-2) — You cannot build entity authority without canonical pages. Deploy Schema.org + Wikidata submissions simultaneously.
2. Hubs before Spokes — The hub establishes the semantic center of each cluster.
3. High-IG pages early (Month 1) — Pages updated within 60 days are 1.9x more likely to appear in AI answers (BrightEdge). Novel content has a freshness advantage.
4. Cross-cluster pages distributed — Pages answering sub-queries shared across pillars spread throughout the calendar to build ongoing entity connections.

Multi-Platform Distribution Plan

Each content node gets a distribution plan. Publishing on your site alone leaves 325% of potential citations on the table.

Per-node distribution template:

Platform	Format	Why	Timing
Own site	Hub/Spoke page	Gemini cites brand-owned sites 52% of the time	Day 0 (publish)
LinkedIn	Article adaptation (500-2,000 words)	#2 most-cited domain; 11% of all AI responses; #1 for professional queries	Day 1-2
Reddit	Discussion thread / comparison post	#1 most-cited domain across all AI platforms. Apollo.io achieved 63% citation rate from Reddit alone	Day 3-5
Industry directories	Listing / review	ChatGPT gets 49% of citations from directories (G2, Yelp, Capterra per vertical)	Week 1
Guest post / PR	Adapted article	Brands 6.5x more likely cited through third-party sources (Ahrefs)	Week 2-3

Platform-specific AI citation strategy:

AI Engine	What It Trusts	Your Move
Gemini	Your domain (52% of citations from brand-owned sites)	Schema markup, structured content, consistent subdomains
ChatGPT	Third-party consensus (49% from directories)	G2/Capterra listings, review sites, "best of" inclusions
Perplexity	Industry experts + niche directories	Zocdoc (health), Avvo (legal), G2 (SaaS) — vertical-specific
Google AI Overviews	Entity authority + freshness	Knowledge Graph presence, Wikidata, recent updates

Calendar Output

| Week | Date | Content Node | Type | CIS | Schema Deploy | Distribution | Entity Action |
|------|------|-------------|------|-----|---------------|-------------|---------------|
| 1 | Mon | Entity Home: About [Brand] | Entity Home | 9.2 | Organization + sameAs | LinkedIn, directories | Wikidata submission |
| 1 | Thu | [Pillar 1 Hub] | Hub | 8.4 | FAQPage + Article | LinkedIn article, Reddit | Deploy @graph |
| 2 | Mon | [Pillar 1 Spoke 1] | Spoke | 7.8 | HowTo | LinkedIn post, Reddit | - |
| 2 | Thu | [Pillar 2 Hub] | Hub | 7.5 | Dataset + Article | LinkedIn article | Deploy schema |

Brief Generation

Generate briefs for the first 4 weeks using the schema from harness/brief-schema.md, extended with AEO-specific fields:

{
  "standard_brief_fields": "...",

  "aeo_extensions": {
    "information_gain_angle": "What novel content does this piece contribute?",
    "fan_out_queries": ["Sub-query 1", "Sub-query 2", "..."],
    "citation_signals": {
      "statistics_required": 5,
      "expert_quotes_required": 2,
      "external_citations_required": 8
    },
    "entity_targets": ["Entity 1", "Entity 2"],
    "schema_type": "FAQPage + Article",
    "schema_completeness_note": "All attributes must be populated — incomplete schema = -18% penalty",
     
       
       
       
    
  

Output:

• workspace/topical-map/_90-day-calendar.md
• workspace/topical-map/_distribution-plan.md
• workspace/topical-map/briefs/w[N]-[slug].json

Integration: Calendar format is directly consumable by /kai-content-calendar for production mode.

---

Phase 7: Quality Report + Next Steps

Output a summary report at workspace/topical-map/_quality-report.md:

# AEO Topical Map — Quality Report

> **Kai root note:** `knowledge/`, `harness/`, and `scripts/` paths in this skill live in the Kai install, not the user's project. Resolve them against the first ancestor directory of this SKILL.md that contains a `knowledge/` folder (the Kai plugin root, `~/.claude/kai`, or the kai-cmo-harness repo). `MARKETING.md`, `memory/`, and any output files live in the current project. If a referenced `scripts/` command is not available in this install, say so, skip it, and continue with the file-based guidance — never fabricate its output.

## Entity Coverage
- Total entities mapped: [N]
- Tier 1 (Own): [N] — Entity Homes defined: [N/N]
- Tier 2 (Associate): [N]
- Tier 3 (Reference): [N]
- Wikidata submissions planned: [N]
- Schema deployments needed: [N]

## Fan-Out Coverage
- Total pillar topics: [N]
- Sub-queries identified: [N]
- Average coverage per pillar: [X/8]
- Critical gaps (< 4/8): [list]

## Information Gain
- Total IG opportunities: [N]
- High novelty: [N] | Medium: [N] | Low: [N]
- Requires data collection before writing: [N]
- Achievable with existing assets: [N]

## Content Nodes
- Total pages planned: [N]
- Entity Homes: [N] | Hubs: [N] | Spokes: [N]
- Average Citation Impact Score: [X/10]
- Top 5 by CIS: [list with scores]

## Distribution Plan
- Total on-site pages: [N]
- LinkedIn articles planned: [N]
- Reddit threads planned: [N]
- Directory updates planned: [N]
- Guest posts / PR planned: [N]

## 90-Day Calendar
- Total pieces scheduled: [N]
- Schema deployments scheduled: [N]
 Entity actions scheduled: [N]


  — Move into production mode with the calendar
  — Build off-site consensus web (complements the distribution plan)
  — Ensure technical foundation supports search and agent readability (crawlability, renderability, optional llms.txt, schema, accessibility tree)
  — Produce content using briefs (AEO extensions auto-loaded)
 Re-run AI Presence Scorecard (Phase 1) at 30/60/90 days to measure progress

---

Reference: Benchmark Handling

Do not paste benchmark numbers into client-facing maps unless each claim has a source URL, retrieval date, evidence tier, confidence label, and fit note. Use the following as a research checklist, not as promised outcomes.

Case Study Evidence To Collect

• Method: what changed, what stayed constant, and whether a control or holdout existed
• Measurement: engine, prompt set, sample size, dates, geography, account state, and citation definition
• Source quality: official study, academic paper, vendor report, practitioner benchmark, or internal measurement
• Applicability: whether the client's category, site authority, content depth, and distribution match the case
• Caveat: what data is missing and what should be treated as hypothesis

Citation Signal Guidance

Signal	Use As	Caveat
External citations	Provenance and source-quality support	Not a guaranteed AI visibility lift
Expert quotes	Attributable authority and customer language	Requires permission and context
Statistics / data	Specificity and answer usefulness	Must be sourced and current
Proprietary research	Information gain and PR asset	Needs methodology disclosure
Topic clusters	Coverage and internal linking	Measure by queries and conversions, not vanity citation counts alone
Freshness	Accuracy maintenance	Update only when facts or examples change
Multi-platform distribution	Legitimate audience discovery	Avoid astroturfing, hidden ownership, or fake consensus
Schema	Entity and content clarity	Schema does not substitute for useful visible content

Platform Citation DNA

AI Engine	Primary Citation Source	Implication
Gemini / Google AI surfaces	Google Search crawl and index systems	Invest in helpful, crawlable, snippet-eligible pages
ChatGPT	Mixed retrieval, browsing, and partner/source behavior	Earn coverage in trusted sources and make pages retrievable
Perplexity	Industry-specific directories	Zocdoc (health), Avvo (legal), G2 (SaaS)
Google AI Overviews	Search ranking, retrieval, and query fan-out	Focus on Search fundamentals, source quality, and page-level answers
Community/social platforms	Public discussion and professional context	Participate transparently; do not seed fake threads

AI Traffic Measurement

Track AI traffic with source/medium rules, referrer inspection, landing-page cohorts, assisted conversions, and qualitative lead-source notes. Do not compare AI and organic conversion rates without channel definitions, attribution windows, sample sizes, and confidence labels.

Sources

• HubSpot AEO Case Studies: https://blog.hubspot.com/marketing/answer-engine-optimization-case-studies
• Superlines AI Search Statistics: https://www.superlines.io/articles/ai-search-statistics/
• Yext AI Visibility Study: https://www.yext.com/blog/ai-visibility-in-2025-how-gemini-chatgpt-perplexity-cite-brands
• Semrush LinkedIn Citations: https://almcorp.com/blog/linkedin-ai-search-citations-2026/
• Schema App Entity Linking: https://www.schemaapp.com/schema-markup/case-study-entity-linking-increases-aio-visibility/
• Google AI Overview Rankings: https://wellows.com/blog/google-ai-overviews-ranking-factors/
• AI Overview Citation Shifts: https://almcorp.com/blog/google-ai-overview-citations-drop-top-ranking-pages-2026/
• Reddit AEO Signal Source: https://discoveredlabs.com/blog/reddit-as-an-aeo-signal-source-how-llms-pull-brand-mentions-to-drive-ai-citations
• GEO Paper: Aggarwal et al. (2024), Princeton/Georgia Tech
• Information Gain Patent: US12013887B2 (Carbune & Gonnet, 2024)
• Query Fan-Out: Liz Reid, Google I/O 2025