AEO Master System (2026)

(GEO is the same system aimed at generative surfaces. AEO = winning the cited answer.)

1. PART A

   THE STACK

   (components: what you build for answer engines)

   A1

   Prompt & Query Intelligence

   (the AEO "keyword research")

   • Prompt Research (what your ICP actually asks LLMs)
   • Question Mining (PAA, AlsoAsked, Reddit threads)
   • Conversational / Natural-Language Queries
   • Recommendation Prompts ("best X for Y", "what should I use for Z")
   • Prompt Intent (research / compare / recommend / buy)
   • Prompt Clustering by Engine + Funnel (TOFU / MOFU / BOFU)

   A2

   Entity & Authority

   • Entity Establishment (company, founders, products, events)
   • Knowledge Graph Presence (Wikidata, Wikipedia, Google KG)
   • 30+ Corroborated Mentions (trusted reference domains)
   • Profile Consistency (sameAs, bios, handles across platforms)
   • Author / Expert Entities (E-E-A-T persons)
   • Brand-Category Co-occurrence

   A3

   Answer-First Content

   (extractable by design)

   • Answer-First / Inverted Pyramid
   • Self-Contained Quotable Sentences
   • Question-Based Headings
   • Direct Definitions & Short Answers
   • Extractable Formats (lists, tables, steps)
   • Stat & Proof-Point Density
   • Semantic Completeness (answer the clustered sub-questions)

   A4

   Structured Data & Machine Readability

   • Schema Markup (FAQPage, HowTo, Article, Organization, Person, Product)
   • Passage / Chunk Structure (clean section boundaries)
   • Semantic HTML
   • Tables & Structured Facts
   • llms.txt
   • AI Crawler Access (GPTBot, PerplexityBot, Google-Extended, CCBot)

   A5

   Corpus Presence

   (where models retrieve and train)

   • Reddit
   • Wikipedia / Wikidata
   • YouTube
   • Review Platforms (G2, Capterra, TrustPilot; honest reviews only)
   • Third-Party Listicles ("best X" roundups)
   • News / PR / Editorial
   • GitHub / Docs
   • LinkedIn
   • Industry Directories

   A6

   Consensus & Corroboration

   • Consensus Matching (agree with the synthesized answer)
   • Claim Corroboration (same claim across multiple sources)
   • Information Gain (the quotable proprietary edge)
   • Fact Consistency (across your own properties)
   • Framing / Sentiment Alignment

   A7

   Engine-Specific Optimization

   (the trilogy, plus the rest)

   • Google AI Overviews / AI Mode (ranking + snippet dependent)
   • ChatGPT (search + training corpus)
   • Perplexity (live retrieval, citation-heavy)
   • Gemini (Google ecosystem + KG)
   • Copilot (Bing index)
   • Grok (X + Reddit, real-time)
   • Claude (web search + citations)
   • One Engine Per URL (engine trilogy rule)
   • Per-Engine Citation-Source Patterns

   A8

   Retrieval & Technical

   (RAG-readiness)

   • AI Bot Access & robots policy
   • Bing + Google Indexation
   • Server-Side Rendered Content
   • Freshness & Recency Signals
   • Renderability / Speed
   • Canonical & Dedup

   A9

   Measurement & Monitoring

   • Share of Model / Share of Voice
   • Citation Tracking (cited? where? for which prompts?)
   • Brand Mention Monitoring (per engine)
   • AI Referral Traffic (visits from ChatGPT / Perplexity)
   • Answer Sentiment
   • Competitor Share of Model
   • Tooling (Ahrefs Brand Radar, prompt trackers)

   A10

   Strategy

   • Category Ownership
   • Recommendation Positioning (be the option models name)
   • Share-of-Model Growth
   • Multi-Engine Coverage
   • Compounding Corpus Systems
2. PART B

   THE OPERATING SYSTEM

   (5 frameworks: why you get cited)

   Premise: a citation is the model vouching for you at the moment of the question.

   Models cannot verify truth, so they count trusted sources and reward agreement.

   • B11. Entity Trust → models must recognize and trust WHO you are before citing you
   • B22. Corpus Grounding → if you are not in the sources it retrieves or trained on, you cannot be cited
   • B33. Consensus + Info Gain → agree with the synthesized answer, then add the edge that makes yours quotable
   • B44. Extractability → write self-contained, attributable chunks the model can lift without rewriting
   • B55. Co-occurrence → appear beside your category, consistently, so the model recommends you for it
3. PART C

   THE CONVERGENCE

   (strategic run-order: each step de-risks the next)

   1. 1. Establish entity trust across the corpus, become eligible to be cited→ Entity Trust
   2. 2. Map ICP prompts and reverse-engineer what each engine cites now→ Prompt Intel + Reverse Eng.
   3. 3. Seed the sources each engine grounds on→ Corpus Grounding
   4. 4. Publish extractable, consensus-matched, info-gain answers everywhere→ Consensus + Extractability
   5. 5. Measure share of model, plug per-engine gaps, reinforce co-occurrence→ Measurement + Co-occurrence
4. PART D

   THE EXECUTION PLAYBOOK

   (the AEO production run-order that builds Part A, guided by Part B, sequenced per Part C)

   Note: results appear as citations, not on a fixed timeline. AI answers are probabilistic.

   • 0. Baseline Audit → current share of model + citations per engine (Brand Radar), list the prompts
   • 1. Entity Foundation → Knowledge Graph, Wikidata/Wikipedia eligibility, 30+ corroborated citations, consistent profiles
   • 2. Prompt Map → mine ICP questions, cluster by intent + engine + funnel
   • 3. Reverse-Engineer Citations → per prompt, capture what each engine cites now (sources, format, entities)
   • 4. Answer-Asset Build → owned answer-first pages, one engine per URL, schema'd, quotable, chunked
   • 5. Corpus Seeding → Reddit answers, review-site presence, listicle inclusion, YouTube, news/PR, Wikipedia-eligible mentions
   • 6. Consensus + Info-Gain Injection → align with the current answer, add the proprietary stat/table nobody else has
   • 7. Recommendation Plays → land in third-party "best X for Y" roundups (co-occurrence with the category)
   • 8. Measure & Reinforce → track share of model, sentiment, AI referral traffic; plug gaps; refresh
   • + Cadence
     • Weekly: prompt monitoring, new Reddit + answer coverage
     • Monthly: citation audit per engine, corpus refresh, competitor share of model
     • Quarterly: prompt-map revisit, entity-trust expansion, scale / kill