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Articles

Framework, engineering, and comparison pieces on AI Native MEO and the LLMO Framework.

  1. 01
    Engineering

    FAQPage vs QAPage: Local Business Q&A JSON-LD Type Choice

    FAQPage aggregates many Q&A; QAPage gives each its own URL. Which wins for AI citation? Four dimensions, four engines, hybrid pattern.
  2. 02
    Comparison

    Cold-Start Asymmetry: How AI Cites New vs Established Businesses

    Newly opened businesses face months of AI citation asymmetry against established peers. Four missing signals explain why.
  3. 03
    Engineering

    JSON-LD Review Generator: aggregateRating for AI Citation

    Reviews and aggregateRating in JSON-LD: generator patterns for 3 verticals, AI engine weighting, and 9 silent failure modes.
  4. 04
    Engineering

    Service Area Business JSON-LD: 6 Industry Patterns for AI Citation

    Service area businesses need areaServed JSON-LD. Engineers map 6 industries × 3 form choices (GeoCircle / GeoShape / AdministrativeArea) for AI citation.
  5. 05
    Comparison

    Structured Data vs Prose: Which Does AI Cite First?

    Structured data vs prose: when page copy and JSON-LD disagree about the same fact, AI assistants cite the schema first — until a corroborated external source overrides it.
  6. 06
    Comparison

    Local SEO vs AI Native MEO: What Changes When the Target Shifts From Ranking a Page to Citing an Entity

    Local SEO optimizes a page's position in a ranked list; AI Native MEO optimizes whether a structured entity fact gets cited at all. Same business, same Google Business Profile — but a relative ranking and an absolute citation are two different evaluation functions. A paradigm comparison of what actually changes when the optimization target moves from rank to entity, and why the older local-SEO assets carry forward rather than getting thrown away.
  7. 07
    Engineering

    Encoding Opening Hours as JSON-LD: How AI Assistants Answer 'Is It Open Right Now?'

    When an AI answers 'Is it open right now?', openingHoursSpecification freshness decides the citation. Overnight, 24-hour, split-shift, and holiday encoding edges.
  8. 08
    Framework

    State Fields for AI Citation: How Hours and Menus Reach LLMs

    State fields — hours, closures, availability — are cited by AI under freshness rules, not consistency alone. A framework analysis.
  9. 09
    Comparison

    Local Pack vs the AI Answer: Where Google Search and AI Assistants Pull a Business's Citation Surface

    Google's local pack and an AI assistant's answer can read the same Google Business Profile and still cite different things. The local pack rank-orders structured fields; the AI answer synthesizes facts from provenance paths. A surface-level comparison of why optimizing for rank and optimizing for citation are two different jobs on the same data.
  10. 10
    Engineering

    NAP Consistency as Entity Reconciliation: How AI Engines Merge a Business Across Sources

    "Make your NAP consistent" is advice about a symptom. The real mechanism is entity reconciliation: the probabilistic string-matching layer where an AI engine decides that your Google Business Profile, your site, and a dozen directories all denote one place. Here is what that layer actually does, in code.
  11. 11
    Comparison

    Why ChatGPT, Claude, Perplexity, and Gemini Cite Different Local Sources for the Same Business

    Ask four AI assistants about the same local business and you get four answers drawn from four different sources. The cause is not opinion — it is retrieval architecture. A comparison of how ChatGPT, Claude, Perplexity, and Gemini reach a local fact, and why provenance is the variable that decides who cites whom.
  12. 12
    Engineering

    Entity Linking with sameAs and @id: JSON-LD for AI Citation

    NAP lets AI guess your business is one entity. sameAs and @id let you declare it — the layer above NAP string matching for AI citation.
  13. 13
    Framework

    What Has to Be True Before an AI Assistant Will Cite Your Business

    Getting cited by an AI assistant is not a score you accumulate; it is a chain of preconditions that has to hold in order. This piece reframes the three axes of AI Native MEO — Confidence, Structure, Provenance — as a dependency graph: which link, if it breaks, sends the whole entity to 'not cited' regardless of everything downstream.
  14. 14
    Comparison

    Provenance Paths for AI Citation: Schema, Knowledge Graph, and Reviews

    AI assistants cite local business via three paths: first-party schema, Knowledge Graph, and third-party reviews. Which path AI trusts and why.
  15. 15
    Engineering

    LocalBusiness vs Place vs Restaurant: Which Schema.org Type Carries More Weight for AI Assistants

    An engineer's read of the schema.org LocalBusiness inheritance tree, the properties each subtype gains, and how four AI engines appear to resolve the hierarchy when deciding which entity to cite.
  16. 16
    Engineering

    How AI Assistants Read Your Google Business Profile as JSON-LD

    AI assistants don't read your GBP directly — they read a JSON-LD projection Google emits. See what that projection looks like and where the mapping breaks.
  17. 17
    Framework

    The Three Axes of AI Native MEO: Structure, Confidence, Provenance

    The three primitive axes of AI Native MEO — Structure, Confidence, Provenance — decomposed for local business citation. Why two out of three isn't enough.
  18. 18
    Comparison

    LLMO vs GEO vs AEO vs AIO: Which Framework Should You Choose?

    LLMO vs GEO vs AEO vs AIO compared: term origins, definition differences, a side-by-side table, and a decision guide for which framework to pick.
  19. 19
    Framework

    What is AI Native MEO?

    AI Native MEO — a working definition: the LLMO Framework's local-business layer for the shift from map rankings to AI citation.