Archive · all entries
Articles
Framework, engineering, and comparison pieces on AI Native MEO and the LLMO Framework.
- 01
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.
- 02
Established vs Newly Opened: The Cold-Start Asymmetry in How AI Assistants Cite Local Businesses
An established business and a newly opened one can publish the same Google Business Profile fields and be treated as utterly different entities by an AI assistant. The reason is a cold-start asymmetry: four signals that an old business has accumulated and a new one structurally cannot, and the way each engine reasons about their absence. A comparison of where the asymmetry lives, what compensates for it, and why the trap of faking maturity is worse than admitting youth.
- 03
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.
- 04
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.
- 05
Structured Data vs Prose: Which Does AI Cite First?
When your page says 'open until 9pm' but your JSON-LD says '10pm,' AI assistants cite the schema first — until an external source disagrees. Trust order.
- 06
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.
- 07
Encoding Opening Hours as JSON-LD: How AI Assistants Answer 'Is It Open Right Now?'
An engineer's walk through openingHoursSpecification: how to encode regular hours, overnight shifts, 24-hour operation, and holiday exceptions in JSON-LD — and why a perfectly typed set of hours still won't get cited unless its freshness holds.
- 08
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.
- 09
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
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
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
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
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
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
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
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
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
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
What is AI Native MEO?
A working definition of AI Native MEO — the LLMO Framework's local-business implementation, written for the shift from search rankings to AI recommendations.