Archive · all entries
Articles
Framework, engineering, and comparison pieces on AI Native MEO and the LLMO Framework.
- 01
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.
- 02
State Fields: The Part of a Business That Changes, and How AI Assistants Decide to Trust It
Not every fact about a business is the same kind of fact. Name and address are static identity; hours, menu, and availability are state fields that change by the hour. This piece defines state fields as a layer beneath the Structure axis of AI Native MEO, and shows why an AI assistant cites them under different conditions than it cites your address.
- 03
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.
- 04
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.
- 05
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.
- 06
Wiring Your Business into the Knowledge Graph: sameAs, @id, and Entity Linking for AI Citation
An engineer's guide to entity linking for local business: how @id gives your business a stable identity, how sameAs connects it to authoritative URIs, and why explicit graph declaration beats the string-matching layer that NAP consistency lives on.
- 07
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.
- 08
The Three Provenance Paths: How AI Assistants Choose Between First-Party Schema, the Knowledge Graph, and Third-Party Reviews
The same fact about a local business can reach an AI assistant through three different provenance paths — first-party JSON-LD, the Google Knowledge Graph, and third-party review platforms. They are not interchangeable. A path-by-path comparison from the Provenance axis of the LLMO Framework.
- 09
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.
- 10
Reading Google Business Profile as JSON-LD: What AI Assistants Actually See
An engineer's reading of Google Business Profile as structured data: the GBP → schema.org mapping, the Knowledge Graph layer in between, and why Schema Confidence Score now matters for AI citation.
- 11
The Three Axes of AI Native MEO: Structure, Confidence, and Provenance
AI Native MEO is not one optimization problem but three: Structure (is your data machine-extractable?), Confidence (does the model trust it?), and Provenance (where did the model learn it from?). A taxonomy piece from the LLMO Framework's local-business implementation.
- 12
LLMO vs GEO vs AEO: Which Framework Wins for AI Search Optimization?
A precise comparison of LLMO, GEO, and AEO — the three competing names for AI search optimization — and why practitioners are consolidating on LLMO.
- 13
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.