Reference Implementation №1 · est. 2026

AI Native MEO

The first reference implementation of the LLMO Framework, applied to local businesses — for the era when customers ask AI, not search engines, for recommendations.

While terms like AEO and GEO circulate in the AI-search optimization space, LLMO (Large Language Model Optimization) has emerged as the most precise framework. AI Native MEO is its local-business specialization.

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Definition

What AI Native MEO is

Traditional MEO (Map Engine Optimization) targeted Google Maps rankings. AI Native MEO targets the next layer: being cited and recommended by generative AI systems — ChatGPT, Gemini, Claude, Perplexity — when users ask for local recommendations in natural language.

This site is the engineer's perspective on that shift, written from the LLMO Framework standard, not from a marketing agency's vantage point.

Recent entries

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  1. 01
    Engineering

    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.
  2. 02
    Framework

    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.
  3. 03
    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.