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

    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

    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.
  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.