The LLMO Framework

The open standard for making your content discoverable by ChatGPT, Claude, Gemini, and Perplexity. Backed by research. Implemented in production. Open source.

Quickstart in 30 min Read the Framework GitHub

The Six Components

1. Knowledge Clarity

Clear, factual, unambiguous content that AI can understand and summarize accurately.

2. Structural Formatting

Machine-readable structure: Markdown, JSON-LD, semantic HTML, llms.txt.

3. Retrieval Signals

llms.txt, /ai/ directory, robots.txt, sitemap — help AI systems find you.

4. Authority Signals

Cross-platform presence, publications, verifiable expertise and credentials.

5. Citation Signals

Primary sources, statistics, dates, and references that AI prefers to cite.

6. Coherence Signals

Same fact tells the same story across HTML, JSON-LD, Markdown, llms.txt — single source of truth.

What the Research Shows

+115.1%Citation rate from adding statistics (GEO, KDD 2024)

25xHigher conversion from AI search vs traditional (Go Fish Digital)

-25%Traditional search usage by 2026 (Gartner)

Why LLMO?

LLMO (Large Language Model Optimization) is the practice of optimizing web content so that AI systems can accurately discover, understand, and cite it.

As AI-powered search becomes mainstream, traditional SEO alone is no longer sufficient. Users get answers from ChatGPT, Claude, Gemini, and Perplexity — not just Google. LLMO ensures your content is discoverable across all AI systems.

LLMO is the umbrella framework that encompasses AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization), providing a broader, implementation-focused standard for all LLM interactions.

Approach	Target	Goal
SEO	Search engines (Google, Bing)	Rank higher in search results
AEO	Answer engines (Featured Snippets, Voice)	Become the direct answer
GEO	Generative engines (ChatGPT, Perplexity)	Be cited in AI-generated responses
LLMO	All LLM-powered systems	Comprehensive AI discoverability