AEO · GEO
AI search visibility
Answer engines reuse sources they can parse, trust and quote. We optimize for entities, evidence, and clear structures.
What is AI search visibility?
AI search visibility refers to how frequently and accurately your organization is cited by generative AI engines and traditional search engines utilizing AI features (like Google AI Overviews). It is about structuring your best content so that Large Language Models (LLMs) and retrieval-augmented generation (RAG) systems can confidently extract and attribute your facts.
AEO: Answer Engine Optimization
Answer Engine Optimization involves preparing your content to answer specific user queries directly. This requires entity clarity—ensuring the engine knows exactly who you are and what you do. It involves a strict question-and-answer structure, providing verifiable evidence, and maintaining consistent naming conventions across the web. AEO helps engines cite you as a definitive source.
GEO: Generative Engine Optimization
Generative Engine Optimization focuses on earning a presence inside synthesized, generated answers. For content to be citable by generative engines, it must offer unique observations, original data, or strong expert consensus. GEO requires high-quality, human-expert inputs that an LLM identifies as authoritative and valuable.
Which answer engines matter
Google AI Overviews
Synthesizes search results into AI answers directly in Google Search.
ChatGPT
Increasingly used for direct research, integrating web browsing features.
Perplexity
An answer engine built entirely around cited search and RAG.
Gemini & Claude
Core LLMs that often serve as research assistants for business users.
What we measure
Tracking AI visibility is fundamentally different from traditional rank tracking. We measure manual spot checks of core queries in target answer engines, analyze branded query behavior (if an engine recommends you, brand searches often increase), and rigorously check technical indexability to ensure your content is even available for a RAG system to find.
Frequently Asked Questions
Can schema markup get me into AI Overviews?
Schema markup helps machines parse your content. It provides clear, structured data that makes it easier for answer engines to extract key information and facts.
Does an llms.txt file help?
An llms.txt file provides a clean, markdown-friendly map of your content for LLMs. It is a best practice that reduces parsing friction and clearly communicates your authoritative sources.
Should I create content specifically for AI?
You should create content for users, formatted in a way that AI can easily parse. Answer questions directly, provide evidence, and structure your headings logically. Good content for humans is highly effective content for answer engines.
How quickly can I see results in AI search?
AI engines update their indices and RAG databases at different intervals. It can take several weeks for structural changes and new, optimized content to consistently appear in generated answers.