AI Engine Optimization
Optimize forAI search engines.
AI engines don't rank pages. They cite sources. Learn the technical approach to making your website the source that AI models trust, reference, and recommend.
Core Optimization Tactics
What AI enginesactually look for.
AI Engine Optimization (AEO) is the technical discipline of structuring your web presence so AI-powered search engines can accurately understand, evaluate, and cite your content. It goes beyond traditional SEO to address how large language models process information.
While traditional search engines evaluate hundreds of ranking signals to order a list of links, AI engines need to understand the meaning, authority, and context of your content to generate accurate answers. This requires a different optimization approach.
Schema.org Structured Data
JSON-LD markup that explicitly defines your business entities, products, services, and relationships: the language AI engines speak natively.
AI Crawler Configuration
robots.txt rules that allow discovery bots (GPTBot, ClaudeBot, PerplexityBot) while blocking training scrapers from using your content without citation.
Machine Context Files
llms.txt and agents.md files that provide AI systems with a structured, authoritative summary of your business, like a README for AI engines.
Content Structure Optimization
Clear heading hierarchies, factual density, proper meta descriptions, and semantic HTML that AI models can parse efficiently.
Entity Definition
Explicit definition of your brand as an entity: Organization schema, knowledge panel signals, and consistent NAP data across the web.
Directory Presence
Listings on platforms that AI engines use as training data: G2, Capterra, Product Hunt, Crunchbase, with consistent, optimized descriptions.
Capability
Traditional SEO vsAI Engine Optimization.
Traditional SEO
AI Engine Optimization
AI optimization questions.
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