Generative Engine Optimization
How to get cited by ChatGPT, Gemini, Claude, and Perplexity. Entity clarity, structured data, and verifiable authority systems.
// SUB-PROCESSES & SPECIFICATIONS
Generative Engine Optimization (GEO) for Intelligent Platforms: Making Your Product Discoverable to AI Answer Engines
Generative engine optimization for products is how you make product pages, docs, and comparisons easy for AI answer engines to retrieve, trust, and cite. It shifts “visibility” from ranking alone to being included as the referenced source inside answers.
Generative Engine Optimization (GEO) for AI Agents: Making Your Product and Docs Retrievable, Citable, and Actionable
GEO for AI agents is the practice of structuring product pages and documentation so assistants can retrieve specific facts, cite your URL, and take correct actions without guessing.
Schema-First GEO for Ecommerce: Product Knowledge Graphs, Merchant Feeds, and LLM-Ready Structured Content
Ecommerce knowledge graph SEO turns a product catalog into a set of resolvable entities and relationships, so search engines and answer engines can retrieve correct facts and cite you with confidence. This guide explains what to model, what to publish, and which structured channel to prioritize first.
Entity Density in SEO: Building Knowledge Graphs for Search
Entity density SEO is about making the people, products, and concepts on a page unambiguous enough for search engines and LLMs to trust and cite. This guide explains how entity clarity supports knowledge graphs and citation-ready GEO.
AI Citation Strategy: How to Get Cited by ChatGPT, Perplexity & Gemini
An AI citation strategy turns your best claims into extractable, verifiable units that ChatGPT, Perplexity, Gemini, and Google can quote with confidence instead of paraphrasing from weaker sources.
Structured Data for LLMs: Schema Markup That AI Agents Understand
Structured data helps LLM-driven products resolve what your page is about, which entities it references, and how to safely quote it. This guide explains what actually gets parsed, what gets ignored, and which markup patterns earn citations.