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The GEOly blog covers GEO (Generative Engine Optimization), AI search and agentic commerce — data-driven insights, benchmarks and playbooks on how brands get mentioned, cited and recommended across ChatGPT, Gemini, Perplexity and Google AI. Browse every topic by tag or author below.

A semantic moat is the defensible advantage a brand holds inside AI models' structural understanding of its category — when it runs deep, engines like ChatGPT cannot explain the topic without naming you, which is what wins winner-take-few AI answers.

Prompt engineering for SEO/GEO is the practice of researching the natural-language prompts customers type into AI engines and optimizing content so your brand becomes the answer — the AI-era successor to keyword research.

The Model Context Protocol (MCP) is the open standard that lets AI agents like Claude, ChatGPT, and Cursor connect to live external data and tools through one uniform interface — the plumbing that turns a brand from readable text into a callable system.

llms.txt is a proposed web standard — a curated Markdown index at your domain root that tells AI models which pages matter most; it takes an hour to ship, has no proven ranking weight in 2026, but hedges your brand for the era of live-browsing AI agents.

A brand knowledge graph is a structured map of your brand as an entity — nodes for facts, edges for relationships — that AI engines use to disambiguate, reason about, and recommend you; a weak entity never even enters the answer.

Grounding queries are the search queries an AI model generates and runs behind the scenes to retrieve fresh web content before answering — rank for them and you can be cited; miss them and the AI writes its answer without you.