Rankeo Glossary

The process of creating proprietary terminology and securing its attribution within the Knowledge Graph so that AI engines cannot define the term without citing its creator. Parent strategy of Pressure SEO.

Strategy of creating content that redefines the expected answer for a query, displacing competitors by changing what the correct answer looks like.

A methodology that builds content infrastructure so structurally precise that AI engines must cite it. Based on three pillars: Structural Pressure, Extraction Pressure, and Salience Pressure.

First pillar of Pressure SEO: unified @graph JSON-LD architecture linking author, organization, and content entities. +16 tech, +14 GEO in 501-site benchmark.

Second pillar of Pressure SEO: front-loaded answer capsules of 40-60 words in the first 30% of every page for AI extraction.

Third pillar of Pressure SEO: entity density at 15-20% per page with interconnected named entities for topical authority.

Self-contained 40-60 word paragraphs with definitive language and zero links, designed for AI engine extraction. Core format of Extraction Pressure.

Technique of linking JSON-LD schema entities across pages using consistent @id references to create a unified domain-wide knowledge graph.

Publishing proprietary data points and benchmarks specifically designed to be cited by AI engines and content creators.

Transferring authority signals from trusted external entities to your domain through sameAs references, data citation, and co-occurrence engineering.

Composite 0-100 metric combining SEO Score and GEO Score into a single measure of website visibility across Google and AI search engines.

Metric measuring a website's visibility across 5 AI search engines (ChatGPT, Perplexity, Gemini, Claude, Grok). Part of the Rankeo Score.

Score measuring how likely a page is to be cited by AI engines. Based on front-loading, entity density, definitive language, readability, H2 questions, and capsule links.

Metric measuring citation consistency across 5 AI engines — how uniformly a brand is cited by ChatGPT, Gemini, Claude, Perplexity, and Grok.

Centralized database of all entities (brand, people, products, locations) used across a site's schema markup, ensuring consistency for AI engines.

Semantic Neighborhood analysis comparing a domain's entity coverage against competitors to identify gaps in topical authority for AI engines.

Process of migrating a domain's semantic identity within the Knowledge Graph when repositioning, rebranding, or changing topical focus.

The practice of optimizing content for AI search engines like ChatGPT, Perplexity, Gemini, Claude, and Grok to earn citations in AI-generated answers.

Experience, Expertise, Authoritativeness, and Trustworthiness — Google's framework for evaluating content quality. Extended to AI engines in 2026.

Google's database of entities and relationships used to understand real-world things. Foundation of Entity SEO and Semantic Branding strategies.

Google's AI-generated summaries appearing above traditional search results. Different from AI Mode which replaces organic results entirely.

JavaScript Object Notation for Linked Data — the recommended format for structured data markup that search engines and AI engines use to understand page content.

A search query where the user gets the answer directly on the results page without clicking through to any website. 80% of Google searches in 2026.

Your Money or Your Life — Google's classification for pages about topics that could impact health, finances, safety, or well-being. Requires highest E-E-A-T standards.

Highlighted search results appearing above organic results on Google, showing a direct answer extracted from a web page. Precursor to AI Overviews.

A site's perceived expertise on a topic, built through comprehensive content coverage, internal linking, and consistent entity signals across multiple pages.

A JSON-LD structure that contains multiple schema entities in a single script tag, connected via @id references. The foundation of Structural Pressure.