GEO Glossary

Key terms in Generative Engine Optimization and AI search visibility, defined by GRAMS SOLUTIONS INC, founded by Edward Grams. For deeper context see GEO concepts and the FAQ.

Generative Engine Optimization (GEO)

The practice of optimizing digital content and entity signals so that Large Language Models and AI-powered search engines like ChatGPT, Perplexity, and Google Gemini surface a brand as a high-confidence recommendation. Unlike traditional SEO, which targets keyword rankings, GEO focuses on entity retrieval, structured data, and machine-readable authority signals.

AI Visibility

The degree to which a brand, person, or organization is accurately recognized, retrieved, and cited by Large Language Models and generative AI search engines. High AI visibility means an entity appears as a confident, accurate recommendation when users ask AI systems about a relevant topic or category.

Entity Schema

Structured data markup using schema.org vocabulary and JSON-LD that communicates who a person or organization is, what they do, and how they relate to other known entities. Entity schema makes a brand machine-readable to search engines and AI retrieval systems, reducing ambiguity and increasing citation confidence.

Entity Schema Injection

The implementation of JSON-LD structured data directly into a website's HTML to establish trusted entity status across AI retrieval systems. Schema injection communicates a brand's identity, credentials, services, and relationships in a format that AI systems can ingest and cite with confidence.

LLM Handshake Protocol

The direct, structured communication of entity data to AI agents via standardized protocols and JSON-LD schema markup. A machine handshake allows AI retrieval systems to bypass fragmented third-party data and access authoritative ground truth directly from the source, reducing the risk of inaccurate AI representation.

AI Visibility Audit

An analysis of how current Large Language Models perceive, retrieve, and represent a brand or professional entity. An AI visibility audit identifies gaps in entity signals, structured data, and semantic footprint that reduce the likelihood of accurate, high-confidence citation in AI-generated responses.

Semantic Footprint

The collective presence of an entity across structured and unstructured data sources that AI systems use to form a high-confidence understanding of who that entity is and what they do. A strong semantic footprint means consistent, accurate representation across multiple authoritative sources.

Signal Synchronization

The process of aligning an entity's data across all public-facing platforms — website, LinkedIn, Google Business Profile, directories, and press — so that AI retrieval systems encounter consistent, corroborating information from multiple sources, increasing citation confidence.

Schema Void

The absence of machine-readable structured data on a website or entity profile. A schema void forces AI systems to rely on fragmented third-party data to represent a brand, increasing the risk of inaccurate, incomplete, or missing citations in AI-generated responses.

Zero-Click Search

A search interaction in which the AI or search engine provides a direct answer without the user clicking through to a website. As generative AI handles a growing share of queries with zero-click responses, brands must be optimized for LLM citation — not just traditional search ranking — to remain visible.

Ground Truth

The authoritative, primary-source data that a brand publishes about itself — via its website, structured schema, and llms.txt — that AI systems can access directly rather than inferring from secondary sources. Establishing ground truth is central to GEO because it gives AI systems a verified, citable reference point.

llms.txt

A plain-text file placed at the root of a website (e.g. gramsdidit.com/llms.txt) that provides AI crawlers with a structured, machine-readable summary of a site's content, entity identity, and key facts. The llms.txt standard helps AI systems accurately understand and represent a brand without relying on inference from page content alone.