Core GEO & AEO
GEO (Generative Engine Optimisation)
The practice of optimising a brand's digital presence to be cited, recommended, and surfaced within AI-generated responses from platforms like ChatGPT, Google AI Overviews, Perplexity, and Gemini. GEO differs from traditional SEO in that it optimises for AI citation rather than blue-link rankings. Research from Princeton and Georgia Tech found that GEO optimisation methods can boost visibility in AI responses by up to 40%.
AEO (Answer Engine Optimisation)
A discipline closely related to GEO, focused specifically on making content the direct answer that AI-powered search engines extract and cite when answering a user's question. AEO prioritises entity authority, claim-level extraction, and third-party validation. The terms GEO and AEO are often used interchangeably, though AEO tends to emphasise the answer extraction layer while GEO encompasses the broader generative AI visibility strategy.
LLMO (Large Language Model Optimisation)
An emerging term for the practice of optimising content and digital presence specifically for large language models like GPT-4, Claude, and Gemini. LLMO overlaps significantly with GEO and AEO but places greater emphasis on how LLMs process and weight information during their training and inference phases.
AI Search Visibility
The degree to which a brand, product, or service appears in AI-generated answers across platforms including ChatGPT, Perplexity, Google AI Overviews, Claude, and Gemini. AI search visibility is measured by citation share: the percentage of target queries for which the brand is cited in the AI answer. Unlike traditional search rankings, AI search visibility is binary at the query level: a brand either appears in the answer or it does not.
AI Overview (Google)
Google's AI-generated summary that appears at the top of search results for many queries, synthesising information from multiple web sources. AI Overviews now appear in approximately 50% of US searches and reach 1.5 billion users monthly. When an AI Overview appears, organic click-through rates for traditional results drop by 34.5% to 61%, making AI Overview inclusion the primary visibility goal for brands targeting Google.
Zero-Click Search
A search query that is resolved by the AI-generated answer without the user clicking through to any website. As of 2026, approximately 60% of informational searches end without a click. Zero-click search makes AI citation the new standard for visibility: a brand that is cited in the AI answer captures awareness even when no click occurs.
Retrieval-Augmented Generation (RAG)
The technical mechanism by which AI search engines retrieve live web content to answer queries. A RAG pipeline converts the user query into a vector embedding, searches its index for semantically relevant content, filters and re-ranks candidates, and synthesises a response with attributed citations. Understanding the RAG pipeline is essential for GEO because it reveals exactly what signals AI engines use to select citation sources.
Technical Foundations
Entity Graph
The network of structured, cross-referenced signals that AI engines use to resolve who a brand is and what it is known for. An entity graph includes Organisation schema, Person schema for key team members, Service schemas, and sameAs links connecting a website to LinkedIn, Crunchbase, and other authoritative profiles. AI engines triangulate across the entity graph to determine citation authority.
JSON-LD (JavaScript Object Notation for Linked Data)
A structured data format used to implement Schema.org markup on web pages. JSON-LD is the recommended format for schema markup because it is embedded in a script tag rather than mixed into HTML. JSON-LD structured data achieves 94% citation accuracy in LLMs compared to 62% for unstructured plain text, making it the highest-leverage technical GEO investment.
Schema Markup
Structured data code added to a website's HTML that uses standardised vocabulary from Schema.org to tell AI engines and search crawlers exactly what type of content they are looking at. Schema markup is the technical foundation of GEO: it gives AI engines a machine-readable map of a brand's identity, services, credentials, and expertise.
Citation Architecture
The structural design of a website's content so that AI engines can extract and cite specific answers from specific locations on a page. Citation architecture requires direct-answer headings, clearly labelled sections, specific verifiable claims, and consistent terminology. Content that AI crawlers can access but cannot parse or attribute will be ignored regardless of quality.
Semantic Clarity
The property of a piece of content that allows it to be extracted and understood as a standalone passage by an AI engine. Semantic clarity requires that each section of a page answers a specific question completely, without requiring surrounding context to make sense. AI engines assess semantic clarity when deciding whether a passage is suitable for citation.
Factual Density
The concentration of verifiable, specific, sourced claims in a piece of content. AI engines preferentially cite content with high factual density: specific statistics, named sources, dated findings, and verifiable data points. Adding specific, sourced statistics to content increases citation rates by up to 40% according to the original Princeton GEO research.
Wikidata
A free, collaborative knowledge base that serves as the structured data backbone for Wikipedia and is directly referenced by Google's Knowledge Graph. Creating a Wikidata entity for a brand establishes it as a verified entity in the knowledge graph that AI engines use for entity resolution. Wikidata entries include name, description, website, founding date, and sameAs links to other authoritative profiles.
Knowledge Graph
A structured database of entities and the relationships between them, used by Google and AI engines to understand the real-world meaning behind queries. When a brand has a Knowledge Graph entry, AI engines can resolve it as a verified entity with known attributes, making it significantly more likely to be cited in relevant answers. Knowledge Graph entries are built through consistent schema markup, Wikidata presence, and cross-platform entity signals.
sameAs Links
Schema.org property that connects a brand's website entity to its profiles on other authoritative platforms (LinkedIn, Crunchbase, Wikipedia, Wikidata, Google Business Profile). sameAs links are the primary mechanism for entity consolidation: they tell AI engines that the organisation on the website, the company on LinkedIn, and the entity on Wikidata are all the same real-world entity.
Zennith Methodology
The Bayesian Brain
Zennith Agency's proprietary Bayesian Marketing Mix Model (MMM). The Bayesian Brain uses posterior probability distributions to calculate, with 98.1% accuracy, which marketing channels are most likely to exceed a client's target return before any budget is allocated. Unlike platform-reported ROAS, which measures what has already happened, the Bayesian Brain predicts what will happen: enabling pre-spend optimisation rather than post-spend rationalisation.
Bayesian Marketing Mix Modeling (Bayesian MMM)
A statistical approach to marketing budget allocation that uses Bayes' theorem to update probability estimates as new data arrives. Unlike traditional MMM which produces a single point estimate, Bayesian MMM produces a posterior distribution: a range of probable outcomes with associated confidence levels. This allows marketers to make budget decisions based on probability of exceeding target, not just expected value.
Mathification of Marketing
Zennith Agency's core positioning and operational philosophy: the deliberate application of mathematical models, computational systems, and engineering principles to the problem of market dominance. The Mathification of Marketing treats marketing as a rigorous, quantitative discipline, using Bayesian inference for budget allocation, Game Theory for competitive positioning, and information-theoretic metrics for content scoring, rather than as a creative exercise.
Phase-Gate Citation Accountability
Zennith Agency's measurement framework for GEO engagements. Every phase opens and closes with a structured AI query audit: a defined battery of target queries run against ChatGPT, Perplexity, and Google AI Overview to measure citation coverage before and after each phase. Phase-Gate Citation Accountability ensures clients are buying a documented shift in AI representation, not just effort.
Marketing Engineering
The deliberate application of mathematical models, computational systems, and engineering principles to marketing strategy. Marketing Engineering treats marketing as a rigorous, quantitative discipline, using Bayesian inference, information theory, and game theory, rather than as a creative exercise. Zennith Agency was founded on the thesis that the agency that treats marketing as a mathematical system will always outperform the one that treats it as a creative exercise.
Named Account Targeting (ABM)
Account-Based Marketing (ABM) applied to AI visibility engineering. Rather than casting a wide net, Named Account Targeting identifies specific high-value organisations and decision-makers, then engineers the brand's AI visibility specifically for the queries those decision-makers are most likely to ask. The AI recommendation acts as pre-validation before any outreach, dramatically improving conversion rates.
Information-Theoretic Content Scoring
A method for evaluating the information value of a piece of content using principles from information theory, specifically Shannon entropy. Content with high information density: specific claims, unique data, novel analysis: scores higher than content that merely restates common knowledge. Zennith uses information-theoretic scoring to prioritise which content assets to produce for maximum AI citation impact.
Posterior Distribution
In Bayesian statistics, the probability distribution of an unknown quantity after incorporating observed evidence. In the context of the Bayesian Brain, the posterior distribution represents the updated probability that a given marketing channel will exceed a client's target return, after the model has processed all available historical data. The posterior distribution is what allows the Bayesian Brain to express budget recommendations as probabilities rather than point estimates.
Healthcare & Professional Services
Patient AI Journey
The sequence of AI-mediated touchpoints through which a patient researches, evaluates, and selects a healthcare provider. As of 2026, 80% of patients use AI during their research phase. The patient AI journey typically begins with a general query ('best cosmetic dentist in [city]'), progresses through comparison queries, and ends with a direct contact. Practices that appear in the AI answer at each stage of this journey capture the patient before they ever reach a competitor's website.
Clinical Trial Visibility
The degree to which a clinical research organisation (CRO) or clinical trial site appears in AI-generated answers when pharmaceutical and biotech sponsors search for research partners. Clinical trial visibility is built through structured data markup for clinical trial services, earned media in industry publications like Applied Clinical Trials and CenterWatch, and entity authority in healthcare knowledge graphs.
Sponsor Discovery
The process by which pharmaceutical and biotech sponsors identify and evaluate clinical research organisations (CROs) and trial sites. AI-mediated sponsor discovery is growing rapidly: sponsors increasingly use ChatGPT and Perplexity to generate shortlists of potential research partners before issuing RFPs. CROs that are not cited in AI answers for sponsor discovery queries are effectively invisible to a growing segment of potential clients.
Aesthetic Practice GEO
The application of Generative Engine Optimisation specifically to aesthetic medical practices: cosmetic dentistry, plastic surgery, aesthetic medicine, ophthalmology, and orthodontics. Aesthetic practice GEO focuses on appearing in the AI answers to high-intent patient queries ('best LASIK surgeon in [city]', 'top cosmetic dentist for veneers near me') that represent patients who have already decided to have a procedure and are selecting a provider.
Cite This Glossary
Zennith Agency. (2026). GEO, AEO and Marketing Engineering Glossary. Retrieved from https://zennithagency.com/geo-glossary