What is GEO? Defining AI search optimisation
GEO, or Generative Engine Optimization, is the set of methods aimed at making a brand, organisation or piece of content visible, understandable and citable in answers produced by generative AI. Its objective is not merely a position in a list: it is to be selected as a source, mentioned in the right context and associated with an accurate narrative.
Search is entering the answer. Some discovery now takes place through a synthesis already written by an AI answer engine. Users see more than ten links: they receive an interpretation, sometimes with citations. Visibility in these answers becomes part of brands’ digital presence, without every search or audience following the same journey.
The issue is becoming tangible: according to the Microsoft AI Economy Institute, estimated AI-tool use among France’s working-age population rose from 40.9% in the first half of 2025 to 44.0% in the second half. France ranks fifth in this index. This is an estimate based on adjusted telemetry, not a measurement of all French people or their purchases.
GEO, AEO, LLMO, AIO: what do these terms mean?
- The category is young and its vocabulary is still evolving
- GEO means Generative Engine Optimization: a brand’s presence, citations, recommendations and context in generative answers
- AEO means Answer Engine Optimization: the ability of content to provide an answer that an answer engine can use directly.
- LLMO means Large Language Model Optimization: how an entity or content is understood, retrieved and represented by large language models
- AIO means AI Optimization: a broader term for optimising AI-powered search and discovery experiences
- AI Overviews, by contrast, is a Google Search feature, not a discipline: it displays generative summaries on certain results pages.
In practice, AI search optimisation, generative search optimisation, GEO and LLMO often refer to the same challenge: appearing in conversational systems and generative engines’ answers. AEO is broader and also includes answer snippets or voice search. For Google’s generative experiences, SEO fundamentals still apply.
The expression LLM ranking can be misleading. AI does not always produce a stable ranking. It may mention several players, recommend only one or reuse information without producing a league table. The issue is therefore not just “ranking on ChatGPT”, but understanding how a brand enters the answer.
GEO vs SEO: what is the difference?
SEO and GEO share a foundation, but do not measure the same outcome. SEO optimises a page for a system of ordered results. GEO concerns information within a system that searches, compares, synthesises and writes the answer itself.
- Visible output: SEO produces a ranked list of pages; GEO observes a synthetic answer, citations and recommendations
- Visibility unit: in SEO, URL position, impressions and clicks; in GEO, mentions, citations, prominence, context and narrative.
- The central question changes from “Where does my page rank?” to “Is my brand selected, and how is it presented?”
- Key signals: crawling, indexing, relevance, authority and links for SEO; sources, reputation, entity consistency and evidence quality for GEO
- These categories overlap: they are not two separate recipes.
- Measurement: a SERP may offer a relatively reproducible snapshot, whereas a generative answer varies and calls for repeated observations
- Desired outcome: a position and click in SEO; selection as a source or recommended option in GEO
- A mention without a link is not a click, and a one-off citation is not an established position.
GEO does not replace SEO. Generative engines still rely on accessible content and, depending on the product, search systems or web indexes. Google says its AI features rely on existing ranking and quality systems. The difference lies in the final object: SEO optimises presence in results; GEO observes presence in the answer.
Which signals does AI actually reuse?
No platform publishes a universal recipe for being cited by ChatGPT or another engine. Three signal families are useful for organising work: source quality, reputation in the ecosystem and narrative consistency. These are analytical dimensions, not guaranteed ranking factors.
Source quality
AI can only reuse what it can find, interpret and connect to a question. Pages need to be accessible, clearly structured and precise enough to serve as evidence. A clear definition, sourced data point or documented methodology is more reusable than vague promotional language.
The foundational study by Aggarwal and colleagues, published at KDD 2024, observed visibility gains of up to 40% in its experimental setting. That figure promises neither traffic nor lasting citations. It shows that content structure, citations and factual richness can affect reuse under the studied protocol. Martinez’s preprint, submitted in July 2026, notes that these conditional results do not by themselves demonstrate better long-term organic discoverability.
Reputation in the ecosystem
A brand does not define itself alone. Engines may compare its website with specialist articles, directories, comparisons, reviews, databases and third-party mentions. Reputation is therefore not just backlink count: the quality, independence and relevance of sources describing the entity also matter.
A claim repeated only on a brand’s own website remains self-reported. The same claim confirmed by several credible sources has stronger support. Publishing a comparison on your own blog, as hitak does, does not turn it into an independent ranking.
Narrative consistency
AI should be able to answer unambiguously: what does this brand do, for whom, in which market, and with what verifiable difference? If the website, public profiles and third-party sources tell conflicting stories, the entity becomes harder to interpret. A stable description supported by converging evidence is easier to represent. Sustainable GEO is less about repeating a slogan than building a consistent, documented narrative.
Why GEO hacks do not last
Every new discipline attracts shortcuts: keyword stuffing, mass-produced near-identical pages, fake consensus, hidden instructions or files presented as mandatory for AI. These techniques confuse one-off exposure with authority. An effect in an isolated test does not build the third-party sources, reputation and consistency needed for sustained visibility.
Google states that no special markup or “AI” file is required to appear in AI Overviews or AI Mode. Content should above all be accessible, original, useful and consistent with search fundamentals. The right test is simple: does this tactic improve information available to users, or merely try to manipulate a system? In the latter case, its lifetime depends on a loophole or a particular model. That is not a defensible strategy.
Measuring GEO: why depth beats frequency
Generative answers are non-deterministic. Asking the same question several times may produce different wording, sources and recommendations. One observation is not necessarily representative. Schulte, Bleeker and Kaufmann’s preprint, submitted on 8 April 2026, studies variation across runs, wordings and time. It recommends assessing visibility as a distribution derived from repeated measurements, rather than a single point.
Running many questions once a day gives frequency, but not necessarily certainty for each one. For an aggregate metric over a large portfolio, this may be appropriate. To understand a brand’s position with a particular audience, intention and question type, a one-answer sample remains fragile. Frequency and depth address different measurement objects.
Measuring an unstable thing often does not make it more true.
Robust measurement starts by defining the observed point: audience, intention and question type. It repeats that point enough to describe its variability; around fifteen repetitions is neither a universal scientific threshold nor a statistical guarantee. It distinguishes mentions, citations, recommendations, context and sources; treats engines separately; then compares results at a cadence consistent with real change and comparable scope.
Frequency remains useful for detecting a break or rapid change. It does not replace sampling depth when establishing a state. A GEO action often follows several steps: publication or correction, indexing, possible reuse by credible sources, then incorporation into answers. These delays may span weeks or months, without a guaranteed schedule. Reading a score every morning does not shorten the chain or prove that an action caused its movement.
How can you improve visibility in generative AI?
A serious strategy starts less with “produce more” than with clearer, more provable and consistent information. Map real questions: the problems, comparisons and criteria prospects express, from definition queries to recommendation requests. Make the entity explicit: category, offering, audiences and verifiable differences, with logical internal linking.
Publish evidence: definitions, data, studies, methods, documented cases and honest comparisons. Develop credible third-party sources through media, expert contributions, editorial partners and sector references. Measure before concluding: observe several answers, analyse sources and compare changes with actions without confusing correlation with causality. A one-off citation is not an established position. GEO is not a fixed ranking: it is a visibility trajectory.
GEO FAQ
Does GEO replace SEO?
No. SEO remains necessary to make content accessible, indexable, understandable and credible. GEO adds a reading of how that content and reputation are reused in a generative answer.
How long does it take to be cited by AI?
There is no guaranteed delay. The brand’s starting state, indexing, third-party sources and the engine concerned affect timing. A citation may appear quickly; sustained visibility requires a consistent ecosystem presence and multiple observations.
Can you guarantee a ChatGPT citation?
No. No provider controls a third-party model’s answers. A GEO strategy may improve the quality, accessibility and credibility of available information, but cannot guarantee citations or promise quantified gains.
What is the difference between GEO and AEO?
AEO targets a direct answer usable by different engines. GEO focuses on generative systems that search, synthesise and reformulate multiple sources. The two disciplines overlap substantially.
How do you measure a brand’s AI visibility?
Examine mention frequency, citations, prominence, context, associated competitors and sources used, with an explicit definition for each indicator. Since answers vary, observe each point repeatedly rather than through one test. Do not compare scores produced under incompatible scopes.
Follow the hitak launch
hitak is launching. The platform is designed to measure brand visibility in generative answers through repeated observations, then connect findings to evidence and prioritised recommendations. It guarantees neither citations nor quantified improvements.
Subscribe to the newsletter for the launch announcement and our methodology notes. Subscription is immediate after anti-spam verification and consent; it does not reserve early access or commit you to a paid subscription.
Sources and verification
Google Search Central — AI features and your website
Microsoft AI Economy Institute — Global AI Adoption in 2025 (8 January 2026)
Schulte, Bleeker and Kaufmann — Don’t Measure Once (preprint, 8 April 2026)
Aggarwal et al. — GEO: Generative Engine Optimization (KDD 2024)
Martinez — Optimizing Visibility in Generative Engines: A Critical Survey (preprint, 15 July 2026)
