Frequently Asked Questions

AI search refers to questions asked directly to conversational artificial intelligences like ChatGPT, Gemini, or Claude to obtain information, recommendations, or comparisons.

Unlike traditional search engines that display a list of links, these tools generate a direct response in natural language. They can recommend brands, explain products, or compare offers in just a few sentences.

For businesses, this creates a new visibility channel: users now discover brands through AI responses, and no longer solely via classic search results.

Conversational AIs are based on language models called LLMs (Large Language Models). These models are trained on massive amounts of data to understand human language and generate coherent responses.

When a user asks a question, the AI analyzes the query, identifies relevant information within its knowledge base, and generates a structured response.

In many cases, these responses include recommendations for brands, products, or services. AIs are therefore playing an increasingly important role in how users discover and compare companies.

AI assistants are increasingly used to research information, compare products, and ask for recommendations.

When a user asks, for example, “what is the best insurance,” “which school to choose,” or “what software to use,” the AI can directly cite several brands and explain their advantages.

These synthetic responses facilitate decision-making and reduce the number of steps in information gathering. As a result, AIs are progressively becoming a new recommendation channel, capable of influencing brand perception and consumer choices.

Search engines like Google primarily display a list of links to web pages. The user must then consult these pages to find the information they are looking for.

Conversational AIs work differently: they analyze the question and produce a synthetic response directly within the interface. This response can include comparisons, brand recommendations, or a full explanation of a topic.

This changes the logic of visibility: brands no longer just seek to appear in search results, but also to be mentioned in AI-generated responses.

Generative Engine Optimization (GEO) refers to the set of practices aimed at improving a brand’s presence and representation in responses generated by artificial intelligence.

In the same way that SEO aims to optimize visibility in search engines, GEO focuses on how AIs understand, describe, and recommend a brand.

This notably involves working on content quality, the consistency of information available online, and the signals that allow AI models to correctly identify a company. GEO is progressively becoming a new field of digital marketing.

SEO (Search Engine Optimization) aims to improve a site’s visibility in search engine results like Google.

AEO (Answer Engine Optimization) focuses on optimizing content to appear in direct answers, such as featured snippets or voice assistants.

GEO (Generative Engine Optimization) focuses on responses generated by conversational artificial intelligences.

These three approaches are complementary: SEO optimizes presence in search engines, while GEO aims to improve the way AIs mention and recommend a brand.

Brand visibility in AI corresponds to the way conversational artificial intelligences mention, describe, or recommend a company in their responses.

When a user asks a question like “what are the best insurance companies” or “which software should I use,” the AI may cite certain companies and not others.

Visibility therefore depends on several elements: frequency of mentions, position in responses, the context in which the brand is cited, and comparison with other players.

Artificial intelligences are progressively becoming a new recommendation channel. Millions of users already use assistants like ChatGPT or Gemini to research information, compare products, or ask for advice.

In these responses, AIs may cite certain brands, ignore others, or sometimes spread inaccurate information. Without a measurement tool, it is difficult for a company to know how it is represented.

Analyzing what AIs say helps understand your presence in this new channel and identify opportunities or risks for your brand.

Artificial intelligences can provide very useful answers, but they are not always perfectly reliable. Language models generate responses from their training data and can sometimes produce incomplete, biased recommendations or errors.

In some cases, certain brands may be overrepresented while others are totally absent from the responses.

This is why it is becoming important for companies to understand how AIs actually talk about their brand, in order to detect potential inconsistencies and improve their presence.

Language models rely on a combination of factors, including:

• information present in their training data
• content available on the web
• consistency of information associated with a brand
• relevance to the question asked

Since these mechanisms are complex and vary by model, it can be difficult for a company to understand why it appears — or doesn’t — in the responses.

If a company does not monitor its AI visibility, it may miss out on a rapidly growing influence channel. AI assistants can recommend brands, compare products, or explain services without the company having any visibility into these responses.

This can lead to several risks:

• being absent from recommendations
• being poorly described or associated with incorrect information
• leaving more space for competitors in responses

On the contrary, analyzing its AI presence allows a company to understand these dynamics and anticipate the evolution of this new discovery channel.

LLM Monitor is a platform that allows you to measure and analyze brand visibility within conversational artificial intelligences.

The tool observes how AIs like ChatGPT, Gemini, or Claude mention brands in their responses, then analyzes these results based on a set of representative queries.

Data is then grouped into indicators that allow you to understand a brand’s presence in AI responses, compare it to competitors, and track the evolution of this visibility over time.

LLM Monitor analyzes a set of questions that simulate the searches users might ask artificial intelligences. For each query, the platform observes the responses generated by several AI models and identifies the brands cited.

Several elements are then analyzed, including:

• the presence or absence of a brand in responses
• the brand’s position within the response
• the context in which it is mentioned
• comparison with other cited players

This information is aggregated to produce indicators for measuring and comparing brand visibility in AI.

LLM Monitor analyzes several conversational artificial intelligences to obtain a more complete view of brand visibility. The platform can notably analyze responses generated by:

• ChatGPT
• Gemini
• Claude
• Mistral
• and other emerging models

Since each model has different sources and behaviors, analyzing multiple AIs provides a better understanding of how a brand is represented in this evolving ecosystem.

LLM Monitor allows you to analyze a brand’s presence in AI responses by comparing it directly with that of its competitors. When several brands are cited in a response, the platform observes their position, mention frequency, and the associated context.

This information helps identify:

• the most visible players
• differences in perception between brands
• opportunities for growth

Results are presented in dashboards that allow you to track the evolution of these indicators over time.

LLM Monitor uses several indicators to analyze a brand’s presence in AI responses. Key metrics include:

• the AI Visibility Score, which measures the frequency and position of brand mentions
• Share of Voice, which compares a brand’s presence with that of its competitors
• analysis by persona or question type
• visibility evolution over time

These indicators allow companies to better understand how their brand is represented in AI responses.

LLM Monitor is for companies and organizations that want to understand and manage how artificial intelligences talk about their brand. The platform is particularly useful for:

• Marketing and communication teams
• SEO or acquisition teams
• Product or innovation departments
• Agencies that support brands on their online visibility

LLM Monitor allows marketing and SEO teams to better understand how their brand appears in responses generated by artificial intelligence. The platform provides indicators to identify:

• the queries for which a brand is cited
• competitors present in the same responses
• visibility changes over time

This information helps detect improvement opportunities, prioritize certain content or SEO actions, and track the impact of implemented initiatives.

Yes. LLM Monitor allows you to analyze a brand’s presence in AI responses by comparing it directly with that of its competitors.

When several brands are mentioned in a response, the platform observes their position, citation frequency, and the context in which they appear.

This analysis helps understand which players are the most visible for a given type of question and track how these positions evolve over time.

Yes. LLM Monitor offers freemium access allowing companies to discover the platform and get a first glimpse of their visibility in artificial intelligence.

This access allows you to view certain analyses and explore key indicators. More advanced offers provide access to more analyzed queries, full dashboards, competitive analysis, and time-based visibility tracking.