Appearing in AI generated SaaS comparisons
More and more B2B buyers are asking ChatGPT to compare SaaS tools before signing. Here's what determines whether your solution shows up in that comparison, or not.
Read articleGuides, analysis, and field notes on how AIs pick their sources — and how to show up in their answers.
More and more B2B buyers are asking ChatGPT to compare SaaS tools before signing. Here's what determines whether your solution shows up in that comparison, or not.
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More and more clients are asking ChatGPT to recommend a freelancer before contacting anyone. Here's what determines whether you show up in that answer, or not.
A generative AI can repeat false information about your brand for months. Here's what you can actually do to fix it, and what doesn't work.
Choosing an ai monitoring tool isn't just about comparing features. Discover the criteria that actually make a difference: model coverage, methodology, measurement frequency and data reliability.
AI share of voice measures how often your brand is mentioned by generative AIs compared to your competitors. Definition, calculation method and concrete examples.
LLM Monitor consolidates AI visibility measurements, reveals query fan-out and source usage, and turns competitive gaps into actionable GEO opportunities.
AEO changes the rules of the game: AIs no longer rank pages, they pick one answer. Definition, key differences from classic SEO, and a concrete method to get cited by ChatGPT, Perplexity and Gemini.
Query fan-out, web search, chunking, scoring and context: discover the workflow AI systems use to select sources and build an answer.
Structure your projects by analysis angle to turn your AI visibility data into actionable decisions
Llms.txt promises better visibility in AI systems, but its real impact remains limited. What I observed in practice between SEO beliefs and actual LLM usage.
Influencer les sources utilisées par les IA est le levier le plus direct pour améliorer sa visibilité dans leurs réponses. Méthode concrète, types de sources à travailler en priorité et ce qu'on ne peut pas contrôler directement.
Choosing the right queries to monitor in AI is a structural step that is often overlooked. A practical method, query categories to cover and selection mistakes that give you a partial view of your visibility.
Structuring an AI visibility strategy requires distinguishing what is measured, what is optimized and what is managed over time. A practical method, applicable framework and common mistakes that waste time and resources.
A brand mention in AI does not last indefinitely — it evolves, transforms and can disappear. Understanding the lifecycle of these mentions helps you anticipate variations and manage your visibility over time rather than being caught off guard.
Understanding which types of content ai models use to generate their answers helps identify the right levers to improve your visibility. Methods, criteria and real world observations.
Why do some brands always appear in AI responses while others are never mentioned? A breakdown of the real criteria, observed signals and what truly makes the difference.
Building an AI visibility dashboard requires defining the right indicators before choosing tools. A practical method, key metrics and common mistakes to manage your presence in generated responses.
Auditing your visibility across multiple AI models at once requires a clear method. A practical protocol, step by step process and key signals to get a reliable picture without spending hours on it.
Analyzing brand sentiment in AI responses goes beyond knowing whether you are cited. A practical method, key signals to observe and common mistakes to understand how AI truly talks about you.
Improving your visibility in Google Gemini takes more than good content. Real world insights, key signals and practical levers to appear in ai generated answers.