As large language models (LLMs) evolve into a primary source of information, the way brands appear within them is becoming increasingly important. Today, the game is changing. It is no longer enough to rank first on search engines. What truly matters is convincing AI systems to recommend your brand as the top choice.
4 Stage Methodology
As Dimitris Bachtsevanis, Director of Performance & Partner, explains, “A brand’s presence within LLMs is no longer only about visibility, but about how its narrative is shaped through AI-generated responses.”
The approach is built around a structured four-stage methodology:
- Semantic Intent Intelligence
The analysis goes beyond traditional keyword research and focuses on understanding user intent through human-centric prompts, reflecting the way real users formulate questions for AI systems.
- Cross-Model Stress Testing
Parallel simulations are conducted across leading LLMs in order to measure each brand’s visibility within different AI ecosystems. The process takes place in a clean-slate environment without interaction history, minimizing influences such as historicity bias and personalization effects.
- Citation & Source Authority Tracking
The methodology tracks the sources AI systems rely on, including websites, forums, and reviews, enabling a deeper understanding of how specific digital assets influence generated responses.
- Share of Model Voice (SoMV) & Sentiment Analysis
The process evaluates each brand’s share of mentions compared to competitors, while also analyzing sentiment and the broader context in which the brand is presented.
This methodology helps businesses better understand how brands are represented within the emerging AI-driven information landscape, providing a more structured framework for evaluating digital presence and influence.