RAIDAR measures how your brand performs across today’s AI answer environments, on three distinct levels. Brand Resonance captures how deeply your brand is embedded in the training data of major LLMs like ChatGPT and Gemini, so the foundational knowledge AI models draw on. Brand Visibility measures how your brand actually appears when users interact with AI chat interfaces and Google AI Overviews in real time. And lastly, Source Strengths analyses which online sources from your own website to Reddit, LinkedIn, Wikipedia, news outlets and beyond are being referenced by AI systems when they talk about your category.
Together, these three levels show not just whether your brand is visible, but why and where the biggest levers for improvement are. In practice, RAIDAR works like synthetic market research in LLMs: it simulates up to 20,000 real-world prompts across audiences, intents, markets, funnel stages and customer journeys to measure visibility, recommendation presence, sentiment and competitive position at scale.

RAIDAR is built as a prompt analytics and measurement system, not a simple prompt checker. Where most tools only track what AI chatbots say in response to a handful of prompts, RAIDAR measures across multiple data layers: the knowledge baked into LLM training data, the real-time answers users see in chat interfaces and AI Overviews, and the underlying sources AI systems reference. It analyses how brands perform across a broad, structured prompt universe and turns those findings into statistically validated visibility insights. This gives clients a robust foundation for benchmarking brand visibility, positioning, sentiment and competitive perception, not just a snapshot, but a full picture of how and why AI systems represent their brand the way they do.

Every RAIDAR setup is calibrated from client input. We use your brand and category specifics to define the measurement parameters (e.g. competitors, brand attributes, target groups). This makes the analysis highly specific to your business, not a generic off-the-shelf benchmark.

RAIDAR is built for statistical validity by design. Instead of drawing conclusions from a small set of manually chosen prompts, it constructs and validates a large prompt universe, ensures comprehensive and equal coverage across key dimensions such as personas, purchase criteria, journey stages, pain points and brand attributes, and samples that space uniformly using semantic mapping and PCA-based distribution logic. On top of that, RAIDAR applies stable prompt classifications, bias correction through prompt rotation, and statistical validation methods such as bootstrap testing, ANOVA and co-occurrence analysis. The result is a far more robust and reproducible measurement baseline for AI visibility, brand perception and competitive positioning. The methodology has been reviewed and approved by Statista.

Clients get a KPI-based view of AI visibility and competitive strength, broken down by measurement level. This includes metrics such as overall visibility performance, first-mention frequency, co-mention dynamics, attribute ownership, purchase-criteria strength, sentiment distribution, persona insights, journey-stage performance and source attribution. Because RAIDAR measures across training data, real-time retrieval and source signals, the output distinguishes between long-term brand authority and short-term visibility shifts. It helps identify where brands are strong, where they are losing relevance, and where the biggest opportunities for improvement lie.

RAIDAR is designed to turn measurement into action. Beyond visibility and benchmarking, the analysis helps identify where to improve across content, UX, technology, data, operations and communication. This creates a clear basis for prioritised next steps, transformation roadmaps and continuous optimisation over time.