RAIDAR measures how your brand and products perform across today’s AI answer environments. That includes major LLMs such as ChatGPT, Gemini, and Perplexity, as well as Google AI Overviews. In practice, RAIDAR works like synthetic market research in LLMs: instead of testing a few isolated prompts, 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. It analyzes how brands perform across a broad, structured prompt universe and turns those findings into statistically validated visibility insights. At its core, RAIDAR measures how brands are represented and positioned in the underlying knowledge space of major LLMs, creating a more stable and strategically relevant baseline. This gives clients a robust foundation for benchmarking brand visibility, positioning and competitive perception.

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.

Clients get a KPI-based view of AI visibility and competitive strength. This includes metrics such as overall visibility performance, first-mention frequency, co-mention dynamics, attribute ownership, purchase-criteria strength, sentiment distribution, persona insights and journey-stage performance. The output is designed to do more than describe the status quo: 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.