Which audiences see which brands in LLMs? Who dominates the answers from ChatGPT, Gemini and Claude? Where are the growth opportunities? RAIDAR.GEO is the leading platform for AI Brand Visibility, mapping the competitive landscape across every LLM, every customer segment and every market. Precise analytics reveal a brand's position today — and the territory worth claiming tomorrow.
01 — Clients
02 — Method
A brand lives in AI training data, in chat answers and in Google's AI Overviews. RAIDAR.GEO measures all three layers and shows where a brand actually stands.
Brands and products appear across three layers of AI search: training data, chat answers and Google AI Overviews. Each layer follows its own rules and requires its own optimization levers. RAIDAR.GEO is the only platform on the market that measures all three in an integrated way. We call this Layer Coverage.
Result: Clear investment prioritization across training data, chat presence and search-embedded moments, driven by data, not assumptions.
The same question produces different answers depending on audience, use case and market. RAIDAR.GEO measures how brand position shifts across customer segments and geographies. This methodology is called the Customer Gradient.
Result: No more averaged-out numbers. You see in which segments a brand performs and where it struggles — and allocate marketing spend where the highest ROI sits.
Some sources reinforce a brand's position, others push competitors. High-authority sites often overlook the very brands they should be referencing. RAIDAR tracks every cited source down to URL level. This analysis is called Source Grounding.
Result: Content strategy turns evidence-based: you see the publications, reviews and platforms where brand presence has to be built and with what narrative.
The competitive landscape in LLMs keeps shifting. RAIDAR.GEO continuously maps progress in AI visibility. OH-SO optionally supports sharpening and executing content strategy, including developing new digital products and services to capture identified market opportunities.
Result: RAIDAR.GEO is available as a one-time snapshot or as ongoing tracking with hands-on support in strategy, content and tech.
The Map · 02
A ranking gives you a single coordinate. A map shows where you're visible, where competitors own the field, and where the whitespaces sit that you can claim next.
03 — 3 types of resolution
RAIDAR.GEO comes in three resolution tiers. The more prompts generated, the finer the topography, and the more precisely untapped territory gets identified. Higher resolution means finer whitespaces and more robust insights per segment.
First map of a single category. Suited for an initial scan, launch diagnosis or market entry.
All audiences, deep source analysis. The data foundation for the quarterly strategy.
Full market coverage. Every persona, every sub-segment, every long-tail territory. Designed for category leaders and attackers in contested markets.
04 — PHASES
Workshop at project start: brand, category, competitors and the decisive questions get defined. The query set is calibrated for the relevant market and audiences.
RAIDAR.GEO fans out thousands of prompts across the models actually used in the category: ChatGPT, Claude, Gemini and others. Each answer is parsed, classified and placed on the map.
An interactive dashboard is delivered. Teams explore the map, filter by customer segment, market, source or layer — and identify the next strategically meaningful steps. OH-SO optionally supports execution.
05 — Perspectives
MARKETING LEADS
Steer brand positioning evidence-based. You see how a brand is captured across models — and where it drifts from the intended narrative.
PRODUCT LEADS
Spot demand before competitors do. RAIDAR.GEO surfaces questions emerging in the category that no brand yet answers.
CONTENT LEADS
Content strategy becomes citation strategy. You see which sources LLMs draw from in the category — and in what format and narrative content has to land.
ECOM LEADS
Pre-purchase conversation in AI models, measurable for the first time. RAIDAR.GEO shows how a brand appears when customers ask their AI what to buy.
15 minutes. No slides. Concrete insights into brand position in ChatGPT, Gemini and Claude.
06 — FAQ
AEO (Answer Engine Optimization) is the discipline of making brands visible in the answers of generative AI models like ChatGPT, Gemini and Claude — also referred to as GEO (Generative Engine Optimization). The difference from SEO is fundamental: SEO optimizes for a single gatekeeper (Google), a ranked results list and keyword matches. AEO operates across dozens of models with different training data, reasoning patterns and answer styles that don't match keywords but interpret intent, context and audience. RAIDAR is the leading platform for AEO. Instead of a single visibility number per model, RAIDAR delivers a cartographic representation of brand presence across all relevant LLMs — including the concrete levers for optimization.
No. A single visibility score is unsuited for steering brand presence in AI. It averages across models, audiences, use cases and geographies and produces a flat number for a multidimensional reality. The same brand can be strong in ChatGPT, weak in Gemini and invisible in Claude. It can dominate for enterprise buyers and be entirely absent for SMB buyers. A single score doesn't capture this variation. RAIDAR delivers a topography instead: a map of where a brand is visible, where competitors own the space, which sources feed the answers, and where the whitespaces sit — relevant query spaces with no dominant brand. RAIDAR calls this approach AI Cartography. AI visibility becomes a strategic planning instrument: brands see their current position and concrete growth fields.
Tools for AI Brand Visibility split into two classes. Score tools like Profound, peec.ai, Otterly or Athena deliver a single visibility number per brand and model. Mapping tools like RAIDAR deliver a multidimensional map: where a brand appears, for which audience, based on which sources — plus the whitespaces where neither the brand itself nor competitors dominate. Score tools answer the question „How visible is the brand?". Mapping tools additionally answer „Where should the brand be positioned next?". For brand and content teams the second question is the strategically more valuable one; it can't be derived from the first. RAIDAR is currently the only platform in the mapping segment that measures all three layers of AI search — training data, chat interfaces and AI Overviews — in an integrated way.
Brands appear in three clearly separate layers of AI search that have to be measured independently: Training data: articles, reviews, forums and databases that LLMs ingest when learning. This layer shapes what a model knows about a category, long before a user asks. Chat interfaces: the answers users see when they interact directly with ChatGPT, Gemini or Claude. Brand perception is shaped here at scale. AI Overviews: Google AI Overviews and comparable search-embedded AI answers, where search and answers converge. Each layer has its own dynamics, levers and optimization strategies. A measurement covering only one layer — typically the chat interface — produces a distorted picture. RAIDAR is the only platform on the market that measures all three layers across all dimensions in depth; this coverage is called Layer Coverage.
No. The same question produces noticeably different answers depending on audience, context and geography. A prompt like „best laptop for video editing" delivers one answer for a creator in New York, another for a student in Berlin, and a third for an enterprise buyer in Tokyo. Measuring as a single number averages out these segments and makes audience-specific steering impossible. RAIDAR systematically tests identical query structures across audience segments, use cases and markets and maps the shifts in position, sentiment and brand attributes. This methodology is called the Customer Gradient. The result is a resolved picture of brand position in every segment — and the segments with the highest growth potential.
ChatGPT, Gemini and Claude increasingly ground their answers in concrete URLs, citing publications, comparison sites, reviews, forums and databases. Which sources are relevant per category differs sharply between models and topics: ChatGPT often leans on Reddit, Wikipedia and established tech publications, Gemini heavily on Google-indexed sources, Claude on structured reference sites. RAIDAR measures this source landscape at URL level per category — an analysis called Source Grounding. Three insights are in focus: which sources reinforce a brand's position, which push competitors, and which high-authority sources entirely overlook the brand. The third category is the most actionable: it converts content strategy into a concrete target set — which publications, reviews and forums to prioritize and with what narrative.
A statistically robust measurement requires thousands of queries per brand and category. AI outputs are probabilistic: the same question asked twice to ChatGPT often produces different answers, the same question in two language variants often a different brand position. Measurements with small sample sizes produce mostly noise, no usable signal. RAIDAR generates thousands of semantically related query variants per brand and category, across intent types, funnel stages, audiences and geographies. This approach is called High-Resolution Scanning and serves two functions: first, statistical robustness — stochasticity becomes averaged signal instead of distortion. Second, whitespaces become visible: query spaces with real demand where no brand dominates, and where early repositioning, new messaging or category ownership are possible.
Brands should monitor every LLM that shapes purchase decisions in their category — typically OpenAI ChatGPT, Google Gemini and Anthropic Claude, plus Perplexity, Microsoft Copilot or regional models depending on the market. Which models carry the biggest lever is category-specific and shifts quarterly. RAIDAR covers all relevant models per market and expands coverage as new models gain user relevance. Model-specific asymmetries are substantial: a brand strong in one model can be near-invisible in another. RAIDAR makes these asymmetries visible and enables model-specific optimization instead of optimizing against a non-existent average.
Rolling out RAIDAR requires neither engineering work nor data pipeline integration. RAIDAR is a pure SaaS application running in the browser. Marketing, brand and content teams can onboard the same day, define their competitive set and relevant audiences, and derive insights from the dashboards. Tracking codes, script installations or engineering tickets are not needed. Enterprise customers with specific compliance, SSO or API requirements get a tailored setup; the default experience is self-serve. RAIDAR is explicitly designed for teams that own brand communication, and comes without technical infrastructure prerequisites.
The reliability of LLM analytics tools rests on two factors: sample size and statistical methodology. AI outputs are probabilistic, which makes single-prompt tests inherently unreliable; a substantial share of available tools operates at this level. RAIDAR sets a different standard: every insight is based on hundreds to thousands of queries per topic, run across multiple models and repeated over time to control for model drift and stochastic variation. Over fifteen statistical and mathematical models filter signal from noise. The methodology has been independently validated by Statista. The result are findings defensible in the boardroom and operationalizable with confidence.
RAIDAR is built by OH-SO Digital, a team of AdTech and MarTech pioneers with three decades of measurement experience in Europe. In the 1990s, the team built ADTRACTION, one of the world's first ad-tracking tools. Later came NEXT AUDIENCE, the world's first Data Management Platform — grown out of a retargeting ad-server stack that was years ahead of the market. This heritage matters: RAIDAR isn't a feature bolted onto a generic analytics suite; it's a platform purpose-built for AEO. AEO is the next discipline of brand stewardship; OH-SO builds the corresponding measurement infrastructure.
A tool alone isn't enough in most organizations; the path from AI visibility data to strategic action benefits from expert guidance. RAIDAR is designed for self-driven exploration; OH-SO additionally supports clients in three areas: setup and prompt calibration, so the query set precisely captures the actual market and competition; analysis and interpretation, to identify the highest-leverage insights from the dashboards; and strategic and tactical execution — from content production and technical optimization to opening up new market opportunities. Available as one-off workshops as well as ongoing advisory.
