RAIDAR utilizes proprietary technology to comprehend how AI assistants, such as ChatGPT, Google Gemini, and Claude, discuss your brand. Think of it as market research for the AI age.
 
Here's how it works in simple terms: The system creates thousands of natural questions that consumers might ask AI assistants about products in your category. For example, instead of directly asking “What do you think of Nike?”, it asks questions like “Which running shoe brands would you recommend?” or "What should I consider when buying athletic footwear?" This approach reveals how AI naturally positions your brand without prompting.
 
The system then sends these questions to multiple AI platforms simultaneously and analyzes their responses to understand how often your brand is mentioned, in what context, and with what sentiment. All of this happens automatically through an 11-step process that takes some time.

RAIDAR currently analyzes the three major AI assistants that consumers use most:
- OpenAI's ChatGPT
- Google's Gemini
- Anthropic's Claude
 
This matters because these platforms collectively serve more than one billion users who increasingly rely on AI for product research and purchase decisions. When someone asks ChatGPT ,“What's the best laptop for graphic design?” or queries Gemini about “reliable car brands”, your brand's presence (or absence) in these responses directly impacts purchase decisions.

Traditional SEO tools tell you how visible your website is in Google search results. Social media monitoring tracks what people say. RAIDAR does something different: it measures how AI assistants discuss your brand when providing advice to consumers. And analyze the sentiment of these answers in the light of your brand's specific attributes.
 
RAIDAR helps you understand:
1. Whether AI assistants mention your brand when asked about your product category
2. How AI positions your brand compared to competitors
3. What aspects of your brand does AI emphasizes (quality, price, features, etc)
4. How strong are your brand attributes in comparison with your direct competition
5. Which purchase scenarios trigger AI to recommend your brand
 
It's about understanding how your brand appears in this new, rapidly growing channel for consumer discovery.

RAIDAR transforms complex AI response data into clear, actionable insights through several key features:
 
Brand Mention Analysis: See how often each AI platform mentions your brand across different types of questions. If your brand appears less frequently than competitors in recommendation queries, you know there's an opportunity to improve.
 
Sentiment Scoring: Understand whether AI assistants speak positively, negatively, or neutrally about your brand. This helps identify potential reputation issues or strengths that can be leveraged.
 
Competitive Positioning Maps: Visual charts show where your brand stands relative to competitors across different attributes like quality, value, innovation, or customer service.
 
AIDA Funnel Analysis: See how your brand performs at each stage of the customer journey:
- Awareness: Does AI mention your brand when asked about the category?
- Interest: How does AI describe your brand's unique features?
- Desire: Does AI recommend your brand for specific use cases?
- Action: Does AI guide users toward purchasing your brand?

RAIDAR ensures reliable results through several methods:
 
Multiple AI Platform Analysis: By querying several AI assistants, RAIDAR reduces the risk of getting skewed results from any single platform. It's like conducting focus groups with different demographic segments rather than relying on just one.
 
Large Sample Size: With approximately hundreds of different questions per topic, RAIDAR creates a statistically meaningful dataset. This variety of questions ensures we capture various aspects of how AI discusses your brand.
 
Consistent Methodology: Every brand is evaluated using the same question sets and analysis methods, ensuring fair and comparable results. When you see that your competitor scores higher in certain areas, you can trust that this reflects a real difference in AI visibility.

The RAIDAR system places a strong emphasis on a solid scientific foundation and the generation of robust, reliable results. This is achieved through the implementation of a multi-stage validation framework and a suite of over 15 statistical and mathematical models.

RAIDAR is designed for marketing professionals without technical expertise. It’s 100% SaaS, no tech setup needed.

While any brand can benefit from understanding their AI visibility, certain businesses see particularly high value:
 
Ideal Candidates:
1. Consumer brands in competitive categories (electronics, fashion, beauty, automotive)
2. B2B companies where buyers research solutions via AI
3. E-commerce brands competing for online discovery
4. New market entrants tare rying to build awareness quickly
5. Premium brands nneedto justify their value proposition
6. International brands expanding into new markets
 
Marketing leaders who recognize that AI assistants are becoming a primary discovery channel for their customers will find RAIDAR invaluable for maintaining a competitive advantage in this new landscape.

RAIDAR is a solution developed by OH-SO Digital. The talent of OH-SO are among the pioneers of AdTech in Europe. Back in the 1990s, they developed ADTRACTION, one of the world's first ad tracking tools. Later, with NEXT AUDIENCE, they built the world's first data management platform (DMP) based on their retargeting ad server stack. It leveraged in-memory technology to deliver personalized advertising in real-time, both on-site and off-site.