GetWhy AI-Agent Consumer Interview Simulation

📌 Background

GetWhy utilizes AI agents to simulate consumer interviews, compressing traditional qualitative research from weeks into days. In 2026, the rising demand from brands for rapid validation of creative concepts and ad assets, combined with improvements in AI moderator quality, has driven this model from pilot programs to standard procurement. As advertiser budgets continue to shift toward measurable performance, the speed of asset iteration and attribution capabilities determine client retention, allowing ad-spend methodologies to be codified into data assets and product features; all financial figures are based on company financial reports and official announcements, and data provided by merchants has not been independently verified.

👤 Target Customers

Market research teams, advertising creative teams, and new product development teams at brands, as well as agencies looking to rapidly validate marketing concepts. Demand is initiated by brand business teams, followed by technical and procurement reviews before signing contracts per research project, while agencies typically sign annual framework agreements for renewals; cooperation scale is settled based on the scope of signed research projects (contracted scale remains unverified).

💰 Revenue Streams

1) Service fees charged per research project or via subscription, with clients paying for each qualitative research project; 2) Methodology replication: Packaging verified interview workflows and prompt assets into reusable solutions, charging project-based deployment and team training fees to similar brand clients (Opportunity: The annual revenue potential of this replication line remains unverified); 3) Deepening renewals (an extension of the second line): Charging additional fees for replication and ongoing training as existing clients expand interview templates to more product lines (Opportunity: The volume of repeat purchases remains unverified).

🧮 Cost Structure

AI model inference costs, operational and quality control labor for interview agents, and expenses for sales and customer success teams. Inference compute and quality control labor constitute the base costs; investment in customized interview execution and customer success fluctuates with the number of projects—the more concurrent projects, the lower the cost allocated per project.

🛡️ Moat

Experience in workflows that blend AI moderation with human respondent execution, alongside the reputation for research quality built through existing client case studies; fundamentally, this is a comprehensive barrier built on scale and accumulated experience.

🔑 Keys to Success

• Leveraging major client case studies to validate research quality
• Maintaining a hybrid delivery model of AI moderation and human quality control
• Positioning delivery speed as the core value proposition

⚠️ Risks

• Insufficient client trust in AI-generated insights
• Homogenization of AI agent output leading to a decline in perceived brand value

🏢 Cases

• Headquartered in Copenhagen, Denmark, GetWhy provides an AI-moderated market research platform with clients including eBay.

📊 SWOT Analysis