Candor is a qualitative synthetic user research platform that enables teams to conduct in-depth interviews with AI-powered synthetic participants. Unlike traditional AI research tools, Candor grounds its synthetic users in verifiable evidence, ensuring that insights are reliable and actionable, not based on AI imagination.
The platform's core functionality revolves around three key steps:
- Audience Definition: Users describe their target audience, uploading existing context documents or starting from scratch. Candor then leverages published data—including government statistics, industry reports, peer-reviewed research, and professional association data—to build detailed synthetic participant profiles. Crucially, all participant attributes are labeled with their evidence source, allowing users to verify the information. Users can also upload their own customer interviews, support tickets, survey data, and market reports, which take priority in shaping the synthetic participants.
- Intelligent Interviewing: Candor's interviewing methodology is built with research rigor, adapting to each synthetic participant. The AI interviewer follows up uniquely, deviates from the guide when interesting conversations emerge, and knows when a topic has been sufficiently explored. This dynamic approach helps surface unexpected insights and problems that might not have been included in the initial interview guide.
- Actionable Reporting: After interviews, Candor synthesizes findings into clear, actionable reports. These reports highlight strong themes, areas of disagreement, and key takeaways. Full interview transcripts are provided, allowing users to cross-reference findings with the original conversations.
Candor offers various use cases, including:
- Problem Discovery: Uncovering real pains, frustrations, and unmet needs before solution development.
- Problem Validation: Pressure-testing specific problems to determine their significance and worthiness of investment.
- Concept Testing: Evaluating products, features, names, value propositions, or messages to identify what resonates and what falls flat.
- Price Testing: Assessing new pricing tiers, packaging changes, or launch prices to understand willingness-to-pay.
Unique selling points that differentiate Candor include:
- Differentiated Modeling: It models business buyers and everyday consumers distinctly, accounting for different personality profiles, decision frameworks, and buying triggers.
- Participant Memory: Synthetic participants remember previous interactions, allowing for longitudinal studies and consistent feedback over time.
- Realistic Pushback: Participants are designed to hold positions, disagree, and stay in character, avoiding generic AI agreeableness.
- Critic Review: A separate AI critic reviews every participant response against their prior statements and underlying evidence, catching contradictions and ensuring consistency.
Candor is ideal for product teams and PMs needing faster research cycles, startups and consultants requiring quick validation, research and insights leaders looking to screen concepts efficiently, and innovation teams needing evidence-backed decisions on tight deadlines. It aims to bring disciplined curiosity and evidence to critical business decisions.
