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 ![Candor](https://images.g2crowd.com/uploads/product/image/large_detail/large_detail_a4727deb6da284e6313724334e7c00c2/candor-2026-09-26.jpg "Candor")
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# Candor Discussions

Candor is a synthetic user research platform for product teams, corporate innovators, marketers and researchers. It lets you run AI user interviews with synthetic participants (also called AI personas or synthetic users) built from real market evidence, so you can test ideas, explore customer pain points and validate assumptions in hours instead of weeks. Candor is designed for B2B research and other hard-to-reach audiences. Executives, business owners, procurement leads and industry specialists are expensive and slow to recruit. Candor lets teams pressure-test assumptions about these buyers before committing time and budget to recruiting and live interviews. Common use cases: 1. Value proposition and message testing. Test several versions of a pitch, positioning statement or marketing message with different buyer roles. See which version lands, what objections each one raises, and which language creates skepticism. 2. Customer discovery and pain point research. Explore a new market segment's workflows, frustrations and current solutions before a product manager commits weeks to recruiting. 3. Concept testing and product validation. Put new product concepts, features and pricing in front of target buyers to see how they react and what tradeoffs they make. 4. Interview preparation and follow-up. Build stronger interview guides and hypotheses before real customer interviews, and follow up on open questions afterward. Built for research validity and transparency: Most synthetic research tools are black boxes. Candor makes every participant inspectable. Each attribute carries a provenance tag: Grounded (traceable to a source), Derived (one reasoning step from evidence) or Assumed (no specific evidence, and labeled as such). Researchers can see what a participant is built from before running a single interview. Candor also guards against the known failure modes of large language models (LLMs) in qualitative research. Every answer is checked against the participant's own profile, and contradictions are regenerated. Made-up tool names and spending figures are flagged. Leading interview questions are rewritten before delivery to reduce sycophancy, which is a model's tendency to simply agree. During long sessions, participants are regularly re-anchored so they don't drift into generic answers. Where Candor fits: Candor complements human user research rather than replacing it. It turns secondary research, such as market reports and industry evidence, into participants you can interview, so teams arrive at real customer conversations with sharper hypotheses. Synthetic participants can show how a price or concept lands, but they can't predict actual purchasing behavior. Try Candor free at runcandor.com.

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