Crowdwave provides a platform that creates AI‑generated personas using census, study, and qualitative data, allowing product, marketing, and research teams to run surveys with simulated respondents that accurately reflect any target audience. The service designs and executes custom surveys, delivering rapid, encrypted results in hours rather than weeks, and includes validation metrics to ensure the fidelity of the simulated data.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional survey recruitment and screening are costly, slow, and often unable to reach niche or highly specific audiences, leading to delayed insights and limited testing of sensitive concepts.
Solution
Crowdwave offers a platform that generates AI‑modeled personas based on census data, human studies, and qualitative research, enabling teams to run surveys with simulated respondents that closely mirror any target audience. Users submit their research goals and stimuli, and the service designs custom surveys and runs them against thousands of these personas, delivering rapid, confidential results. The simulation leverages real human data to ensure high similarity to actual respondents while eliminating the risk of data leaks associated with real‑human testing. Results are provided through an intuitive dashboard, allowing teams to make informed decisions quickly without the logistical overhead of traditional field research.
Target Audience
Primary customers are product, marketing, and research teams at enterprises that need fast, reliable insights from hard‑to‑reach or confidential audience segments.
Features
- AI‑generated personas that replicate complex audience qualifiers using combined census, study, and qualitative data
- White‑glove survey design and execution service tailored to specific research objectives
- Rapid testing at scale with simulated respondents, delivering results in hours instead of weeks
- End‑to‑end encryption and advanced security to protect proprietary concepts during testing
- Validation metrics (e.g., 85% similarity score) to demonstrate fidelity of simulated responses to real humans