This technology is an AI-powered digital twin survey and panel research platform that generates synthetic respondents from historical survey and persona data to enable rapid, low-cost market research, concept testing, and structured user feedback.
Current survey and panel research relies heavily on recruiting human participants, a process that is often expensive, time-consuming, and operationally complex. Organizations frequently wait for long periods to field surveys, collect responses, and conduct follow-up studies, limiting the speed of decision-making and iterative testing. Existing synthetic research tools are often fragmented, enterprise-focused, or limited to narrow use cases, leaving smaller organizations and academic researchers without accessible alternatives. There is a growing need for scalable research approaches that can rapidly generate structured insights while reducing the cost, logistical burden, and turnaround time associated with traditional human-subject research.
This platform generates synthetic research participants, or digital twins, using historical survey records and persona-based data from publicly accessible and verifiable sources. It supports ingestion of structured survey instruments while preserving key survey logic such as branching, randomization, and multi-section workflows. The system converts AI-generated outputs into structured survey responses and automatically produces summary statistics, charts, open-ended insights, and downloadable reports. It also supports persistent digital twin identities for follow-up interviews, panel-style interactions, and feedback on uploaded documents or media.
IR CU26312
Licensing Contact: Joan Martinez
