Traditional survey-based research often faces challenges such as design inefficiencies, respondent bias, and low-quality data collection. As the demand for rapid, data-driven insights grows, there is a critical need for systems that bridge the gap between raw data collection and actionable intelligence. This project presents Survonica, an intelligent ecosystem designed to automate and optimize the end-to-end lifecycle of survey-based research. The platform introduces a paradigm shift in data collection by utilizing computational intelligence to facilitate seamless survey design through conversational interfaces and automated structure generation. To ensure data integrity, the system implements a unique dual-layer validation framework: first, auditing survey questions for bias and ambiguity during the design phase, and second, employing a rigorous real-time screening process to flag and exclude low-effort or fraudulent responses. Furthermore, the system transforms raw data into high-dimensional intelligence reports. By integrating multi-faceted analytical modules—including sentiment analysis, trend identification, and respondent profiling—the platform provides researchers with deep insights that exceed traditional statistical summaries. This project demonstrates how intelligent automation can significantly reduce manual overhead while elevating the quality and reliability of data-driven decision-making.
Tools: React,Django,PostgraSQL,Tailwind,HTML5,CSS,JavaScript
Department: Department of Computer Science
Poster