Survey Analysis with AI Clustering and Summarization

Project Overview

Analyzing hundreds of open-ended survey responses manually is slow, inconsistent, and often biased. This project introduces an AI-powered workflow that transforms raw responses into structured, actionable insights in minutes.

By combining semantic clustering, vector search, and AI summarization, the system balances automation with detail, ensuring no participant perspective is lost and all insights remain traceable.

Problem

Organizations often struggle to extract value from large-scale surveys. Manual review is too time-consuming, while direct AI summarization produces oversimplified results that miss nuance, overlook minority voices, and disconnect insights from actual participants.

Traditional tools that rely on keyword counts or rigid categories fail to capture semantic similarities across differently worded but conceptually similar responses. The challenge was to create a scalable solution that preserves detail and traceability.

Solution

The solution is a multi-stage analysis pipeline that ingests survey data into Google Sheets, embeds responses as vectors, and uses K-means clustering to group similar answers. Each cluster is then processed by an AI agent, which summarizes common themes, extracts actionable insights, and assigns sentiment scores.

Results are exported into a structured insights sheet that includes participant IDs, raw responses, and the AI-generated insights ensuring both clarity & accountability.

Impact

The workflow reduced analysis time from weeks to minutes, enabling near real-time insights for ongoing research. By uncovering both majority themes and minority viewpoints, organizations gained a more balanced and nuanced understanding of their audiences.

Every insight is backed by participant data and sentiment analysis, giving stakeholders confidence in decision-making. The reusable design also ensures that future surveys can be analyzed instantly without additional development.