Insight Pulse

Customer feedback is often scattered across platforms, unstructured, and difficult to analyze at scale. Our project solves this problem by building an end-to-end AI-driven customer feedback intelligence system. We start with a structured dataset of customer reviews collected from platforms such as Reddit, Product Hunt, and review directories. Using machine learning and Grok-based preprocessing, we clean, normalize, and categorize raw text feedback into meaningful issue categories such as communication gaps, delayed results, and payment concerns. The processed data is served through a FastAPI backend and stored efficiently for analysis. A modern frontend dashboard visualizes key insights including channel distribution, top pain points, issue severity, and trends over time. This allows teams to quickly identify critical issues, understand customer sentiment, and make data-driven decisions. The solution is scalable, automated, and designed for real-world customer experience monitoring.