Feedback Radar

Inspiration

Product teams collect feedback from many places: app reviews, support tickets, surveys, and customer interviews. The challenge is not collecting feedback—it's turning that feedback into clear decisions about what to build next.

I wanted to create a tool that helps product managers, founders, and builders quickly understand what users are saying and convert that information into actionable product insights.

What it does

Feedback Radar is an AI-powered feedback analysis tool.

Users can paste customer reviews, support tickets, survey responses, or interview notes into the application. The system analyzes the feedback and automatically generates:

• Executive Summary
• Sentiment Overview
• Top Pain Points
• Feature Requests
• Priority Roadmap
• Recommended Next Actions
• Product Decision Brief

The result is a structured report that helps teams identify recurring issues, prioritize improvements, and make faster product decisions.

How we built it

We built Feedback Radar using:

• Lovable for rapid AI-assisted development
• Groq API for AI-powered feedback analysis
• GitHub for version control and code management
• Novus.ai for product intelligence and repository analysis

The application uses a simple web interface where users submit feedback and receive a structured AI-generated product report within seconds.

Challenges we ran into

The biggest challenge was balancing speed with usefulness.

There were many possible directions to take the project, but we focused on solving one specific problem well instead of adding unnecessary features. Another challenge was ensuring the AI output remained structured and useful for product decision-making rather than producing generic summaries.

Integrating GitHub and Novus.ai while keeping the workflow simple was also an important part of the build process.

Accomplishments that we're proud of

• Built and deployed a working product in a very short time.
• Created a tool that can analyze multiple types of customer feedback.
• Generated actionable product recommendations instead of simple summaries.
• Successfully integrated GitHub and Novus.ai into the workflow.
• Delivered a fully functional public web application.

What we learned

We learned that shipping quickly requires strong prioritization. AI-powered development tools significantly reduce development time, but the most important skill is deciding what not to build.

We also learned how product intelligence, AI analysis, and rapid development tools can be combined to create useful products in a very short amount of time.

What's next for Feedback Radar

Future improvements include:

• CSV and survey file uploads
• Team collaboration features
• Trend analysis across multiple feedback sources
• Customer segmentation
• PDF export functionality
• Slack and Jira integrations
• Historical feedback tracking and analytics
• Cross-project product insight dashboards

Built With

• github
• groq
• lovable
• novus.ai
• react
• tailwind
• typescript