📊 FeedbackSense

AI-powered SaaS platform for customer feedback sentiment analysis.

FeedbackSense transforms raw customer feedback into actionable business insights using AI-driven sentiment analysis and structured reporting. Built for production-grade reliability and data-driven decision making.

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🏗 Architecture & Data Flow

graph LR
    User[Business User] -->|Import CSV/Manual| App[Next.js Application]
    App -->|Auth/Data| Supabase[Supabase / PostgreSQL]
    App -->|Analyze| AI[Sentiment Analysis Engine]
    AI -->|Categorize| App
    App -->|Visualize| Dashboard[Analytics Dashboard]

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🚀 Core Engineering Features

1. AI-Driven Sentiment Engine

FeedbackSense utilizes a specialized sentiment analysis engine to categorize customer feedback (Positive, Negative, Neutral) and extract key topics. This enables businesses to identify pain points and "wins" at scale without manual review.

2. High-Performance CSV Ingestion

Implemented a robust CSV parsing and validation pipeline. Users can bulk-import thousands of feedback entries, which are processed and stored with proper data isolation and validation to ensure database integrity.

3. Real-Time Analytics Dashboard

Built a dynamic dashboard using Chart.js, providing real-time visualizations of sentiment trends, source distribution, and category breakdowns. The dashboard is reactive to the underlying PostgreSQL data via Supabase.

4. Secure Data Isolation (RLS)

Leverages Supabase Row Level Security (RLS) to ensure strict data multi-tenancy. Every business user can only access, modify, or delete their own feedback data at the database level, preventing cross-tenant leaks.

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💻 Tech Stack

• Frontend: Next.js 15, React 19, Tailwind CSS
• Backend-as-a-Service: Supabase (Auth, PostgreSQL, Real-time)
• Data Visualization: Chart.js
• State Management: React Context + Hooks
• Deployment: Vercel

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🛠️ Setup & Local Development

1. Clone & Install

   git clone https://github.com/belikedeep/feedbacksense.git
   cd feedbacksense
   npm install

2. Supabase Configuration

   • Create a project at supabase.com.
   • Run the provided supabase/setup.sql in the Supabase SQL Editor to initialize tables and RLS policies.

3. Environment Variables

   cp .env.local.example .env.local
   # Fill in NEXT_PUBLIC_SUPABASE_URL and NEXT_PUBLIC_SUPABASE_ANON_KEY

4. Start Development

   npm run dev

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🎯 Production Roadmap

• Webhooks: Integration with Zendesk/Intercom for automated feedback ingestion.
• Advanced NLP: Entity extraction to identify specific product features mentioned in feedback.
• Reporting: Automated weekly PDF summaries sent to business stakeholders.

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⚖️ License

MIT - See LICENSE for details.