TransfiNitt Agent Smith - AI Market Research Platform

🎯 Project Overview

This is a hackathon MVP for an AI-powered market research platform that conducts intelligent interviews with respondents. The platform consists of multiple microservices working together to create a complete market research ecosystem.

🏗️ Architecture

The platform follows a microservices architecture with the following components:

Core Services

1. Backend Service (Node.js + Express) - http://localhost:8000
2. AI Interviewer Agent (Python FastAPI) - http://localhost:8001
3. Researcher Dashboard (React) - http://localhost:3000
4. Respondent Interview App (React) - http://localhost:3001

📁 Project Structure

TransfiNitt_Agent_Smith/
├── ai_interviewer/          # AI Agent Service (Python FastAPI)
├── backend/                 # Backend API (Node.js + Express)
├── researcher-dashboard/     # Researcher Interface (React)
├── respondent-interview/    # Respondent Interview App (React)
├── docker-compose.yml      # Docker orchestration
├── nginx.conf             # Load balancer config
└── README.md              # Main project documentation

🔧 Service Details

1. AI Interviewer Agent Service (ai_interviewer/)

• Technology: Python FastAPI
• Purpose: Intelligent conversation management
• Features:
  • Ultra-fast LLM responses using Groq
  • Redis-based conversation storage
  • Automatic sentiment analysis
  • Smart probing for vague responses
  • Conversation flow orchestration

Key Endpoints:

• POST /agent/start - Initialize interview session
• POST /agent/chat - Continue conversation
• POST /agent/end - Complete interview & get summary
• GET /agent/health - Health check

2. Backend Service (backend/)

• Technology: Node.js + Express + Prisma
• Purpose: Main API gateway and data management
• Features:
  • JWT authentication
  • PostgreSQL database with Prisma ORM
  • Session management
  • Template management
  • Integration with AI agent service

3. Researcher Dashboard (researcher-dashboard/)

• Technology: React + TailwindCSS
• Purpose: Interface for researchers to manage studies
• Features:
  • Template creation and management
  • Session monitoring
  • Respondent management
  • Analytics and insights
  • Incentive management

4. Respondent Interview App (respondent-interview/)

• Technology: React + TailwindCSS
• Purpose: Interface for respondents to participate in interviews
• Features:
  • Voice-to-text input
  • Text-to-speech responses
  • Real-time chat interface
  • Interview completion tracking
  • Responsive design

🚀 Quick Start

Prerequisites

• Docker and Docker Compose installed
• Git (to clone the repository)

Setup Instructions

1. Clone the repository:

   git clone <repository-url>
   cd TransfiNitt_Agent_Smith

2. Set up environment variables:

   • Copy .env.example to .env in the root directory
   • Configure database URLs, API keys, and service URLs

3. Start all services with Docker Compose:

   docker compose up

   This single command will start all services:

   • Backend Service (Node.js + Express)
   • AI Agent Service (Python FastAPI)
   • Researcher Dashboard (React)
   • Respondent Interview App (React)
   • PostgreSQL Database
   • Redis Cache
   • Nginx Load Balancer

4. Access the platform:

   • Researcher Dashboard: http://localhost:3000
   • Respondent Interview: http://localhost:3001

Docker Commands

# Start all services
docker compose up

# Start in background (detached mode)
docker compose up -d

# Stop all services
docker compose down

# View logs
docker compose logs

# View logs for specific service
docker compose logs backend

# Rebuild and start services
docker compose up --build

# Stop and remove volumes
docker compose down -v

🔄 Workflow

1. Researcher creates interview templates via the dashboard
2. System generates interview links for respondents
3. Respondents access the interview app and participate in AI-conducted interviews
4. AI Agent manages the conversation flow, asks follow-up questions, and analyzes responses
5. Researcher reviews results, insights, and analytics through the dashboard

🛠️ Development Team Structure

• Person 1: Agent Service (Python FastAPI)
• Person 2: Backend (Node.js Express)
• Person 3: Dashboard (React)
• Person 4: Respondent App (React)
• Person 5: DevOps & Integration

📦 Docker Support

The entire platform is fully containerized for easy deployment and development:

• Single Command Setup: docker-compose up starts everything
• Individual Dockerfiles for each service
• Docker Compose for orchestration and service dependencies
• Nginx configuration for load balancing
• PostgreSQL and Redis containers included
• No local dependencies required (Node.js, Python, databases)

🔐 Security & Authentication

• JWT-based authentication
• Postgres integration for user management
• Protected routes and API endpoints
• Secure session management

📊 Key Features

• Intelligent Interviews: AI-powered conversation management
• Real-time Communication: Voice and text input/output
• Sentiment Analysis: Automatic emotion and sentiment tracking
• Template Management: Flexible interview template creation
• Analytics Dashboard: Comprehensive insights and reporting
• Responsive Design: Works across all devices
• Microservices Architecture: Scalable and maintainable

🌐 Service URLs

• Backend API: http://localhost:8000
• AI Agent Service: http://localhost:8001

📝 Environment Variables

Copy .env.example to .env in each service folder and configure:

• Database URLs
• API keys (Groq, Cerebras(optional))
• JWT secrets
• Service URLs

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This platform represents a complete solution for conducting AI-powered market research interviews, combining modern web technologies with advanced AI capabilities to create an efficient and user-friendly research experience.