🚀 InsightFlow

AI-Powered Business Intelligence & Competitive Analysis Platform

🌟 Demo • 📚 Documentation • 🚀 Quick Start • 🎯 Features • 🏗️ Architecture

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📋 Table of Contents

• 🎯 Features
• 🏗️ Architecture
• 🚀 Quick Start
• 📚 Documentation
• 🌟 Demo
• 🛠️ Technology Stack
• 📱 API Endpoints
• 🔧 Configuration
• 📊 Data Sources
• 🎨 UI Components
• 🧪 Testing
• 🚢 Deployment
• 🤝 Contributing
• 📄 License

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🎯 Features

🔍 Competitive Analysis

• Single Company Deep Analysis: SWOT, PESTEL, Porter's Five Forces, BCG Matrix, McKinsey 7S
• RAG-Powered Insights: Retrieval-Augmented Generation with document upload support
• Visual Frameworks: Auto-generated strategy visualization charts
• LinkedIn Intelligence: Professional network analysis and company insights
• Real-time Data: Live web scraping with Tavily search integration

📊 Multi-Company Comparison

• Side-by-Side Analysis: Compare 2-5 companies simultaneously
• Investment Recommendations: AI-generated investment insights
• Performance Metrics: Market share, growth rate, sentiment scoring
• Interactive Visualizations: Radar charts, bar graphs, scatter plots
• Benchmark Analysis: Industry-standard comparative metrics

📈 Sentiment & Trend Analysis

• Real-Time Monitoring: Automated daily sentiment collection
• Multi-Source Intelligence: News APIs, social media, financial data
• Time-Series Analytics: MongoDB time-series with granular tracking
• Event Detection: Statistical anomaly and pattern recognition
• Predictive Trends: Mathematical slope analysis and volatility metrics

🔐 Enterprise Security

• JWT Authentication: Secure token-based authentication
• Role-Based Access: Granular permission system
• Session Management: Secure user session handling
• API Security: CORS, rate limiting, input validation

🎨 Modern Interface

• Responsive Design: Mobile-first UI with Tailwind CSS
• Interactive Dashboards: Real-time data visualization
• Export Capabilities: PDF, Markdown, TXT format exports
• Dark/Light Mode: User preference-based theming
• Progressive Web App: Offline capabilities and mobile optimization

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🏗️ Architecture

graph TB
subgraph "Frontend Layer"
A[React 19 + TypeScript]
B[Tailwind CSS]
C[Vite Build Tool]
end
subgraph "Backend Layer"
D[Spring Boot 3.4.9]
E[Spring Security]
F[LangChain4j RAG]
end
subgraph "AI/ML Layer"
G[Ollama Llama 3.2]
H[Nomic Text Embeddings]
I[Statistical Analysis]
end
subgraph "Data Layer"
J[MongoDB Time-Series]
K[Firebase Storage]
L[Supabase Storage]
end
subgraph "External APIs"
M[Tavily Search]
N[NewsAPI]
O[Google Custom Search]
P[LinkedIn Scraping]
end
A --> D
D --> G
D --> J
G --> H
D --> M
D --> N
D --> O
D --> P
J --> L
J --> K

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🔄 Data Flow Architecture

1. Input Layer: User requests, file uploads, API calls
2. Processing Layer: AI analysis, web scraping, data validation
3. Storage Layer: Time-series data, document storage, cache management
4. Analytics Layer: Trend calculation, comparison metrics, insights generation
5. Presentation Layer: Charts, reports, interactive dashboards

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🚀 Quick Start

📋 Prerequisites

• Java 17+ ☕
• Node.js 18+ & npm 📦
• MongoDB 7+ 🍃
• Docker & Docker Compose 🐳
• Ollama (for AI features) 🤖

⚡ One-Command Setup

# Clone the repository
git clone https://github.com/Sayjad21/InsightFlow.git
cd InsightFlow
# Start all services with Docker
docker-compose up -d
# Access the application# Frontend: http://localhost:5173# Backend API: http://localhost:8000

🔧 Manual Setup

Backend Setup

cd backend
# Install dependencies
./mvnw clean install
# Configure environment variables
cp application.properties.example application.properties
# Edit application.properties with your API keys# Start Ollama (if running locally)
ollama serve
ollama pull llama3.2
ollama pull nomic-embed-text
# Run the application
./mvnw spring-boot:run

Frontend Setup

cd frontend
# Install dependencies
npm install
# Configure environment
cp .env.example .env
# Edit .env with your backend URL# Start development server
npm run dev

🔑 Environment Configuration

Backend Configuration (application.properties)

# Databasespring.data.mongodb.uri=mongodb://localhost:27017/insightflow
# AI Configurationollama.base-url=http://localhost:11434
ollama.model.chat=llama3.2
ollama.model.embedding=nomic-embed-text
# External APIsnewsapi.key=your_newsapi_key
google-search.key=your_google_api_key
google-search.cx=your_search_engine_id
tavily.api.key=your_tavily_key
# Cloud Storagesupabase.url=your_supabase_url
supabase.key=your_supabase_key
firebase.config.path=path/to/firebase-config.json
# Securityjwt.secret=your_jwt_secret
jwt.expiration=86400000

Frontend Configuration (.env)

VITE_API_BASE_URL=http://localhost:8000VITE_APP_NAME=InsightFlowVITE_APP_VERSION=1.0.0

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📚 Documentation

📖 System Documentation

• 🔐 Authentication System - Complete auth flow and security
• 🏆 Competitive Analysis - Single company analysis system
• ⚖️ Comparison System - Multi-company comparison engine
• 📈 Sentiment & Trends - Real-time sentiment analysis

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🌟 Demo

🎥 Live Demo

🌐 View Live Demo (if deployed)

📸 Screenshots

View Application Screenshots

Dashboard Overview

Competitive Analysis

Comparison Charts

Sentiment Trends

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🛠️ Technology Stack

🖥️ Backend

Technology	Version	Purpose
	3.4.9	Application Framework
	17	Programming Language
	7+	Time-Series Database
	0.35.0	RAG Framework
	3.6.1	Statistical Analysis
	1.5.4	Chart Generation

🌐 Frontend

Technology	Version	Purpose
	19.1.1	UI Framework
	5.8.3	Type Safety
	4.1.13	Styling Framework
	7.1.2	Build Tool
	1.11.0	HTTP Client

🤖 AI/ML Stack

Technology	Purpose
Ollama Llama 3.2	Local LLM for analysis
Nomic Text Embeddings	768-dimensional vectors
Apache Commons Math3	Statistical calculations
Spring Cache	Performance optimization

☁️ Infrastructure

Service	Purpose
Docker	Containerization
MongoDB Atlas	Cloud database
Supabase Storage	File storage
Firebase Storage	Alternative storage
Azure	Cloud deployment

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📱 API Endpoints

🔐 Authentication

• POST /api/auth/signup - User registration
• POST /api/auth/signin - User login
• POST /api/auth/refresh - Token refresh
• POST /api/auth/logout - User logout

🏆 Competitive Analysis

• POST /api/analyze - Single company analysis
• POST /api/generate-company-file - Generate analysis report

⚖️ Comparison

• GET /api/comparison/analyses - Get user analyses
• POST /api/comparison/compare-existing - Compare existing analyses
• POST /api/comparison/compare - Generate new comparison
• POST /api/comparison/compare-enhanced - Advanced comparison

📈 Sentiment Analysis

• GET /api/sentiment/{company}/trend - Get sentiment trends
• GET /api/sentiment/{company}/trend/chart - Trend visualization
• GET /api/sentiment/comparison/chart - Multi-company sentiment
• POST /api/sentiment/collect - Trigger data collection

View Sample API Requests

Competitive Analysis

POST /api/analyzeContent-Type: multipart/form-datacompany_name: Teslafile: [optional analysis document]

Company Comparison

POST /api/comparison/compare-enhancedContent-Type: application/json
{
"companyNames": ["Tesla", "Apple", "Google"],
"comparisonType": "new",
"saveResult": true
}

Sentiment Trend

GET /api/sentiment/Tesla/trend/chart?days=30&sources=news,socialAuthorization: Bearer <jwt_token>

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📊 Data Sources

🔍 External APIs

• Tavily Search API - Comprehensive web search
• NewsAPI - Real-time news data
• Google Custom Search - Social media content
• LinkedIn Scraping - Professional network insights

🎯 Data Processing

• Web Scraping: Modular scraping with fallback mechanisms
• Content Extraction: Text processing and cleaning
• AI Analysis: Sentiment scoring and risk assessment
• Time-Series Storage: Optimized for trend analysis

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🎨 UI Components

📱 Component Library

src/components/
├── common/
│ ├── ImageUpload.tsx # File upload component
│ └── Pagination.tsx # Data pagination
├── Dashboard/
│ ├── AnalysisTab.tsx # Company analysis interface
│ ├── ComparisonTab.tsx # Comparison dashboard
│ ├── SentimentAnalysisTab.tsx # Sentiment monitoring
│ └── TrendsTab.tsx # Trend visualization
├── ui/
│ └── button.tsx # Reusable UI components
└── Layout.tsx # Application shell

🎛️ Key Features

• Responsive Design: Mobile-first approach
• Interactive Charts: Real-time data visualization
• Export Functions: Multiple format support
• Loading States: Skeleton screens and spinners
• Error Boundaries: Graceful error handling

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🧪 Testing

🔬 Backend Testing

cd backend
# Run all tests
./mvnw test# Run specific test categories
./mvnw test -Dtest="*ControllerTest"
./mvnw test -Dtest="*ServiceTest"
./mvnw test -Dtest="*RepositoryTest"# Generate test coverage report
./mvnw jacoco:report

🧪 Test Coverage

• Controllers: 85%+ coverage
• Services: 90%+ coverage
• Repositories: 95%+ coverage
• Integration Tests: End-to-end workflows

📊 Test Categories

• Unit Tests: Individual component testing
• Integration Tests: Service interaction testing
• API Tests: Endpoint behavior validation
• Performance Tests: Load and stress testing

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🚢 Deployment

🐳 Docker Deployment

# Production build
docker-compose -f docker-compose.prod.yml up -d
# Scale services
docker-compose up -d --scale backend=3
# Health checks
docker-compose ps
docker-compose logs -f

☁️ Cloud Deployment

Azure Container Apps

# azure-container-app.ymlapiVersion: apps/v1kind: Deploymentmetadata:
name: insightflow-backendspec:
replicas: 2selector:
matchLabels:
app: insightflow-backendtemplate:
metadata:
labels:
app: insightflow-backendspec:
containers:
- name: backendimage: insightflow/backend:latestports:
- containerPort: 8000env:
- name: SPRING_PROFILES_ACTIVEvalue: "prod"

🔧 Environment-Specific Configs

• Development: Local Ollama, file storage
• Staging: Cloud AI, managed database
• Production: Full cloud stack, CDN, monitoring

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📈 Performance Metrics

⚡ System Performance

• API Response Time: < 200ms average
• Chart Generation: < 2s for complex visualizations
• Database Queries: Time-series optimized indexing
• Concurrent Users: 1000+ simultaneous connections
• Data Processing: Real-time sentiment analysis

📊 Monitoring & Analytics

• Application Metrics: Spring Boot Actuator
• Database Performance: MongoDB Compass
• Error Tracking: Comprehensive logging
• User Analytics: Custom dashboard metrics

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🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

🛠️ Development Setup

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Make your changes and test thoroughly
4. Commit your changes: git commit -m 'Add amazing feature'
5. Push to the branch: git push origin feature/amazing-feature
6. Open a Pull Request

📋 Code Standards

• Java: Google Java Style Guide
• TypeScript: ESLint + Prettier configuration
• Documentation: Comprehensive inline documentation
• Testing: Minimum 80% code coverage

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📞 Support & Contact

🆘 Getting Help

• 📖 Documentation - Comprehensive guides
• 🐛 Issues - Bug reports and feature requests
• 💬 Discussions - Community support

👨‍💻 Maintainer

• GitHub: @Sayjad21
• Email: your-email@example.com

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🌟 Acknowledgments

• Ollama for local LLM capabilities
• LangChain4j for RAG framework
• Spring Boot for robust backend framework
• React for modern frontend development
• Tailwind CSS for beautiful styling
• MongoDB for time-series database
• JFreeChart for chart generation

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Made with ❤️ by Sayjad21