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TurboPredict AI - Industrial Monitoring & Analytics Platform

A comprehensive industrial monitoring and AI-powered analytics platform designed for turbomachinery and asset management. Features real-time monitoring, intelligent alerting, predictive analytics, and an AI assistant (ALI) for operational insights.

🚀 Features

Core Functionality

  • Real-time Monitoring: Live asset health monitoring with interactive dashboards
  • Intelligent Alerting: Smart alert management with AI-powered analysis
  • Predictive Analytics: Machine learning models for asset performance prediction
  • AI Assistant (ALI): Conversational AI for operational insights and assistance

Key Modules

  • Dashboard: Comprehensive overview with key metrics and system health
  • Analytics: Advanced data analysis and trend monitoring
  • Alerts Management: Priority-based alert handling with escalation
  • Fleet Management: Multi-asset monitoring and management
  • Reports: Automated reporting and data export capabilities
  • Settings & Administration: User management and system configuration

Advanced Features

  • TurboPredict Chat: AI-powered conversational interface
  • Self-Healing Systems: Automated issue detection and resolution
  • Document Processing: AI-powered document analysis and insights
  • Real-time Data Integration: Live data streams and PI tag monitoring
  • Mobile-Responsive Design: Full functionality across all devices

🛠️ Technology Stack

  • Frontend: React 18, TypeScript, Tailwind CSS
  • UI Components: shadcn/ui, Radix UI
  • Backend: Supabase (PostgreSQL, Edge Functions, Authentication)
  • Charts & Visualization: Recharts, TradingView Charts
  • State Management: Zustand
  • Routing: React Router DOM
  • Build Tool: Vite

🏗️ Architecture

Frontend Structure

src/
├── components/           # Reusable UI components
│   ├── ui/              # Base UI components (shadcn/ui)
│   ├── dashboard/       # Dashboard-specific components
│   ├── analytics/       # Analytics components
│   └── alerts/          # Alert management components
├── pages/               # Main application pages
├── hooks/               # Custom React hooks
├── stores/              # Zustand state management
├── utils/               # Utility functions
└── integrations/        # External service integrations

Backend (Supabase Edge Functions)

supabase/functions/
├── unified-ai-assistant/     # ALI AI assistant
├── turbo-predict-rag/       # TurboPredict AI chat
├── intelligent-alert-analysis/ # Alert AI analysis
├── self-healing-core/       # Self-healing systems
├── process-document/        # Document processing
└── knowledge-search/        # Knowledge base search

🚀 Getting Started

Prerequisites

  • Node.js 18+ and npm
  • Supabase account (for backend services)

Local Development

  1. Clone the repository

    git clone <repository-url>
    cd turbo-predict-ai
  2. Install dependencies

    npm install
  3. Environment Setup

    • Copy environment variables from Supabase dashboard
    • Configure authentication providers if needed
  4. Start development server

    npm run dev
  5. Access the application

Production Deployment

The application can be deployed using Lovable's built-in deployment or any static hosting service:

  • Lovable: Click "Publish" in the Lovable editor
  • Vercel/Netlify: Connect your GitHub repository
  • Self-hosted: Build with npm run build and serve the dist folder

🤖 AI Features

ALI (AI Assistant)

  • Natural language queries for operational insights
  • Real-time alert analysis and recommendations
  • Historical data analysis and trend identification
  • Intelligent case management and resolution tracking

TurboPredict Chat

  • Conversational AI for turbomachinery insights
  • RAG (Retrieval-Augmented Generation) for knowledge search
  • Document processing and analysis
  • Contextual recommendations based on operational data

Machine Learning

  • Predictive analytics for asset health
  • Anomaly detection algorithms
  • Pattern recognition in operational data
  • Automated model training and deployment

📊 Data Integration

  • PI Tags: Real-time industrial data streaming
  • Document Processing: AI-powered analysis of technical documents
  • Alert Management: Intelligent prioritization and escalation
  • Custom Analytics: Tailored insights for specific use cases

🔐 Security & Authentication

  • Supabase Authentication with Row Level Security (RLS)
  • Role-based access control
  • Secure API endpoints
  • Data encryption and privacy compliance

📱 User Interface

  • Responsive Design: Works seamlessly on desktop, tablet, and mobile
  • Dark/Light Mode: User preference-based theming
  • Interactive Charts: Real-time data visualization
  • Gesture Navigation: Touch-friendly interface controls

🧪 Development

Code Structure

  • TypeScript: Type-safe development
  • Component Architecture: Modular, reusable components
  • Custom Hooks: Shared logic and state management
  • Edge Functions: Serverless backend processing

Testing & Quality

  • ESLint configuration for code quality
  • TypeScript strict mode enabled
  • Component-based architecture for testability

📖 API Documentation

The platform includes several Edge Functions for AI and data processing:

  • /unified-ai-assistant - Main ALI assistant endpoint
  • /turbo-predict-rag - TurboPredict chat functionality
  • /intelligent-alert-analysis - Alert AI analysis
  • /self-healing-core - Automated system healing
  • /process-document - Document processing and analysis

🔧 Configuration

Environment Variables

Configure these in your Supabase dashboard:

  • ANTHROPIC_API_KEY - For Claude AI integration
  • Additional API keys for external services

Supabase Setup

  1. Create a new Supabase project
  2. Run the included migrations
  3. Configure authentication providers
  4. Deploy edge functions

📄 License

MIT License - Open source industrial monitoring platform.

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

  1. Fork the project
  2. Create your feature branch (git checkout -b feature/AmazingFeature)
  3. Commit your changes (git commit -m 'Add some AmazingFeature')
  4. Push to the branch (git push origin feature/AmazingFeature)
  5. Open a Pull Request

📞 Support

For support and questions:

  • Create an issue in this repository
  • Check the documentation for common solutions
  • Review the Edge Function logs for debugging

TurboPredict AI - Intelligent Industrial Monitoring & Analytics Platform

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TurboPredict GUI/RAG system and Agentic AI (Agent ALI) (Applied Logic Integrator)

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