🏭 VYNO Process Guard

An enterprise-grade industrial AI monitoring platform built for real-time factory telemetry analysis, anomaly detection, and predictive operational intelligence.

VYNO Process Guard combines modern cloud infrastructure with AI-powered reasoning systems to help industrial organizations detect mechanical risks before failures occur and generate actionable engineering recommendations in real time.

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✨ Features

• 🏭 Real-time industrial telemetry monitoring
• ⚡ Sub-second live data streaming
• 🤖 AI-powered anomaly detection using Llama 3.1
• 🧠 Prescriptive engineering recommendations
• 🔐 Multi-tenant enterprise architecture
• 🛡 Row Level Security (RLS) for data isolation
• 📊 Real-time operational dashboards
• 🔄 WebSocket-powered live updates
• 📜 Immutable AI audit trail logging
• 🇪🇺 EU AI Act-ready compliance infrastructure

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🛠️ Tech Stack

• Frontend: Next.js 14 (App Router)
• Styling: Tailwind CSS
• Charts & Visualization: Recharts
• Backend: FastAPI (Python)
• AI Engine: Llama-3.1 via Groq API
• Database: PostgreSQL
• Realtime Infrastructure: Supabase Realtime
• Authentication: Supabase Auth
• Security Layer: PostgreSQL Row Level Security (RLS)

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📦 Installation

Clone the repository and install dependencies:

git clone https://github.com/yourusername/vyno-process-guard.git

cd vyno-process-guard

Install frontend dependencies:

npm install

Install backend dependencies:

pip install -r requirements.txt

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⚙️ Environment Setup

Create a .env.local file for the frontend:

NEXT_PUBLIC_SUPABASE_URL=your_supabase_url
NEXT_PUBLIC_SUPABASE_ANON_KEY=your_supabase_anon_key
NEXT_PUBLIC_API_URL=http://localhost:8000

Create a .env file for the backend:

GROQ_API_KEY=your_groq_api_key
SUPABASE_SERVICE_ROLE_KEY=your_service_role_key
DATABASE_URL=your_database_url

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▶️ Running the Platform

Start Frontend

npm run dev

Start Backend

uvicorn main:app --reload

Frontend will run on:

http://localhost:3000

Backend API will run on:

http://localhost:8000

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📁 Project Structure

vyno-process-guard/
│
├── frontend/
│   ├── app/
│   ├── components/
│   ├── dashboard/
│   ├── charts/
│   └── services/
│
├── backend/
│   ├── api/
│   ├── ai/
│   ├── telemetry/
│   ├── monitoring/
│   └── security/
│
├── database/
├── docs/
├── requirements.txt
└── README.md

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🚀 Core Capabilities

🤖 Predictive AI Monitoring

The platform continuously analyzes telemetry streams using Llama-3.1 to identify abnormal mechanical patterns and operational risks before failures occur.

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📡 Real-Time Telemetry Infrastructure

Powered by Supabase Realtime and WebSockets for sub-second telemetry synchronization across industrial dashboards.

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🛡 Enterprise Security Architecture

Every telemetry packet is securely associated with a unique organization and verified using PostgreSQL Row Level Security policies.

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📜 Immutable AI Audit Logging

All AI-generated recommendations are hashed and stored to provide transparent decision tracking and compliance-ready auditability.

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🔐 Security & Compliance

VYNO Process Guard is designed with enterprise-grade security principles:

• Row Level Security (RLS)
• Multi-tenant isolation
• Secure API communication
• Immutable audit logging
• Tenant-scoped telemetry streams
• EU AI Act-oriented traceability architecture

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📊 Use Cases

• Smart Factory Monitoring
• Predictive Maintenance
• Industrial Risk Detection
• Manufacturing Intelligence
• Equipment Failure Prevention
• Real-Time Operational Analytics

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🚀 Deployment

Frontend Deployment

Recommended platform:

• Vercel

Build the frontend:

npm run build

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Backend Deployment

Recommended platforms:

• Railway
• Render
• Docker
• Kubernetes

Run production server:

uvicorn main:app --host 0.0.0.0 --port 8000

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📚 Learn More

Useful resources related to the technologies used in this project:

• Next.js Documentation
• FastAPI Documentation
• Supabase Documentation
• Groq API Documentation
• PostgreSQL RLS Documentation

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📄 License

This project is licensed under the MIT License.

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🤝 Contributing

Contributions, ideas, and improvements are welcome.

Feel free to fork the project and submit pull requests.

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💡 Author

Built for modern industrial intelligence systems using AI-powered monitoring infrastructure.

Eray Özer