🛡️ Intelligent Compliance Assistant

An AI-powered compliance automation system that analyzes regulatory documents and data to help organizations ensure policy and regulatory adherence. Built with a modern microservice architecture, scalable backend APIs, and containerized deployment.

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📋 Table of Contents

• Overview
• Key Features
• Architecture
• Tech Stack
• Project Structure
• Getting Started
• API Documentation
• Future Roadmap
• Contributing
• Author

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🎯 Overview

The Intelligent Compliance Assistant automates the complex process of regulatory compliance checking by:

• Automating compliance checks using AI-driven logic and rules engines
• Processing both structured and unstructured regulatory data from multiple sources
• Providing scalable REST APIs for seamless integration with existing systems
• Ensuring reliable deployment through Docker containerization

Perfect for: Financial institutions, healthcare organizations, legal firms, and any enterprise dealing with complex regulatory frameworks.

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✨ Key Features

🤖 AI-Powered Analysis

• Intelligent document parsing and classification
• Automated policy violation detection
• Natural language processing for regulatory text
• Extensible architecture for ML/NLP enhancements

🔧 Robust Backend

• RESTful API design following industry best practices
• Modular microservice architecture for horizontal scaling
• Dual database support (SQL + NoSQL) for optimal data handling
• Comprehensive error handling and logging

🎨 User-Friendly Frontend

• Intuitive React-based dashboard
• Real-time compliance status monitoring
• Interactive data visualization
• Responsive design for desktop and mobile

🐳 DevOps Ready

• Fully containerized with Docker
• Environment-agnostic deployment
• Easy scaling and orchestration
• Production-ready configurations

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🏗️ Architecture

┌─────────────────────────────────────────────────────────┐
│                     Frontend Layer                       │
│                   (React.js Dashboard)                   │
└────────────────────┬────────────────────────────────────┘
                     │ HTTP/REST
┌────────────────────▼────────────────────────────────────┐
│                   API Gateway Layer                      │
│              (Spring Boot Backend APIs)                  │
└────────────────────┬────────────────────────────────────┘
                     │ Internal API
┌────────────────────▼────────────────────────────────────┐
│                AI/ML Service Layer                       │
│        (Python-based Compliance Intelligence)            │
└────────────┬───────────────────────┬────────────────────┘
             │                       │
┌────────────▼────────┐   ┌─────────▼──────────┐
│   PostgreSQL DB     │   │    MongoDB         │
│  (Structured Data)  │   │  (Document Store)  │
└─────────────────────┘   └────────────────────┘
             │                       │
             └───────────┬───────────┘
                         │
                ┌────────▼─────────┐
                │  Docker Engine   │
                │  (Orchestration) │
                └──────────────────┘

Architecture Highlights

• Frontend: React.js with modern UI/UX patterns
• API Layer: Spring Boot microservices with RESTful endpoints
• AI Engine: Python-based intelligent processing (ready for LLM integration)
• Data Storage: Hybrid approach using PostgreSQL (relational) + MongoDB (documents)
• Deployment: Docker containerization for consistent environments

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⚙️ Tech Stack

Layer	Technologies
Frontend	React.js, JavaScript, CSS3, HTML5
Backend	Spring Boot (Java), RESTful APIs
AI/ML	Python 3.x, NumPy, Pandas
Database	PostgreSQL, MongoDB
DevOps	Docker, Docker Compose
Version Control	Git, GitHub

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📂 Project Structure

intelligent-compliance-assistant/
│
├── frontend/                 # React.js dashboard application
│   ├── src/
│   ├── public/
│   └── package.json
│
├── backend/                  # Spring Boot REST APIs
│   ├── src/
│   ├── pom.xml
│   └── application.properties
│
├── ai-service/              # Python AI/ML services
│   ├── models/              # ML models and algorithms
│   ├── services/            # Business logic
│   ├── utils/               # Helper functions
│   └── requirements.txt
│
├── data/                    # Sample datasets and documents
│   ├── regulations/         # Regulatory documents
│   └── test-cases/          # Test compliance scenarios
│
├── docker/                  # Docker configurations
│   ├── docker-compose.yml
│   ├── Dockerfile.backend
│   ├── Dockerfile.frontend
│   └── Dockerfile.ai
│
├── .gitignore
└── README.md

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🚀 Getting Started

Prerequisites

• Docker (version 20.10+)
• Docker Compose (version 1.29+)
• Node.js (version 16+ for local development)
• Java 11+ (for backend development)
• Python 3.9+ (for AI service development)

Quick Start with Docker

1. Clone the repository

   git clone https://github.com/M1325-source/intelligent-compliance-assistant.git
   cd intelligent-compliance-assistant

2. Build and run with Docker Compose

   cd docker
   docker-compose up --build

3. Access the application

   • Frontend: http://localhost:3000
   • Backend API: http://localhost:8080
   • AI Service: http://localhost:5000

Local Development Setup

Backend (Spring Boot)

cd backend
./mvnw spring-boot:run

Frontend (React)

cd frontend
npm install
npm start

AI Service (Python)

cd ai-service
pip install -r requirements.txt
python app.py

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📚 API Documentation

Core Endpoints

Compliance Analysis

POST /api/v1/compliance/analyze
Content-Type: application/json

{
  "documentId": "string",
  "regulationType": "string",
  "content": "string"
}

Document Upload

POST /api/v1/documents/upload
Content-Type: multipart/form-data

file: <binary>

Compliance Report

GET /api/v1/compliance/report/{documentId}

Full API documentation available at: http://localhost:8080/swagger-ui.html

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🔮 Future Roadmap

Phase 1 (Q2 2026)

• LLM integration for advanced regulatory reasoning (RAG architecture)
• Real-time compliance monitoring dashboard
• Multi-language support for international regulations

Phase 2 (Q3 2026)

• CI/CD pipeline with GitHub Actions
• Role-based access control (RBAC)
• Audit trail and compliance history tracking

Phase 3 (Q4 2026)

• Cloud deployment (AWS/GCP/Azure)
• Integration with popular compliance tools
• Advanced analytics and reporting features
• Mobile application support

Long-term Vision

• AI-powered regulatory change detection
• Predictive compliance risk assessment
• Industry-specific compliance templates
• Enterprise SSO integration

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🤝 Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a 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

Please ensure your code follows the project's coding standards and includes appropriate tests.

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📝 License

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

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👩‍💻 Author

Manisha Priya
Fullstack Developer | DevOps | AI/ML engineer | Cloud Architect

Passionate about building production-ready, scalable systems that solve real-world problems.

• 📧 Email: your-email@example.com
• 🐙 GitHub: @M1325-source

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🙏 Acknowledgments

• Thanks to all contributors who have helped shape this project
• Inspired by the need for automated compliance in modern enterprises
• Built with modern open-source technologies

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📞 Support

For support, please:

• Open an issue on GitHub
• Contact the maintainer via email
• Check the Wiki for documentation

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⭐ If you find this project useful, please consider giving it a star!

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Last Updated: February 2026