MALENIO Project Documentation

Project Overview

MALENIO is a Chrome extension designed to revolutionize Gmail management and discovery patterns using advanced AI for auto-tagging emails. The solution leverages local AI models through Ollama, ensuring privacy and performance while delivering intelligent email organization capabilities.

Vision Statement

MALENIO aims to transform how users interact with their Gmail inbox by providing AI-powered organization that respects privacy above all else. We believe users shouldn’t have to choose between powerful AI assistance and keeping their email data private.

Unique Value Proposition

MALENIO stands apart as the first Gmail enhancement tool that delivers sophisticated AI email management capabilities without sending any user data to the cloud. By processing all data locally through Ollama, MALENIO offers:

• Complete privacy protection - your emails never leave your device
• Intelligent organization without privacy trade-offs
• Personalized email management that adapts to your unique workflow
• Transparent AI decision-making under user control

Key Features

MALENIO delivers a comprehensive set of features designed to enhance Gmail productivity while maintaining strict privacy standards:

1. Intelligent Auto-Tagging

• AI-suggested tags based on email content and context
• Learning from user tagging behavior
• Consistent taxonomy enforcement
• Zero cloud data transmission

2. Pattern Recognition

• Identification of communication patterns across conversations
• Highlighting of important trends in your inbox
• Relationship mapping between senders, topics, and projects
• Visual analytics of email communication patterns

3. Local AI Processing

• Integration with Ollama for local model inference
• No email content sent to external services
• Model selection based on user hardware capabilities
• Works offline once models are downloaded

4. Gmail Integration

• Native-feeling UI extensions
• Keyboard shortcuts and workflow enhancements
• Gmail search integration with AI-generated tags
• Minimal performance impact on Gmail experience

5. Privacy Controls

• Granular control over AI processing
• Transparent explanations for AI decisions
• User override capabilities for all automated actions
• Privacy-first design philosophy

Documentation Index

Research and Definition

• Market Research: Analysis of email management solutions, AI-powered email tools, and local AI model trends
• Project Definition: Core definitions, scope, and objectives for MALENIO
• User Research: Understanding Gmail user needs, pain points, and organizational behaviors, including comprehensive competitive analysis
• Problem Definition: Detailed analysis of email management challenges addressed by MALENIO

Technical Documentation (Planned)

• Technical Architecture: System design, components, and data flow
• Implementation Guide: Development specifications and guidelines
• API Documentation: Integration points and data structures
• Security Model: Privacy protections and data handling specifications

User Documentation (Planned)

• Installation Guide: Step-by-step setup instructions
• User Manual: Feature descriptions and usage instructions
• FAQ: Common questions and troubleshooting
• Privacy Guide: Detailed explanation of privacy approach

Project Roadmap

Architecture Overview

MALENIO follows a privacy-first architecture where all AI processing occurs locally on the user’s machine:

┌─────────────────────────────────────────────────────┐
│                  User's Local Machine                │
│                                                     │
│  ┌─────────────┐    ┌────────────┐    ┌──────────┐  │
│  │             │    │            │    │          │  │
│  │  Chrome +   │◄──►│  MALENIO   │◄──►│  Ollama  │  │
│  │   Gmail     │    │ Extension  │    │          │  │
│  │             │    │            │    │          │  │
│  └─────────────┘    └────────────┘    └──────────┘  │
│                                                     │
└─────────────────────────────────────────────────────┘
             │                  ▲
             │                  │
             ▼                  │
     ┌───────────────┐   ┌─────────────┐
     │ Gmail API     │   │ Model       │
     │ (Mail access) │   │ Downloads   │
     └───────────────┘   └─────────────┘

For the detailed architecture documentation, see Technical Architecture (coming soon).

Technology Stack

MALENIO is built using the following key technologies:

Core Technologies

• Chrome Extension Framework: For Gmail integration and UI
• Ollama: For local AI model execution
• Gmail API: For accessing and managing Gmail data
• IndexedDB: For local data storage
• Open-source LLMs: For email analysis and pattern recognition

Development Stack

• JavaScript/TypeScript: Primary programming languages
• React: For UI components
• RESTful architecture: For Gmail API interaction
• Jest: For testing
• Webpack: For bundling

Security & Privacy

• End-to-end encryption: For sensitive data
• Zero-knowledge design: No data leaves user’s machine
• Local processing only: All AI inference runs on user’s device

Project Status

Current Phase: Research and Definition

Progress:

• ✅ Market research completed
• ✅ Initial user research completed, including competitive analysis
• ✅ Problem definition documented
• ✅ Project scope and objectives defined
• 🔄 User personas under development
• 🔄 Detailed feature requirements in progress
• ⬜ Technical architecture planning to begin
• ⬜ UI/UX design planning to begin

Last Updated: 2024-06-05

Team

Documentation Conventions

This documentation follows the PAELLADOC system conventions:

• All dates use YYYY-MM-DD format
• File names use snake_case
• References use APA format
• Security levels are marked in document metadata
• Architecture diagrams use ASCII art for documentation and SVG for presentations

References and Resources

• Ollama Documentation
• Chrome Extension Developer Guide
• Gmail API Documentation
• GDPR Compliance Guidelines

Contact Information

Project Owner: [Your Name] Lead Developer: [Developer Name] Email: [Contact Email] GitHub: [Repository Link]