How to Build an AI-Powered Email Priority Classifier

Simple Summary

An intelligent email priority classifier that uses machine learning to automatically categorize and prioritize incoming emails, helping users manage their inbox more efficiently.

Product Requirements Document (PRD)

Goals:

1. Create an intelligent email classification system
2. Develop a user-friendly interface for managing prioritized emails
3. Ensure scalability and security of the application

Target Audience:

• Professionals and busy individuals with high email volume
• Organizations looking to improve email management efficiency

Key Features:

1. AI-powered email priority classification
2. Customizable classification rules
3. User-friendly dashboard for email management
4. Integration with popular email providers
5. Mobile app for on-the-go email prioritization
6. Analytics and reporting on email patterns

User Requirements:

1. Easy account creation and email integration
2. Intuitive interface for viewing and managing prioritized emails
3. Ability to customize classification rules
4. Secure handling of email data
5. Cross-platform accessibility (web and mobile)

User Flows

1. User Registration and Email Integration:

   • User signs up for an account
   • User connects their email account(s)
   • System performs initial email analysis and classification

2. Email Priority Management:

   • User receives new email
   • System automatically classifies and prioritizes the email
   • User views prioritized emails in the dashboard
   • User can manually adjust priority if needed

3. Classification Rule Customization:

   • User navigates to settings
   • User creates or modifies classification rules
   • System applies updated rules to incoming and existing emails

Technical Specifications

Frontend:

• React for web application
• React Native for mobile app
• Redux for state management
• Material-UI for consistent design

Backend:

• Node.js with Express.js
• PostgreSQL for relational data storage
• Redis for caching
• Docker for containerization

AI/ML:

• TensorFlow or scikit-learn for machine learning models
• Natural Language Processing (NLP) libraries for text analysis

Authentication:

• JWT for secure authentication
• OAuth2 for email provider integration

API:

• RESTful API design
• GraphQL for complex data queries

API Endpoints

1. /api/auth

   • POST /register
   • POST /login
   • POST /logout

2. /api/emails

   • GET /list
   • GET /:id
   • PATCH /:id/priority

3. /api/classification

   • GET /rules
   • POST /rules
   • PUT /rules/:id
   • DELETE /rules/:id

4. /api/settings

   • GET /
   • PUT /

5. /api/analytics

   • GET /summary
   • GET /trends

Database Schema

1. Users

   • id (PK)
   • email
   • password_hash
   • created_at
   • updated_at

2. EmailAccounts

   • id (PK)
   • user_id (FK)
   • provider
   • access_token
   • refresh_token
   • created_at
   • updated_at

3. Emails

   • id (PK)
   • user_id (FK)
   • email_account_id (FK)
   • subject
   • sender
   • received_at
   • priority
   • classification
   • content_summary

4. ClassificationRules

   • id (PK)
   • user_id (FK)
   • rule_type
   • criteria
   • priority
   • created_at
   • updated_at

5. Analytics

   • id (PK)
   • user_id (FK)
   • date
   • total_emails
   • high_priority
   • medium_priority
   • low_priority

File Structure

Implementation Plan

1. Project Setup (1-2 days)

   • Initialize repository and project structure
   • Set up development environment and tools

2. Backend Development (2-3 weeks)

   • Implement user authentication and authorization
   • Develop email integration services
   • Create API endpoints for email management
   • Implement database models and migrations

3. AI/ML Development (3-4 weeks)

   • Develop email classification algorithm
   • Train and test the machine learning model
   • Integrate the model with the backend services

4. Frontend Development (3-4 weeks)

   • Create user interface components
   • Implement email dashboard and management features
   • Develop settings and customization interfaces

5. Mobile App Development (2-3 weeks)

   • Port core features to React Native
   • Optimize for mobile user experience

6. Integration and Testing (2 weeks)

   • Integrate frontend, backend, and ML components
   • Perform thorough testing (unit, integration, end-to-end)

7. Security and Performance Optimization (1 week)

   • Conduct security audits and implement necessary measures
   • Optimize application performance

8. Deployment Preparation (3-5 days)

   • Set up production environment
   • Configure CI/CD pipeline

9. Launch and Initial Monitoring (1 week)

   • Deploy to production
   • Monitor system performance and user feedback

Deployment Strategy

1. Use containerization (Docker) for consistent deployments
2. Deploy backend services to a scalable cloud platform (e.g., AWS ECS or Google Cloud Run)
3. Use a managed database service (e.g., AWS RDS for PostgreSQL)
4. Deploy frontend to a CDN-enabled static hosting service (e.g., AWS S3 with CloudFront)
5. Implement a CI/CD pipeline using GitHub Actions or GitLab CI
6. Use Infrastructure as Code (e.g., Terraform) for managing cloud resources
7. Set up monitoring and logging (e.g., ELK stack or cloud-native solutions)
8. Implement automated backups and disaster recovery procedures
9. Use a staged deployment approach: development, staging, and production environments

Design Rationale

1. React and React Native chosen for cross-platform development efficiency
2. Node.js backend for JavaScript ecosystem consistency and high performance
3. PostgreSQL selected for its robustness in handling complex data relationships
4. AI/ML components designed to be modular for easy updates and improvements
5. RESTful API with GraphQL option for flexibility in data querying
6. Containerization and cloud deployment for scalability and ease of management
7. Emphasis on security measures due to the sensitive nature of email data
8. Mobile-first design approach to cater to on-the-go professionals
9. Customizable classification rules to adapt to individual user needs and preferences