This is a submission for the Nylas Challenge: AI Expedition.

What I Built and Why

In this project, I developed an AI-Driven Email Categorizer with Priority Detection. The goal was to create a tool that automates the categorization of incoming emails and detects their priority. With an ever-growing volume of emails, distinguishing between work, personal, and urgent messages can be time-consuming and overwhelming. This tool aims to streamline email management by leveraging AI to categorize and prioritize emails, ensuring that users can focus on what matters most.

Demo

Code

Journey

Leveraging Nylas

Nylas provided the foundation for email retrieval and management. Using the Nylas Email API, I was able to:

• Retrieve Emails: I integrated the Nylas API to fetch recent emails from a user's inbox.
• Extract Snippets: For categorization, I utilized email snippets, which are concise summaries of email content.

This integration was crucial for ensuring that the tool could access and process email data in real time.

Integration with AI Models

To categorize emails and detect their priority, I incorporated two AI models:

1. OpenAI: I used OpenAI's gpt-3.5-turbo-instruct model to classify emails into categories such as work, personal, and urgent. This model’s advanced natural language understanding capabilities helped in generating accurate categorizations based on the content of the emails.

2. Google Gemini: I also integrated Google’s Gemini model (gemini-1.5-flash) to provide an alternative approach for email categorization. This model helped in validating the categorization results and offered different insights into email priority detection.

What I Learned

• API Integration: I gained significant experience in working with email APIs and integrating them with AI models. This involved handling various data formats and ensuring smooth communication between the components.
• AI Model Utilization: I learned about the nuances of using different AI models for natural language processing and categorization tasks. Adapting to the requirements of different models, such as handling specific message roles, was a key part of the development process.
• Error Handling and Optimization: Managing errors and optimizing the integration of APIs and AI models was an essential part of ensuring the tool’s reliability and efficiency.

What I’m Most Proud Of

• Seamless Integration: Successfully integrating Nylas with multiple AI models to create a cohesive tool for email categorization and priority detection was a significant achievement.
• User Experience: The tool provides a streamlined experience for users, allowing them to focus on high-priority emails without getting bogged down by less important messages.
• Scalability: The design of the tool allows for easy expansion and adaptation to additional AI models or email providers in the future.

This project showcases how combining modern email APIs with advanced AI models can revolutionize email management, making it more efficient and user-friendly.