Inspiration
Two days ago, as we were standing in the long line to check-in to Calhacks, we were competing to see who had the most emails in their inbox. Surprisingly enough someone had more than 150k unread emails! As fate would have it when it was our turn to check-in we were franticly searching through our inboxes for our tickets. That's when we realized there are so many ways e-mail can be improved on to make life easier, one major one being organization of emails. But, no one in their right mind is going to manually categorize and organize more than 150k e-mails. That's where MailMorph comes in!
What it does
MailMorph is a smart e-mail tool which employs the use of AI and AI-Agents to make life with e-mail easier in five main areas. First, MailMorph can automatically create user specified categories and organize the e-mail in their inbox into the categories. Second, MailMorph can automatically identify mails sitting in the sent folder of you inbox and recommend you to follow up with the other person, and automatically create a draft email for you. Third, MailMorph can automatically identify mails coming into your inbox with important events or dates and automatically add it to your calendar. Fourth, MailMorph can create a daily digest of your emails of that day and summarize it all for you, as well as highlighting the most important details. Last but not least, MailMorph can identify emails with pdf files which need to be filled, and auto fill them for you if there is sufficient information in the email itself.
How we built it
For MailMorph, we combined the capabilities of Gemini AI and AutoGen AI Agents to build a powerful email and PDF automation solution. To integrate with Gmail and Google Calendar, we secured access to their APIs, set up user authentication, and configured credentials to extract the necessary data. Depending on the use case, we retrieved key information: for categorizing emails, we identified relevant details; for adding events to calendars, we extracted dates and times; and for summaries, we collected the full content. We ensured PII redaction before sending any data to the LLMs and used prompt engineering with Gemini AI to generate accurate outputs. On the backend, we structured individual APIs to handle different functionalities, allowing the frontend—built with React, Node.js, and Swift—to communicate seamlessly with the Python backend through API calls. For PDF automation, we leveraged AutoGen AI to process both fillable and scanned PDFs. With fillable PDFs, the agent identified fields and matched them with data from a JSON dataset before generating code to fill the PDF. For scanned PDFs, we incorporated OCR to detect fields and injected fillable inputs at the appropriate locations, following the same logic as the fillable PDFs. This architecture enabled us to efficiently manage emails, automate PDFs, and integrate everything into a smooth, user-friendly interface.
Challenges we ran into
One of the biggest challenges was setting up access to the Gmail API and Google Calendar API, as it involved navigating OAuth authentication and configuring user credentials securely. Another hurdle was ensuring PII redaction before sending any sensitive information to the LLMs, which required careful preprocessing to avoid leaking personal data. Implementing OCR for scanned PDFs was also complex, especially when accurately detecting fields and injecting fillable elements into the correct locations. Additionally, integrating multiple technologies—React, Node.js, Swift, and Python—into a seamless frontend-backend system took considerable effort to ensure smooth communication between all components.
Accomplishments that we're proud of
We are proud of the fact that we got most of our features to perform in less than one second. This is huge for users with very large inboxes since they won't have to wait copious amounts of time. Also, it was our first time integrating flask API endpoints with node.js front end so figuring out how the endpoints worked and integrating everything and seeing it all come together was definitely something we were proud about,
What we learned
Throughout the development of MailMorph, we learned the importance of efficient API integration and data security when working with sensitive user information. We also gained hands-on experience in prompt engineering for LLMs to optimize outputs based on different tasks. Another key takeaway was learning how to leverage AI agents to automate complex tasks like processing PDFs and identifying contextual information in emails. Furthermore, working with multiple programming languages and frameworks helped us improve our collaboration and technical integration skills.
What's next for MailMorph
For now MailMorph is limit to Gmail, so in the future we look forward to implementing in outlook, yahoo, and other email services. Also, we look forward to adding more functionalities such as auto flight check-in, better spam detection, voice activated email, etc. Also, we look forward to implementing a mobile application to make an even more convenient way to organize e-mail.
Log in or sign up for Devpost to join the conversation.