Project Overview
A fully automated system that retrieves emails, identifies their intent using AI, and generates
accurate context-aware replies without manual intervention.
- Emails came in diverse and unstructured formats, making consistent intent detection
difficult.
- Some messages were ambiguous, incomplete, or poorly formatted, requiring careful
fallback handling.
- A continuous background workflow was needed to process new emails efficiently
without performance bottlenecks.
Challenges
- Emails came in diverse and unstructured formats, making consistent intent detection
difficult.
- Some messages were ambiguous, incomplete, or poorly formatted, requiring careful
fallback handling.
- A continuous background workflow was needed to process new emails efficiently
without performance bottlenecks.
Solutions
-
Email Parsing & Structured Storage:
Implemented automated retrieval of emails via IMAP, parsed key fields (sender, subject, body),
and stored them in a structured relational database to ensure traceability and analytics support.
-
AI-Based Intent Detection:
Integrated ChatGPT APIs to analyze message content, detect the sender’s purpose, and
accurately classify intents across varied email types.
-
Automated Response Generation:
Created context-aware reply templates paired with ChatGPT to generate personalized,
meaningful, and professional responses for each detected intent type.
-
Continuous Background Worker:
Used Celery (or a similar task queue) to monitor incoming emails and handle processing
in real time, ensuring smooth performance and scalability.
Technology Used
-
Programming Language:
Python
-
AI / NLP:
ChatGPT APIs
-
Database:
PostgreSQL / MySQL
-
Email Protocols:
IMAP / SMTP
-
Task Queue / Worker:
Celery
-
Deployment:
Docker
