Asthra MailGuard: A Privacy-First Hybrid AI Email Assistant Using ML and Local LLMs

In the era of digital communication, the volume and complexity of email traffic continue to rise, leading to challenges in managing, filtering, and responding effectively to diverse messages. While traditional spam filters offer basic classification, they lack personalization, contextual understanding, and offline privacy guarantees. This paper presents Asthra MailGuard, a privacy-first, AI-powered command-line email assistant that classifies, filters, and responds to emails using a hybrid architecture—combining a lightweight Logistic Regression model with a fallback to Hermes 3, a powerful local Large Language Model (LLM) via Ollama. The system introduces multiple innovations including a Safe Mode for privacy enforcement, domain-based rules, a self-learning feedback loop, and personalized response drafting guided by user tone. With support for offline execution, Asthra MailGuard ensures total data control while offering intelligent predictions and response generation. Evaluated on real-world email samples, it achieves improved accuracy and usability, highlighting its potential as a reliable, scalable solution for email management in both personal and professional environments.