MAIF/melusine

What it solves

Melusine simplifies the complex process of qualifying and routing emails. It helps organizations automate the sorting of emails by identifying urgent messages, extracting key summaries, filtering out noise, and ensuring emails are routed to the correct destination.

How it works

It provides a pipeline framework that allows users to combine deep learning models (from libraries like HuggingFace, PyTorch, and TensorFlow) with deterministic rules such as regular expressions and keywords. The library includes specialized tools to segment email conversations into individual messages and tag specific parts of an email, such as signatures and footers, to improve the accuracy of downstream machine learning models.

Who it’s for

It is designed for developers and data scientists building production-ready email processing workflows who need to integrate AI models with traditional rule-based logic.

Highlights

• Pre-packaged Email Tools: Built-in capabilities for segmenting conversations and tagging message components (bodies, signatures, footers).
• Hybrid Logic: Supports both AI frameworks and deterministic heuristics in a single workflow.
• Production Ready: Developed and proven within the MAIF production environment.
• Execution Management: Handles boilerplate code, including pipeline execution, parallelization, and debug modes.