About

It ingests raw logs, stores them safely, engineers statistical features, applies machine-learning-based anomaly detection, and produces actionable alerts with measurable performance metrics.

The system is designed to work reliably in ephemeral environments such as Zerve, supports both demo and production modes, and never fails silently.