From Explicit Elements to Implicit Intent: A Predefined Library for Auditable Behavioral Inference
SemantiClean is a modular framework that extracts semantic signals from e-commerce session data to predict purchase intent and customer behavior while prioritizing auditability and reproducibility over raw predictive accuracy. The system uses a predefined library of 24 behavioral elements organized across four layers and implements safeguards against signal inflation, representing a shift toward transparent, governance-focused AI systems over conventional black-box optimizers.