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.

SemantiClean addresses a critical tension in modern machine learning: the trade-off between prediction accuracy and explainability. Rather than pursuing marginal gains through opaque end-to-end models, the framework deliberately constrains itself to achieve deterministic, auditable outputs—a design philosophy increasingly demanded by regulatory bodies and enterprise customers. This approach reflects broader industry recognition that trust and defensibility often outweigh fractional improvements in model performance.

The framework's architecture demonstrates sophisticated engineering around governance. By organizing behavioral elements into a four-layer hierarchy (Functional, Interaction, Systemic, Contextual) and implementing anti-inflation mechanisms like RedundancyGroup caps and TieredPenaltyCalculator penalties, SemantiClean prevents signal drift and maintains interpretability at scale. The explicit acknowledgment that gender inference remains non-functional shows responsible development practices—refusing to deploy unreliable components rather than masking failures.

For enterprises in regulated sectors, this framework solves a material problem: how to deploy AI systems that satisfy both performance requirements and compliance obligations. E-commerce platforms, particularly those handling sensitive consumer data, increasingly face scrutiny around algorithmic decision-making. SemantiClean's emphasis on element-level transparency and reproducible decision trails directly addresses these compliance pressures.

The integration of LLM-driven inference while maintaining deterministic outputs for non-LLM components represents pragmatic technology choices. By isolating variability to specific LLM-dependent elements (E8, E10) while preserving reproducibility elsewhere, the framework enables organizations to harness language models without sacrificing auditability—a model other AI systems may increasingly adopt.

• →SemantiClean prioritizes auditability and reproducibility over raw prediction accuracy, reflecting enterprise demand for explainable AI.
• →The framework organizes behavioral signals into a four-layer architecture with anti-inflation mechanisms to prevent signal drift and maintain interpretability.
• →Explicit governance-focused design enables compliance with regulatory requirements while maintaining practical utility for e-commerce applications.
• →Hybrid LLM integration isolates variability to specific components while preserving deterministic outputs elsewhere, balancing innovation with reproducibility.
• →The approach represents a broader industry shift toward transparent, defensible AI systems over opaque black-box models.