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Customer attrition, or churn, is a critical operational challenge for modern digital businesses, directly impacting revenue, growth, and market share. While numerous machine learning models have been proposed to identify at-risk customers, they often lack the necessary components for real-time production inference and fail to provide actionable, interpretable insights for stakeholders. To address these critical gaps, we propose ChurnGuard AI, a comprehensive production-grade customer intelligence system. Our approach introduces a highly scalable architecture centered around a LightGBM classification engine, chosen for its exceptional accuracy and computational efficiency in handling complex, high-dimensional tabular data. Further-more, we integrate SHapley Additive exPlanations (SHAP) to transform ’black-box’ predictions into transparent, quantifiable feature importances, enabling targeted retention strategies. The system implementation features a high- performance FastAPI back-end that achieves sub-100ms inference latency, paired with a modern glassmorphism-based web interface for seamless stake-holder interaction. Extensive experimental evaluations demon-strate that the proposed LightGBM model outperforms baseline algorithms—achieving an outstanding 97.6% accuracy—while seamlessly balancing precision and recall. Ultimately, Churn-Guard AI bridges the gap between theoretical predictive capabil-ity and real-world operational utility, offering a robust blueprint for real-time customer lifecycle management