Churn prediction model for business customers that identified potential churners with higher precision than the client’s existing model, achieving a 15-20% improvement in accuracy.

Real-time retail insights achieved through computer vision on edge devices, enabling person and face detection, demographic profiling, and pose estimation.

The existing approach relied on a static Excel workbook that was limited in flexibility, accessibility, and scalability.