Viktor

Vester

Vester Project AI • Self-employed

Oct 2025 - Dec 2025 • 2 mos

Vester is an AI-powered churn prediction and customer analytics platform designed to help businesses identify at-risk users, reduce customer loss, and maximize long-term revenue. The system transforms raw behavioral and transactional data into actionable business insights through advanced machine learning and scalable backend architecture. The platform is built using FastAPI for high-performance API services, containerized with Docker for reproducible deployment, and designed as a modular microservice system. This architecture enables seamless integration into existing business environments, including CRM systems, analytics dashboards, and customer engagement platforms. Vester supports both real-time inference and batch processing, allowing companies to generate churn predictions at scale with low latency. At the core of Vester is a robust machine learning pipeline that performs data preprocessing, feature engineering, model training, and real-time prediction serving. The modeling approach emphasizes both predictive accuracy and explainability, allowing businesses to understand the drivers behind churn risk rather than relying on black-box predictions. This enables more effective retention strategies and targeted engagement campaigns. The platform provides churn probability scoring, customer risk segmentation, and interpretable feature importance metrics. These outputs allow decision-makers to prioritize high-risk customers, optimize marketing efforts, and proactively prevent revenue loss. Vester is optimized for performance, stability, and scalability, making it suitable for startups, mid-sized companies, and enterprise environments. Vester demonstrates full-cycle AI product development, combining machine learning engineering, backend system design, deployment automation, and business-driven analytics. It serves as a production-ready solution for predictive customer intelligence, delivering measurable impact on retention, customer lifetime value, and operationa