Daan Van Holten

In response to the growing complexity of the global smart infrastructure market, Siemens’ Smart Infrastructure division in Germany launched an initiative to modernize monthly sales forecasting through data-driven methods. With our team of 3, our goal was to replace manual, error-prone processes with an interpretable, machine learning-based solution that improves accuracy, reliability, and business agility. The forecasting system leverages historical sales data and macroeconomic indicators.

Our final solution consolidates fragmented data sources, reduces manual workload, and eliminates subjective judgment.

With a small team of three we build an recommendation system. In order to provide pertinent recommendations based on real purchasing behavior, we created a recommendation system that leverages customer profiles, transaction history, and product metadata.

With a small team of three we developed an AI-powered assistant to support insurance agents in explaining complex financial products, improving client financial literacy, and increasing sales efficiency. We ended up building two versions.

1. Prototype a high-performing Custom GPT with a strong UX 2. Develop a second solution using API-accessible tools, allowing full system integration.