Background:
The introduction to the topic of waste collection happened rather by chance, at a hackathon focused on digital optimisation. It quickly became clear that inefficient collection systems are not a marginal problem, but a structural one. Further investigation revealed why traditional sensor solutions have reached their economic and operational limits and why the problem remains unsolved to this day. This led to the decision to abandon my PhD and focus fully on developing a practical, scalable solution.

What drives me:
Using technology when it makes a real difference. When drivers and dispatchers are noticeably relieved, processes run more smoothly and, at the same time, there are measurable environmental benefits, software becomes a real step forward.

Background:
The focus was early on complex technical systems, optimisation and scalable software. After studying business informatics at the Karlsruhe Institute of Technology, work on various start-up and research projects led him deep into the world of machine learning, optimisation and production-oriented software development. The combination of technical depth and operational feasibility continues to shape his approach today. The decision to join zebrafant arose from the conviction that this is precisely where technology can make a real difference.

What drives me:
Solving complex problems in a way that really works in everyday life. When advanced algorithms are not an end in themselves, but enable stable, scalable systems that relieve people and measurably improve processes, sustainable value is created.

Background:
Many years of experience in developing robust, scalable software systems with a focus on web technologies, cloud infrastructure and data-intensive applications. Work in a wide variety of product environments, from start-ups to international technology companies. Technical depth in backend, frontend and cloud architectures forms the basis for keeping complex systems running reliably. As Lead Software Engineer at zebrafant, Mo is responsible for technical stability in the background and ensures that the software can be used directly in everyday life without training.

What drives me:
Building software that simply works. The motivation lies in making complex technical systems invisible and creating stable, high-performance solutions that take the strain off users and fit seamlessly into existing processes.

Background:
Many years of research experience in machine learning and reinforcement learning at leading institutes, with a doctorate in computer science from the Karlsruhe Institute of Technology. Work on modern learning methods, optimisation algorithms and multi-agent systems, as well as close collaboration with industry partners at appliedAI. Deep scientific expertise is consistently translated into robust, practical applications. As Lead Data Scientist at zebrafant, Maximilian is responsible for the further development of core algorithms and ensures that the latest scientific approaches function reliably and seamlessly in product operation.

What drives me:
Making research effective when it is not noticeable in everyday life. The driving force behind this is to implement complex algorithms in such a way that they work stably, scalably and silently in the background and deliver real operational added value.