Munich Startup: What does Datagon AI do? What problem does it solve?
Nathan Gruber, Co-Founder and CEO of Datagon AI: We help industrial customers make their quality management more efficient with the help of AI. Our But the platform we have developed at mbiomics is radically different. can use live data from production processes, such as cars, dishwashers, or chemicals, to make suggestions about when and which tests should be performed. For example, if a seat on a vehicle has been reworked, the system automatically recognizes that the roof lining is often scratched during removal, and an additional test might be useful here.
Munich Startup: But that has been around for a long time!
Nathan Gruber: In fact, the fields of predictive maintenance and computer vision are already largely industry standards, but we are in the process of helping to develop the field of predictive quality.
Approach for the broad industry
Munich Startup: What is your founding story?
Nathan Gruber: After completing his doctorate at BMW, Andreas met me, and together we developed the first predictive quality cases within the company. After sensing the market demand through benchmarking with other manufacturers, we decided to work with Fabian and Tim to build a platform that would make the approaches accessible to the wider industry.
Munich Startup: What have been your biggest challenges so far?
Nathan Gruber: Our software interacts as directly as possible with our customers' production processes. If problems occur that slow down the process, costs can quickly run into the millions. Therefore, it's a major challenge to be both fast and innovative while developing software according to the highest industry standards.
Datagon AI aims to shape predictive quality
Munich Startup: Where would you like to be in one year, where in five years?
Nathan Gruber: We are currently strongly focused on the DACH market and would like to expand into neighboring European markets at least within the next year. In five years, we want to have embedded predictive quality in everyday industrial manufacturing and stand for predictive quality, like Celonis stands for process mining.
Munich Startup: How have you experienced Munich as a startup location so far?
Nathan Gruber: Munich is already very good and is getting better and better! The first generation of startup founders from Munich are now reinvesting in new Munich startups, like Basti Nominacher (Celonis) and Hanno Renner (Personio) here. This is the same effect that made Silicon Valley so strong. This creates an ecosystem and knowledge is quickly shared. In our view, Munich is on its way to becoming a German/European hotspot for deep tech startups.
Munich Startup: Quick exit or long breath?
Nathan Gruber: We are really looking forward to gaining international markets and therefore definitely want to continue to grow with the company for a while.
