Munich Startup: What does Datagon AI do? What problem do you solve?
Nathan Gruber, Co-Founder and CEO of Datagon AI: We help industrial customers to make their quality management more efficient with the help of AI. Our platform can use live data from production processes, for example from cars, dishwashers or chemicals, to make suggestions as to when which inspections should be carried out. For example, if a seat has been reworked on a vehicle, the system automatically recognizes that the headliner is often scratched during removal and that an extra test could make sense here.
Munich Startup: But that already exists!
Nathan Gruber: In fact, the fields of predictive maintenance and computer vision are already largely industry standards, but we are in the process of developing the field of predictive quality.
Approach for the wider industry
Munich Startup: What’s your founding story?
Nathan Gruber: Andreas got to know me after completing his doctorate at BMW and together we set up the first predictive quality cases in the Group. After we sensed the need in the market in benchmarks with other manufacturers, we decided to build a platform together with Fabian and Tim 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 it holds up the process with problems, costs quickly run into the millions. In this respect, it is a great challenge to be fast and innovative at the same time and to develop software to the highest industry standards.
Datagon AI wants to shape predictive quality
Munich Startup: Where would you like to be in a year, where would you like to be in five years?
Nathan Gruber: We are currently heavily focused on the DACH market and would like to expand into at least the neighboring European markets within the next year. In five years’ time, we want to have established predictive quality in everyday industrial production and stand for predictive quality in the same way that 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 is now reinvesting in new Munich startups, like Basti Nominacher (Celonis) or Hanno Renner (Personio). This is the same effect that has made Silicon Valley so strong. It creates an ecosystem and knowledge is passed on quickly. In our view, Munich is about to become the German / European hotspot for deep tech start-ups.
Munich Startup: Quick exit or staying power?
Nathan Gruber: We are very much looking forward to gaining international markets and therefore definitely want to grow with the company for a while longer.
