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Interview: Prototype for well-founded design decisions
08.10.2025
Digital simulations and virtual prototypes are becoming increasingly important in product development. They promise shorter development cycles, lower costs and a greater number of variants. At the same time, however, the requirements for accuracy and interdisciplinarity of such tools are increasing. With the SAINT (Smart Automated INTerior) platform, our cluster partner CORE MUC GmbH has developed a prototype that combines predictive and prescriptive methods and thus enables early, well-founded design decisions. In this interview, CTO Haris Ceribasic explains how the technology works, what level of maturity it has already reached and which next steps are crucial for industrial application.
Bayern Innovativ (BI): Mr. Ceribasic, digital twins and virtual prototypes are considered key technologies for shortening development cycles. How does your platform address this and what distinguishes it from existing approaches?
Haris Ceribasic (HC): Our SAINT (Smart Automated INTerior) platform addresses a key problem in product development: many existing tools look at individual areas in isolation - such as hardware, software or electronics. In contrast, we pursue an interdisciplinary, holistic approach and incorporate the entire user experience into the simulation. Another difference lies in the methods we use: We use predictive and prescriptive simulations - i.e. methods that not only predict scenarios, but also derive specific recommendations for action for the next development iteration. This allows well-founded decisions to be made in the concept phase, long before physical prototypes are built.
For example, instead of physically building several variants of a seating concept, installation space collisions, optimized control and energy consumption can be run through virtually, allowing the best solution to be selected at an early stage. This saves development time and hardware costs and significantly reduces the need for subsequent redesigns.
BI: The validation of such systems is crucial for their acceptance in the industry. What level of technological maturity has SAINT reached and how has this been demonstrated?
HC: We have developed the platform to a functional, demonstrable prototype and tested it with OEM model data. This enabled us to show that complex interior functions can be realistically simulated as early as the concept phase.
The simulation data generated can also be used directly and transferred to the series development process. This creates a seamless transition from virtual to physical product development. On the Technology Readiness Levels (TRL) scale, we are in the 6-7 range, which means that the technology is no longer purely experimental, but has already been validated in relevant environments and is about to be transferred to industrial application.
"We use predictive and prescriptive simulations - i.e. methods that not only predict scenarios, but also derive specific recommendations for the next development iteration. This allows well-founded decisions to be made in the concept phase, long before physical prototypes are built."
Haris Ceribasic
CTO, CORE MUC GmbH
BI: Many research projects remain at demonstrator level. What next steps are necessary to transfer SAINT to industrial value creation - and what do you bring to the table?
HC: The next step is the transfer to real industrial processes. To this end, we are specifically looking for partners from the automotive industry, OEMs and suppliers alike, who want to integrate our platform into specific development projects, either as part of a cooperation or as strategic customers. CORE MUC provides a solid technological basis for this: a functioning software stack with a modular architecture, extensive technical documentation and unpublished research results from three years of development work. This is complemented by a system architecture and AI-supported simulation algorithms that we can adapt and scale together with partners for specific use cases.
Ultimately, we see SAINT as a response to increasing international cost pressure: AI-supported process optimization can significantly shorten development cycles and reduce the cost of physical prototypes - without compromising on the speed of innovation.