Founder and CEO Niels Reinhard tells us about predrepair’s aspirations for Data Pitch and beyond.


Describe your Data Pitch challenge idea


predrepair develops predictive maintenance algorithms tailored to the needs of car repair shops in Germany. We help car repair shops to  assess maintenance needs and accurately schedule mechanic activities more efficiently. Our software therefore enables car repair shops to improve the quality of their service while also increasing revenue through better cross-selling opportunities.


What does the idea set out to achieve?


With predrepair, we aim to transform car repair and car maintenance. While experience serves car mechanics well, there is potential to speed up processes and enable more cross-selling opportunities through data-driven software. We enable car repair shops to make use of the power of their own data by giving them access to state of the art machine learning algorithms. Therefore, we help a traditionally conservative industry to benefit from the digital age, equipping them with the tools needed to improve their business model for the years ahead.


What makes your idea different or unique?


Our algorithmic engine enables car repair shops to reliably assess spare part needs and the types of mechanic activities for each incoming vehicle. Up till now, car repair shops have relied upon external information (either from the manufacturer or third parties), which makes them highly dependent on external sources. predrepair is on the frontier of helping car repair shops become independent players and establish competitive advantages by making use of their own data sources. With the help of various angel investors and industry experts who have supported us from day one, we will be able to bring a focused and tailored solution to the market, setting us apart from other approaches in the industry.


Where did the idea come from?


We developed the idea in joint cooperation with the Torpedo Group, one of the largest Mercedes car dealerships in Germany, with over 20 physical car repair shops. We have worked together with the Torpedo Group on smaller data projects before, developing a strong business and personal relationship. The Torpedo Group is also our data provider for the challenge, as they possess a large pool of untapped in-house data. Until today, they have been lacking the internal capabilities to use the data to their advantage. predrepair is a highly scalable solution, as it enables both the Torpedo Group and similarly organised car repair shops to structure their internal processes more efficiently.


What excites you about the challenge you applied for?


Data availability and data ownerships in the automotive sector will be the drivers for new competitive advantages and business models. So far, manufacturers have primarily jumped at the opportunity. However, dealerships and car repair shops are also in a good position to explore and leverage the data they possess to streamline internal processes, generate new revenue opportunities and remain relevant in a highly competitive sector. We are extremely excited that the algorithms we are developing at predrepair will also form the basis for enabling car repair shops to sell their service online more effectively.


How did your team meet?


We share a passion for machine learning and artificial intelligence. The community in Berlin is still quite small, so we met through various meet-ups hosted in Berlin on a frequent basis. Niels has worked with large companies before, consulting for them regarding their AI strategy roadmap and leading large scale data initiatives. Simon drives the passion for ML and AI, while Daniel brings in plenty of automotive expertise through previous work at UBER and BMW. We are optimistic that our complementary skill set of technical and domain knowledge can be a key factor in making predrepair a successful venture.


What’s the best thing about working with data?


“Data is the new oil”. While that may be true, there are a lot of pre-processing steps required in order to make purposeful use of data. This pre-processing is driven by domain knowledge and a sound understanding of the sector. Algorithms are useless if data quality and data understanding are not established. For us, the best thing about working with data is that real value is mostly created by tailoring a data product to the specific needs of the customer, which in our case is car repair shops. We are excited to give our clients access to the power of our predictive algorithms, while also engaging them in a continuous dialogue about how data can inform their daily decisions.


Anything else you want to tell us about your startup and why you do what you do?


For a long time, car repair and maintenance services have been deemed to be too complex to be sold online sustainably. All platform approaches have failed to truly go beyond scheduling appointments. While predrepair will focus on internal car repair shop processes in its initial stage, our technology will also enable the shops to sell maintenance and repair services online. We are extremely excited to work hand-in-hand with car repair shops in Germany to enable them to face the challenges of an increasingly digital world.