AeronauticsFrom Motorsport to AI-Powered Product Development: Breaking New Ground with Innovative Ideas
An Idea Created Together with AI
Innovation often emerges where passion meets technology. One example is our colleague Nikita Baklanov, who combined his enthusiasm for motorsport with cutting-edge AI technology to develop an innovative application. His story demonstrates how technological curiosity, professional expertise, and the targeted use of artificial intelligence can unlock entirely new solutions.
The origin of the app was anything but conventional. Rather than starting with a finished product concept, the process began with a simple question: What projects could be created by combining personal skills and interests? With the help of AI, Nikita generated a variety of concepts and merged several of them into a completely new solution. The result was TrackMate – Race Engineer, an application that provides drivers with real-time feedback while they are on the racetrack. While most existing solutions only analyse driving data after a session has ended, TrackMate delivers live insights during the drive itself, effectively acting as a virtual race engineer.
How did the idea of developing the app come about, and what problem were you trying to solve?
The idea itself was actually generated with AI support. I listed my skills and interests in ChatGPT and asked it to generate ten ideas. I then combined several of them in a way that led to the creation of the app. As a motorsport enthusiast, I had always wanted to drive on racetracks myself. However, without the support of an expensive driving coach, practice sessions are often not very effective. You do not always know exactly what happened on track or what you should improve. TrackMate – Race Engineer is designed to close that gap by providing live feedback after each sector of the track.
What sets your app apart from other solutions on the market?
Current solutions already provide post-session analysis once a driving session is completed. However, none of them deliver feedback live during the session, directly inside the car. The app acts like a real race engineer, speaking directly into the driver’s ear while they are on track.
What role did artificial intelligence play in the development of the app?
AI was involved from the very beginning, from ideation and implementation to architectural decisions. I am a software developer myself, but this project took me into completely new territory.
For almost a year, I stuck with my original feedback pipeline concept until I reached a point where I could no longer make progress. I had to accept that it was not the right approach for building the app. So I turned back to AI and worked together with it to design an entirely new feedback pipeline, which I was then able to implement within just a few weeks.
Was there a moment during the project when AI particularly surprised you?
Absolutely. I am a motorsport enthusiast, but I am not a formally trained engineer in that field. The app performs calculations in real time that I would never have been able to develop on my own. Thanks to the broad knowledge base of modern AI systems, I was introduced to mathematical formulas that are now actively integrated into the application. Without AI, achieving this level of quality would not have been possible for me.
How does the app itself benefit from AI, and what value does it provide in practice?
Within the feedback pipeline, the app uses AI to transform raw sensor data into natural feedback, as if it were being delivered by a real race engineer. This information is then communicated directly to the driver while they are on track, enabling immediate adjustments and performance improvements.
Looking back on the project, what does this example tell us about the future potential of AI in software development?
Over the past eighteen months, AI has improved tremendously. But it is not only the AI models themselves that have evolved. The tools we use to work with AI, such as Codex, Claude Code, and Cursor, have become significantly more capable and reliable as well.
In the early stages, I had to carefully inspect every piece of code to identify potential AI-generated errors. Today, AI agents can collaborate with one another to work through a task until they reach a satisfactory solution, while I focus on validating the functionality in real-world conditions, such as testing directly on a racetrack.
That said, one thing remains unchanged for me: AI is a tool that helps bring human ideas to life. It is not a replacement for people, but rather a powerful enabler that allows us to turn our creativity and expertise into reality.
