In the ever-evolving landscape of electric vehicle technology, a leading EV automotive company continues to push the boundaries of innovation, not just in its cars, but also in its customer support systems. At the forefront of this revolution is Vamsi Katragadda, an engineering manager whose recent work on a real-time geolocation-based and machine learning model support feature is set to transform the way owners receive assistance during vehicle breakdowns. This cutting-edge addition to the company’s app promises to deliver unprecedented levels of responsiveness and efficiency in customer support and could potentially save lives by providing support in minutes. In an exclusive interview, Katragadda shares insights into this game-changing technology, its development process, and the potential impact on the owner experience..
Interviewer: Vamsi, can you tell us about the new feature you’ve been working on to enable reimagined customer service?
Vamsi: Certainly. I’ve been leading the development of a real-time geolocation-based support feature for the app based system. This feature allows EV owners to request assistance based on their current location and matching it up with network providers in realtime and takes less than 60 seconds, making the support process much more efficient and tailored to customers vehicle and geo location.
Interviewer: That sounds fascinating. How does this feature work exactly?
Vamsi: The feature utilizes the GPS capabilities already present in the Mobile app. When a user needs support, they can simply tap a button in the app and provide the help taxonomy about the issue, which then sends their current location to our support team and matches the vendor nearby geo location leveraging our machine learning algorithms that are trained and tailored to cost and service provided . This real-time information and prediction allows us to dispatch the nearest available technician and vehicle or provide location-specific troubleshooting advice instantly.
Interviewer: What inspired the development of this feature?
Vamsi: We recognized that many support issues are location-dependent. For instance, an EV owner experiencing trouble with their vehicle in a remote area has different needs than someone in a city center. By leveraging geolocation data, we can provide more accurate and
timely support, ultimately enhancing the overall vehicle ownership experience. More importantly, replicating how humans think like AI leveraging machine learning algos made this possible.
Interviewer: Can you share some of the challenges you faced during development?
Vamsi: One of the main challenges was ensuring user privacy while providing accurate location data. We implemented robust encryption protocols to protect our users’ information. Another hurdle was optimizing the feature to work seamlessly across different regions and in areas with poor network connectivity.
Interviewer: How do you see this feature impacting EV owners?
Vamsi: I believe this feature will significantly improve the support experience for new generation vehicle owners and improve CSAT scores and this will set precedence to be first in the industry to provide real time customer support with machine learning enabled. It will reduce response times, increase the accuracy of support solutions, and provide peace of mind to our customers, knowing that help is just a tap away, wherever they are.
Interviewer: That’s impressive. What has been the most rewarding aspect of working on this project?
Vamsi: The most rewarding aspect has been knowing that our work will directly improve the lives of millions of vehicle owners. It’s exciting to be at the forefront of integrating advanced technology with customer support in the automotive industry and setting a new precedent for the support. Many players started to mimic this support process now, which gives me ultra satisfaction that I have contributed to this.
Interviewer: Looking ahead, what’s next for you and your team?
Vamsi: We’re constantly looking for ways to innovate and improve the Ownership experience. While I can’t disclose specific future projects, I can say that we’re exploring ways to further personalize and streamline the support process using AI and machine learning technologies.
Interviewer: Finally, what advice would you give to aspiring engineers who want to make an impact in the automotive tech industry?
Vamsi: My advice would be very simple. Humans tend to think much more advanced than any system. If you think for yourself and ask questions, what would you do best in this situation? That helps solve any problem with technology leaps.
Interviewer: Thank you for your time and insights, Vamsi. It’s clear that your work is making a significant impact on EV owners customer support capabilities.
Vamsi: Thank you for having me. It’s been a pleasure sharing our work with your audience.
Vamsi Katragadda, Engineering Manager, Tesla
