CustomersImproving Uptime for Electric Trucks
Improving Uptime for Electric Trucks
About the Client – 7Gen: 7Gen is a leading provider of integrated commercial electric vehicle fleet charging solutions across North America. A key part of their service offering is ensuring that the electric vehicle chargers in their network continue to function smoothly, which is no small feat given the potential for chargers to break down or malfunction. The Challenge Maintaining up-time on their electric vehicle charging network was crucial. Customers expect electric vehicle chargers to work reliably, so any issues can lead to customer dissatisfaction and increase business costs due to electrician call-out fees. As a start-up 7Gen had limited data to work with, but they possessed a technical understanding of large language models (LLMs) and their potential, and when they were referred to Inject AI they knew they were onto something. The Solution The plan was to seek to see if there were indicators in charger behaviour that could predict imminent failures. If these could be identified early on, corrective action could be taken to prevent the problem altogether, or the repair could be undertaken faster. The sharing of data with Inject AI began, and initially the data did not reveal any clear issues, so further steps were taken to integrate their ticketing and support data, providing Jamie with a baseline to work from. This secondary data set allowed the project to home in on specific parameters, making predictions more accurate. The vision and long-term goal is to develop a predictive system that can be integrated into our daily operations, a continually learning system that is guided by human support. This system would enable our support team to flag and resolve issues proactively."Inject AI is developing predictive maintenance algorithms to monitor EV charger data in real time and alert the customer of technical failures."
Dr Jamie SherrahInject AI
Our client says:"Working with Jamie from Inject AI has been a refreshing experience. He knew what a good solution looked like and was not trying to sell something that wasn’t ready. Jamie was straightforward, acknowledging that while the findings were interesting, they were not yet conclusive. He emphasised the need for more data to achieve more definitive results, and was willing to persist and push through until we could see the long-term solution.
Jamie’s approach stood out because he did not assume a lack of knowledge on our part, avoiding the common pitfall of technologists who often oversell their solutions. His understanding of the practical realities of our business was invaluable. He recognised that while functional chargers are expected, the real value lies in cost savings and operational efficiency.
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