video1.0<iframe src="https://www.loom.com/embed/ae2c7a5105cc4d138995d0cc5834669d" frameborder="0" width="1952" height="1464" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>14641952Loomhttps://www.loom.com14641952https://cdn.loom.com/sessions/thumbnails/ae2c7a5105cc4d138995d0cc5834669d-bc386a318297b782.gif180.453In this video, I introduce the locomotive diagnostic copilot I built using CHAT GPT, which models my approach to breaking down complex failures and structuring troubleshooting in high-pressure environments. The copilot helps reduce ambiguity through clarifying questions and guides users toward the most likely root causes, ultimately shortening the time from symptom identification to action. I've tested this model with colleagues, and the feedback highlights its clarity and actionable next steps. This project showcases my structured thinking and customer-oriented explanations, serving as my bridge into a sales engineering role. I encourage you to consider how this approach can enhance operational experiences and decision-making in our work.