The Fuel Log That Didn’t Add Up — And How I Found the Right Numbers
In a recent build session for Arise & Shine Transporters, I faced a problem with inconsistent fuel logs that were throwing off financial reports. With the help of AI collaboration, I found a way to align the data and improve accuracy.
It was a quiet Tuesday afternoon when I noticed something odd in the Arise & Shine Transporters dashboard. The fuel logs didn’t add up — some trucks had reported fuel consumption that didn’t match the actual distance they’d driven. This inconsistency was throwing off the financial reports, making it impossible to track which vehicles were truly profitable. I needed to find a way to reconcile these numbers, but I wasn’t sure where to start.
I turned to Claude, as I often do, to help me think through the problem. I described the issue — that fuel logs were being recorded manually by drivers, and sometimes, they’d input incorrect or incomplete data. The AI suggested that instead of relying on manual input, I could use the GPS telemetry data to calculate expected fuel consumption based on distance and speed. That made sense — the GPS already knew where the trucks were and how fast they were moving. Why not use that to verify fuel reports?
But implementing that idea wasn’t straightforward. The GPS data was collected every 60 seconds, and I needed a way to compare it with the fuel logs in real time. I tried a few different approaches, but none of them worked until I revisited the AI’s suggestion with a fresh perspective. I realized that instead of trying to match logs to every GPS point, I could use the overall trip data — the total distance and average speed — to calculate expected fuel consumption. That gave me a baseline to compare against the driver’s manual input.
Once I implemented that change, the dashboard began to show discrepancies immediately. Some drivers had overreported fuel, while others had underreported it. That gave me the opportunity to flag those cases and ask for clarification. Over time, the fuel logs became more accurate, and the financial reports started to reflect the true costs of each trip. It was a small win, but it made a big difference in the accuracy of the platform.
Building with AI isn’t always about dramatic breakthroughs. Sometimes, it’s about refining details and finding the right numbers — the ones that actually matter. This session was a reminder that even the smallest inconsistencies can have a big impact, and that AI can be a valuable tool in finding the right answers, even when the path isn’t clear at first.
