video1.0<iframe src="https://www.loom.com/embed/31f0354a9cfe4f089fda5cbc68c66ebc" frameborder="0" width="1920" height="1440" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>14401920Loomhttps://www.loom.com14401920https://cdn.loom.com/sessions/thumbnails/31f0354a9cfe4f089fda5cbc68c66ebc-a550507620ae3eba.gif156.218This Loom describes deploying sustainability intelligence into a railway track maintenance system to deliver real time carbon insights for track fittings within a 9 second platform window. It explains calculating set-level carbon footprints across material, transport, energy, maintenance, waste, and replacement, including real time electricity grid carbon intensity measured in grams of CO2 per kilowatt hour at the time of use. The system ranks initial hotspots and provides AI driven recommendations to optimize inspection and replacement for wheel and area sustainability, while predicting multi year emissions per asset. It also centralizes inventory, inspections, vendors, and electricity information to identify high emission assets and support carbon reduction strategies.