Bus control is still a very human, very reactive operation. A controller sees a late bus, a bunched pair, or a corridor starting to fail, then decides whether to hold, short-turn, gap-fill, or let the service recover naturally. The issue is that by the time the delay is obvious, the best intervention window may already have passed. For our hackathon project, we built PulseOps : an AI operations layer for London buses that predicts operational risk before it becomes visible on the road. PulseOps ingests live transport signals such as TfL vehicle locations, Countdown predictions, GTFS schedules, stop sequences, road disruption context, weather, and JamCam CCTV. It then builds a per-vehicle risk score and projects that risk roughly fifteen minutes forward. The goal is simple: help a human controller understand which bus is likely to become a problem, why it is happening, and what intervention is worth considering. Why Pydantic mattered PulseOps is not a chatbot. It is an operational decision-support system.…