Riders rate from the back seat and can't see why a driver stopped; telematics knows the car braked, not that a pedestrian stepped out. Dashi reviews each trip's dashcam footage and finds the moments that matter. Proactive side: kudos cards and lesson cards built from the driver's own clips, posted without being asked. Ask side: the driver says "Help me improve my rating" and Dashi finds the 3-star trip, plays the clip where they yielded to a pedestrian, and offers to send the evidence for a rating review. Footage questions in plain English ("show me pedestrians crossing in front of me") run a live VAST hybrid search. For a coachable moment (turning while a pedestrian was still crossing) Dashi shows a better way from a fixed playbook and writes an Ideal replay prompt for a Cosmos Predict/Transfer-style model to render the same scene driven safely. Every verdict quotes the camera caption word for word. Eval on 12 labelled clips: verdict 12/12, scenario 10/12, 0 false alarms; groundedness of summaries 3/7 (our weak spot). Footage is the pie_cam-3 Toronto research dashcam; ratings, routes and driver are simulated. No telemetry, so no speed or braking claims.
