Predictive analytics boils down to data and inference. A startup would start by collecting real‑time feeds—traffic cameras, city sensors, social media chatter, even weather reports. Those inputs feed into a machine‑learning model trained on historical crime patterns to flag anomalies. Then the system issues alerts to the right officers or security teams before a crime even has a chance to unfold. The key is speed and accuracy; you keep the models lightweight, you continuously retrain with new data, and you guard against bias so you don’t blindside entire neighborhoods. That’s the edge: not chasing crime after it happens, but catching the signals that precede it.