Every system designed to detect child grooming has the same problem: it's looking at the wrong unit of analysis. Grooming doesn't happen in a message. It happens across weeks of messages — a slow accumulation of trust, a gradual shift in conversational register, an escalation in contact frequency that would look unremarkable if you sampled any individual session but reads clearly as a pattern when you step back and look at the whole trajectory. When you build a detection system around message-level classification, you're designing for a problem that doesn't exist. Predators don't send a message that contains the whole grooming attempt. They send a hundred messages across a month, each one just slightly further than the last. This post is about how temporal signal analysis changes the problem — and specifically, how SENTINEL's temporal layer works.…