I tried turning messy product signals into AI decisions. Here’s what broke.

I’ve been exploring a simple idea: Can messy, real-world product signals be turned into structured AI decisions? Not dashboards. Not reports. Actual decisions. So I started building small systems around this. Things like support signal triage, a recall monitoring experiment I’ve been building (currently calling it Recall Radar), and trying to detect patterns across product feedback. Nothing fancy. Just trying to move from noise → signal → decision. And very quickly, things started breaking. 1. The input is never clean In theory, “signals” sound structured. In reality, they look like: vague complaints partial context emotional reactions duplicated issues completely unrelated noise Even before AI comes in, the first problem is: What exactly is a “signal”? Here’s what incoming signals actually look like: Different sources. Different tones. Different intents. Nothing is structured. Nothing is consistent. And everything overlaps. 2. Classification sounds easy. It isn’t. You think you can ju