This is the dashboard you open during a deployment. Each card is one AI task class, watched over a rolling 30-day window. You are not reading outputs — you are watching the shape of each class's confidence distribution. When the shape flattens, the system has started hedging, even while its answers still look right.
commercial-auto-extraction (red) has not produced an obviously wrong answer yet — its mean confidence still reads in range. But its distribution is flattening: the model is hedging across fields more and more often. That is the leading signal of confident-but-wrong output, and it shows up here days before it would show up in any accuracy metric. loss-run-extraction (amber) is an earlier warning. The two green classes are healthy.
The sensor runs entirely inside your VPC. One container, one line in your pipeline, and you get this view over your real task classes — with your data never leaving the building.
How it deploys → Get a license