good q — we don't train on customer drawings. Our detection models are trained on a curated dataset of architectural drawings we've sourced and labeled ourselves, focused on the most common fixture and element types across CSI divisions.
The generalization problem you're pointing at is real and it's the hardest part of this. Our approach is to keep the detection scope tight — rather than trying to generalize across every firm's conventions, we train on a small but high-quality set of fixtures and optimize for precision within that scope.
The result is high confidence outputs on the elements we support, rather than mediocre coverage across everything.
We're expanding the detection surface incrementally as we validate accuracy division by division!