wcisco17

Lovely. Are you in the construction space or are you a dev or both?

Have you tried using it with Finnish construction documents? It should work for the detections that cover drawings.

Until then pdf rules!!!

Fair point and thanks for the tip. No AI here though.

Yep we're constantly improving we're currently above 0.87 for doors

we're thinking of adding a params for the ROC curve so that you can decide your own optimal thresholds depend on when false positive true positive rate is acceptable

interesting yeah parsing DWG/DXF natively makes sense when the source file is clean and well-structured. The precision argument is valid in controlled environments.

The challenge we kept running into is that construction drawings in the wild aren’t always that clean. Unresolved xrefs, exploded dynamic blocks, version incompatibilities, SHX font substitutions — by the time a PDF hits a GC’s desk it’s often the only reliable artifact left. The CAD source may not even be available.

That’s why we see vision becomes the more pragmatic path — not because it’s more precise than structured CAD parsing, but because PDFs are the actual lingua franca of construction. Every firm, every trade, every discipline hands off PDFs. So we made a bet on meeting the document where it actually lives.

On consistency and reproducibility — that’s a real challenge with vision models. Our approach is to keep detection scope narrow and validate confidence scores on every output rather than trying to generalize broadly. Happy to go deeper on that if useful.

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!

Anyone building in or for construction tech — whether that's a startup building estimating or project management software, a construction company with an internal tech team solving this themselves, or a builder looking to automate their workflow. The common thread is drawings. Every one of those groups lives and dies by their ability to extract actionable data from a PDF that was never designed to be machine-readable. We're building the layer that makes that possible so they don't have to start from scratch.

Thanks!!