We’re taking a different path, building a parsing engine that converts CAD (DWG/DXF) into fully structured JSON with preserved semantics (no ML in the critical path).We also have a separate GIS parser that extracts vector data (features, layers, geometries) independently, Like to know how you handle consistency and reproducibility across runs using models and how you make it affordable, especially at scale. because as far as i know CAD and GIS need precision and accuracy.

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.

Dumbcad line barf will not help you with that at all.

There already is a format that is plain text and preserves the semantics: IFC. That's what it was made for.

Is this a service / product you plan to offer outwardly? I'd be interested in learning more. Use case: estimation.