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wcisco17

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Fair callout — Bluebeam Max does touch estimation. That said, we've talked with 20+ subs and almost none know them know exists.

The reality is people that work in construction (Excluding field work), refers to AI as a scam and compares it to the crypto hype back in 18 .. compares it to crypto in 2018 — all hype, no landing.. seriously they do not like it.

I built this selfishly. Eight months ago I wished I had a programmatic interface to interact with drawings. So I built one.

last point: our customers aren't just doing takeoffs and they aren't just subs or gc's either — they're building tools in the construction tech space and estimation is one use case. The broader opportunity is making all that structured data accessible.

great q!!

Also I didn't see Claude there "ai" tool (a bit weird they are letting the model providers own the entire interface) count any fixtures, and if it did it was probably pulling it from the markup's table which is enabled via their api, which you can technically just slap an llm on top of it. But hey kudos to them - this space is not easy. (https://developers.bluebeam.com/s/studio?language=en_US)

Hey thanks!! For estimation we cover division 08 (doors and opening) you can use it for your estimating purposes with these two endpoints:

- Counting all the doors: https://www.getanchorgrid.com/developer/docs/endpoints/drawi... - Extracting schedules in architectural drawings: https://www.getanchorgrid.com/developer/docs/endpoints/drawi...

and use Claude or any other AI tool to wire up the UI

We're releasing toilets (division 10) later this week, then floors and pipes next.

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.

What specifically would be most useful — a door detection example, spec parsing, or elevation detection?

You can use it like this --

Upload a pdf first: curl -X POST https://api.anchorgrid.ai/v1/documents \\ 90 + -H "X-API-Key: <your-api-key>" \\ 91 + -F "file=@floor-plan.pdf"

then run the door detection for the pdf: curl -X POST https://api.anchorgrid.ai/v1/drawings/detection/doors \ -H "X-API-Key: <your-api-key>" \ -H "Content-Type: application/json" \ -d '{ "document_id": "550e8400-e29b-41d4-a716-446655440000", "page_numbers": [1, 2, 3] }