Automated quantity takeoff
A structural engineering firm was losing 3 to 4 hours per project manually copying data between PDF drawings, Excel spreadsheets, and their calculation software. We automated all of it.
Representative scenario based on real engagements. Names, figures and contexts have been adapted for confidentiality reasons.
The problem
An 8-engineer structural firm. Every project starts the same way: open the client’s PDF drawings, identify the elements to be sized, copy the dimensions into an Excel spreadsheet, then transfer everything into the calculation software. By hand. Every time.
3 to 4 hours lost per project. With 3 to 4 projects running in parallel, that’s a day and a half of purely mechanical work per week - per engineer.
And mistakes happen. A number misread, a section missed. Nobody catches it until the calculation results don’t add up.
What we built
A Python pipeline that takes a drawing PDF as input and produces a structured quantity takeoff ready to import into their calculation software.
Extraction from PDFs - recognition of structural elements (beams, columns, slabs, foundations), reading of dimensions and annotations, identification of section types.
Automatic structuring - the extracted data is organized in the exact format expected by the team’s software. No manual handling in between.
Validation and alerts - the system flags areas of uncertainty (poor quality drawings, missing dimensions) so the engineer can focus their review where it actually matters.
The main challenge: hand-annotated drawings from clients. Standard OCR breaks down on dense handwriting. We added an uncertainty detection layer that identifies low-confidence zones and requests targeted manual validation - engineers only review the 10 to 15% of elements the system isn’t confident enough to handle automatically.
Results
- 2h30 saved per project on average (varies with drawing complexity)
- Automatic detection rate: 86% of elements without human input
- Paid for itself in 4 weeks based on the team’s project volume
- Engineers do engineering, not data entry
Stack
- Python - PDF extraction, data structuring
- Vector parsing + targeted OCR on degraded areas
- Export to native format of the calculation software
- Local deployment (sensitive data, no cloud)