AI USE CASE
RFQ Drawing and Spec Extraction
Automatically extract dimensions, tolerances, and materials from customer RFQ drawings to populate quotes faster.
What it is
When a customer sends a PDF with technical drawings and specifications, AI reads and extracts key fields, dimensions, tolerances, material grades, quantities, and delivery requirements, and populates an internal quoting template automatically.
Data you need
A library of historical RFQ PDFs (drawings and spec sheets) in standard formats such as PDF or DXF, along with a defined quoting template that maps to the fields being extracted.
Required systems
- erp
Why it works
- Define a clear human-in-the-loop step where estimators verify extracted fields before the quote is submitted.
- Start with a single drawing format or customer type to validate accuracy before broadening scope.
- Map the quoting template fields precisely before any vendor configuration begins.
- Collect a representative sample of 50–100 past RFQ PDFs to use for testing and validation.
How this goes wrong
- Poor OCR quality on hand-annotated or scanned drawings causes extraction errors that estimators must catch manually, eroding time savings.
- Non-standardised drawing formats across customers mean the model must be retrained or reconfigured frequently, increasing maintenance burden.
- Extracted values are trusted without human review, leading to quoting errors and costly production rework.
- Integration with the ERP or quoting tool is underestimated and becomes the main bottleneck, delaying go-live.
When NOT to do this
Do not deploy this if your RFQ volume is fewer than 10 per month, the configuration and maintenance effort will cost more than the time it saves for a small team.
Example vendors
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