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. This reduces quote turnaround from 2–3 days to a few hours, cutting estimator effort by 40–60% per RFQ. Teams can handle 2–3× the quote volume without adding headcount, directly supporting revenue growth in competitive bid environments.
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.
Vendors to consider
Sources
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