Request-for-quote (RFQ) processing is a bottleneck in manufacturing. Engineers and procurement teams spend hours reading PDFs, extracting part numbers, dimensions, materials, and quantities, then manually entering that data into ERP or BOM systems. One RFQ can take 30–60 minutes. Multiply that by dozens of quotes per week, and the cost adds up. AI document intelligence can cut that time to minutes. Here’s how it works and what to expect.
The Manual RFQ Processing Problem
When a customer or internal team sends an RFQ, it often arrives as a PDF — sometimes a clean digital export, sometimes a scan of a printed drawing. The document may contain:
- Part numbers and revision levels
- Dimensions and tolerances
- Material specifications
- Quantities and delivery requirements
- Notes, callouts, and special instructions
Extracting this manually is error-prone. Typos in part numbers, missed dimensions, and misinterpreted callouts lead to wrong quotes and rework. Even when done correctly, the time cost is significant. A mid-size manufacturer processing 50 RFQs per week at 45 minutes each spends nearly 40 hours per week on data entry alone.
How AI Document Intelligence Works
Modern extraction pipelines combine several steps:
OCR and Layout Analysis
First, the system reads the document. For PDFs, it may extract text directly or run OCR on image-based pages. For scanned drawings, OCR is essential. Layout analysis identifies tables, blocks of text, and drawing regions — so the system knows where to look for a BOM table vs. dimension callouts vs. title block.
Structured Extraction with LLMs
Once the content is readable, a large language model (LLM) or specialized extraction model parses it into structured fields. The model understands context: “P/N: 12345” maps to part number, “Qty: 100” maps to quantity, “Al 6061-T6” maps to material. It can handle variations in formatting — different companies use different layouts — and infer missing fields when possible.
Validation and Mapping
Extracted data is validated against your schema (required fields, data types, allowed values) and mapped to your ERP or BOM system. Duplicate detection, unit conversion, and cross-referencing to existing part masters can be automated. For more on document and drawing intelligence, see our CAD drawing intelligence pillar.
Handling Different Formats
PDF: Digital PDFs with selectable text are easiest. Scanned PDFs require OCR; quality depends on scan resolution and clarity. Multi-page RFQs need page-level processing and aggregation of BOM data across pages.
DWG / DXF: CAD files contain geometry and often embedded text (dimensions, notes, title block). Extraction tools read the CAD structure, pull text entities, and parse drawing metadata. Some systems can also extract dimensions directly from the geometry.
STEP / IGES: These are geometry-only formats. Part numbers and materials typically live in the filename or a companion document. Extraction may combine the 3D file with an accompanying PDF or spec sheet.
Paper scans: Handwritten or poorly printed documents are the hardest. OCR accuracy drops; human review is often required for low-confidence extractions. Start with clean digital documents and expand to scans once the pipeline is stable.
Mapping to ERP and BOM Systems
Extraction is only half the job. The data must land in your systems correctly. That means:
- Field mapping: your ERP’s “Part Number” field may not match the RFQ’s “P/N” label
- Unit normalization: inches vs. mm, each vs. lot
- Material code lookup: “Al 6061-T6” → your internal material code
- Creating new part records when the RFQ references parts not in your master
Integration can be via API, file export, or manual copy-paste into a pre-populated form. The more automated the mapping, the higher the ROI — but expect some configuration work to align your schema with the extraction output.
Accuracy Expectations and Human-in-the-Loop
Best-in-class extraction achieves 95%+ field-level accuracy on clean documents. On messy scans or unusual layouts, expect 85–90%. Critical fields (part numbers, quantities) should have higher accuracy; optional fields (notes, special instructions) may be noisier.
Human-in-the-loop is recommended: review extracted data before it hits ERP, especially for high-value quotes. Use confidence scores to route low-confidence extractions to human review and auto-approve high-confidence ones. Over time, as you tune the model and fix edge cases, you can increase the share of auto-approved records.
ROI: Typical Time Savings
Manual RFQ processing: 30–60 minutes per RFQ. Automated extraction with human review: 5–10 minutes. Fully automated for high-confidence cases: under 2 minutes. For a team processing 50 RFQs per week, that’s 20–40 hours saved per week — enough to redeploy staff to higher-value work like quote negotiation and supplier management.
Beyond time savings, automated extraction improves consistency. Every RFQ is parsed the same way; there’s no variation between team members or fatigue-induced errors. That leads to fewer quote corrections, faster cycle times, and better customer experience. The investment pays back quickly when quote volume is moderate — typically within 3–6 months for teams processing 20+ RFQs per week.
Kamna Ventures helps manufacturers automate RFQ and BOM extraction. We assess your document mix, design extraction pipelines, and integrate with your ERP. Ready to cut RFQ processing time? Explore our AI Incubation Lab and our CAD drawing intelligence capabilities.