RFQ Automation / AI Extraction
RFQ attachment extraction and JS lead record workflow
A workflow that reads long RFQ emails and attachments, extracts structured requirement fields, creates a JS lead file...
Reference context
This workflow helps sales and engineering teams stop re-reading long inquiry threads. AI extraction turns paragraphs and files into product category, quantity, target market, certification, material, budget, timeline, open questions, and next action.
This page is a clearly labeled benchmark, demo, or reference playbook unless separately confirmed as a Hexastruct closed client project.
Operating logic
- Receive inquiry from website form or email
- Extract fields from text and attachment notes
- Create a structured JS lead record
- Score urgency and missing information
- Push review summary to Feishu and store the source
Expected outputs
- JS lead file
- RFQ field summary
- Missing requirement checklist
- Quotation handoff brief
- Follow-up question draft
Why this matters for Google and buyers
Specific case pages help search engines understand Hexastruct services by category, market, workflow, and buyer problem. For human buyers, the same structure makes the offer easier to trust and easier to brief.
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