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Manufacturing · Operations
⚙️ AI Workflow Automation

AI-Generated RFQ Forms from Unstructured Customer Emails

How a manufacturing company eliminated manual RFQ processing — cutting turnaround from hours to 28 seconds with near-zero errors.

Faster RFQ turnaround vs manual process
~0
Manual errors on AI-generated RFQs
28s
Time to generate a complete RFQ from raw email
100%
Sales team hours reclaimed from admin
— The Situation

The challenge and the solution

// The Challenge

Customers send rough descriptions. Sales teams spent hours turning them into quotes.

A manufacturing client received dozens of customer enquiries daily — unstructured emails describing what they needed in plain language. Sometimes with attachments, sometimes just a paragraph of text. A sales team member had to read each email, interpret the requirements, cross-reference the product catalogue, and manually fill in a Request for Quotation form before a quote could be produced.

  • Average RFQ creation time: 45–90 minutes per request depending on complexity
  • High error rate — misinterpreted specifications, wrong product codes, missed quantities
  • Sales team spending 30–40% of time on admin rather than selling
  • Delayed response times damaging competitiveness — customers expecting same-day quotes
  • Scaling was impossible — more volume meant more headcount, not more automation
// The Solution

AI interprets the email, extracts specifications, and generates the RFQ in 28 seconds.

We built an AI workflow that reads incoming customer emails using GPT-4, extracts all specification data regardless of how it's phrased, cross-references the product catalogue, and generates a complete, accurate RFQ form — ready for the sales team to review and send in under 30 seconds.

  • Email received → AI reads and interprets customer intent and specifications
  • Product catalogue cross-referenced to identify correct SKUs and pricing
  • Complete RFQ form generated with all fields populated accurately
  • Sales rep reviews in CRM, approves, and sends — no re-keying
  • Edge cases and ambiguous specs flagged for human clarification only

— What We Built

Six components of the solution

Every piece designed to solve a specific part of the problem — integrated into one system that works end-to-end.

🧠

LLM Specification Extraction

GPT-4 reads raw email text and extracts: product type, dimensions, quantities, delivery deadline, and any special requirements — regardless of how loosely they're described.
🔗

Product Catalogue Integration

Real-time API connection to the product database. AI matches extracted specifications to correct SKUs, checks availability, and retrieves current pricing automatically.
📋

RFQ Form Generation

Structured RFQ form populated with all extracted and matched data. Incomplete or ambiguous fields flagged clearly — sales rep only touches exceptions.
✉️
Email Trigger & CRM Routing
Incoming emails auto-detected, processed, and the generated RFQ routed to the right sales rep in the CRM with the original email attached for context.
📊

Error Flagging & Confidence Scoring

Every extracted field has a confidence score. Low-confidence extractions are highlighted for human review — ensuring accuracy without removing human oversight on complex cases.

End-to-End in Under 30 Seconds

From email received to RFQ ready in the sales rep's queue: 28 seconds average. What previously took 45–90 minutes now takes less time than reading the original email.

— Results

What this delivered for the client

The numbers — measured outcomes

Faster RFQ generation — from 45–90 minutes to 28 seconds end-to-end
~0%
Error rate on AI-generated RFQs — validated against product catalogue at point of generation
30h
Per week returned to the sales team — redirected from admin to active selling
Same-day
Quote turnaround achieved — where previously customers waited 1–2 days
Sales team focused on closing, not admin

With RFQ generation automated, the sales team shifted from spending half their day on form-filling to focusing entirely on relationships, negotiations, and closing.

Customer response times improved dramatically

Same-day quote turnaround became the standard. Previously a competitive weakness — now a differentiator. Several new clients cited response speed as a reason for choosing the company.

Zero system replacement

The AI layer was built on top of existing email infrastructure and the existing product catalogue database. No ERP migration. No new CRM. Implementation disruption: zero.

"The RFQ automation they built saves us 5 hours every single day. I will never switch to any other company."
S
Salvatore
Manufacturing Client · Europe
— Delivery Timeline

How we delivered it

From the initial audit to live deployment — every stage designed to minimise risk and maximise speed to value.

Week 1–2
🔍
AI Audit & Process Mapping
Mapped the full RFQ workflow. Analysed 200 historical customer emails to understand variation in how customers describe requirements. Identified edge cases and ambiguity patterns.
Week 3–4
⚙️
LLM Pipeline Build
Prompt engineering for specification extraction. Product catalogue API integration. Confidence scoring system. Initial testing against historical emails — 91% accurate from day one.
Week 5–6
🔗
CRM Integration & Testing
Connected AI output to CRM. Built the sales rep review interface. Parallel run — AI and manual side by side for two weeks. Accuracy validated against human-generated RFQs.
Week 7
🚀
Live Deployment
Full go-live. AI handling 100% of incoming RFQ emails. Sales team reviewing AI-generated forms rather than building from scratch. Average processing time: 28 seconds.
— Technology stack
OpenAI GPT-4Python · FastAPIREST API IntegrationCRM WebhookProduct Catalogue DBEmail Parser

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