RFQ Automation: How AI Can Reduce Manual Quotation Work

Request for Quotes (RFQs) are critical revenue opportunities. Yet, many manufacturing, industrial, and distribution businesses still process complex technical inquiries manually.

Why Manual RFQ Processing Slows Sales

When a new RFQ arrives, the sales clock starts ticking. The faster you respond with an accurate quotation, the higher your conversion rate. However, manual processing introduces immediate bottlenecks:

• Manual Spec Reading: Sales engineers spend hours scanning dense specification sheets or engineering drawings.
• Technical Audits & Checks: Reviewing specific parts to check inventory or manufacturing capacities.
• Product Matching: Trying to match legacy catalog numbers or customer descriptions with active product databases.
• Repeated Data Entry: Manually keying details from customer spreadsheets into your CRM or ERP system.
• Quote Preparation Delays: Waiting for pricing sign-offs, causing quotation lag.

What is RFQ Automation?

In simple words: RFQ automation uses specialized software and generative AI to read incoming customer requests, extract key specifications, match items to inventory databases, and generate draft quotes with minimal manual intervention.

Where AI Fits into the RFQ Workflow

AI models excel at interpreting unstructured data. Rather than relying on simple keyword matching, LLMs (Large Language Models) understand technical context:

• Reading Email Inquiries: Scans emails to classify the urgency and inquiry type.
• PDF Spec Extraction: Extracts structural tables, dimensions, materials, and quantities from attachments instantly.
• Product Matching: Analyzes raw text descriptions and suggests corresponding internal SKU matches.
• Drafting Quotations: Auto-completes the quotation template, factoring in margin guidelines.
• Summarization: Builds short internal summaries for sales managers to review.

Example AI RFQ Workflow

Customer RFQ Email → ERP/CRM Entry → AI Extracts Requirements → Database Product Matching → Auto-Generated Quotation Draft → Human Review → Final Sent Quote.

Case Study: Geobit Industries

Consider our work for Geobit Industries, a leading construction chemicals manufacturer. They spent hours reading complex product specifications from incoming RFQs and manually drafting quotes in ERPNext.

Leaner Studio designed an AI pipeline combining **ERPNext, FastAPI, GPT-4o, and Supabase**. Today, when an RFQ sheet is uploaded:

1. The AI parses the product descriptions, volumes, and custom packing specs.
2. It queries Geobit's database, matches items to correct product SKUs, and auto-calculates bulk pricing.
3. The draft quotation is instantly queued in ERPNext.

This resulted in an 87% reduction in manual processing time, saving over 100 hours per month with 0 manual data entry steps. Read the full details in our Geobit RFQ Automation Case Study.

Calculate Your Quotation ROI

Estimate what manual processing is costing your sales team and how much you could save with custom quotation workflows:

When Should a Company Automate RFQs?

If you notice any of these operational bottleneck indicators, your quotation pipeline is ready for AI optimization:

• High RFQ transaction volumes daily.
• Repeatedly matching client-provided specs with identical catalog items.
• Common, repetitive technical questions from customers.
• Sending quotes takes days instead of hours, resulting in lost deals.
• Your sales engineering team is overloaded with administrative tasks.
• The quotation process is a bottleneck dependent on 1–2 key people.