Smart Procurement with AI Agents: Automating the RFQ Lifecycle ARNASOFTECH.COM
Business at a Glance A mid-sized component manufacturer was overwhelmed by the manual, slow, and error-prone process of handling daily RFQs (Request for Quotations) from global clients. Their team juggled emails, spreadsheets, and fragmented systems —leading to quote delays, supplier blind spots, and missed savings. To eliminate this friction, we implemented an AI-powered RFQ automation system using n8n, Google Gemini, Airtable, and Google Sheets. The solution not only captures RFQs from email, but also auto-extracts product data, identifies suppliers (historical + AI-recommended), drafts emails, consolidates quotes, and delivers the best-value quotation—end to end, within an hour. The result? A self-evolving system that slashes manual effort, boosts supplier diversity, and delivers smarter, faster procurement decisions.
Complexities Even with a semi-digital setup, the client faced deep-rooted inefficiencies across the RFQ lifecycle: MANUAL RFQ EXTRACTION FROM EMAILS
Procurement teams had to open every incoming RFQ email, extract line items manually, and transpose them into spreadsheets. The error rate was high— and turnaround was slow. DISJOINTED SUPPLIER RECORDS
Supplier data was scattered. “History Orders” lived in Airtable, but new suppliers were tracked separately. Merging them manually meant lost time and missed vendor opportunities.
NO REAL VISIBILITY INTO PRICING TRENDS
Teams lacked easy access to historical price data. Spotting anomalies or negotiating better rates was near impossible—everything had to be crossreferenced across tools. TEDIOUS FOLLOW-UPS AND SCATTERED EMAIL CHAINS
Procurement managers spent hours drafting repetitive RFQ emails, monitoring replies, and tracking quote statuses manually—hurting response timesand audittrail quality.
Approach Webuiltamodular,AI-enhancedRFQautomationpipeline using no-code tools and human-inthe-loopflexibility—maximizingaccuracywhileretaining team control.
Smart RFQ Intake with AI Structuring A Gmail trigger scans for emails containing RFQ indicators (“PR#”, “Request for Quotation”). Google Gemini parses unstructured email content (PDFs or text) into structured JSON: Material Number Quantity Description n8n flows store this in Airtable and initiate downstream processing.
Dual-Track Supplier Identification Historical Search: Queries Airtable’s “History Orders” by Material Number to retrieve vetted past suppliers. AI Suggestions: Gemini accesses a Google Sheet of global suppliers and ranks additional candidates using attributes like Material Group and Brand. Both lists merge into a review interface in Airtable, ready for human approval before proceeding.
Streamlined Communication Automation Once approved, n8n generates a single Gmail draft to all selected suppliers using a standardized RFQ template, ensuring consistency and traceability.
Automated Quotation Capture & Structuring A second Gmail watcher listens for replies. Gemini parses incoming emails into structured quote components. These are logged in Airtable against the original RFQ.
Cost Comparison & Final Quotation Synthesis Each night, a scheduled n8n flow: Reviews open RFQs Compares supplier prices vs. historical data Identifies the best-value supplier Logs cost deltas and trends
Final Quote Drafting & RFQ Closure Once final prices are computed: n8n drafts a polished client-facing email summarizing the top offers Updates “History Orders” with new material entries Marks the RFQ as “Closed”
Capture Customer RFQ Request-
Send RFQ Draft Email to Approved Suppliers-
Capture RFQ Quotations by Suppliers-
Process Previous RFQ Orders to Calculate Final Cost-
Send Final Quotation Draft Email to Client-
RFQ Email for Supplier
RFQ Quotation for Client
RFQ Airtable
Technology Stack LAYER
TOOLS/PLATFORMS
AI Engine
Google Gemini
Workflow Automation
n8n
Communication Layer
Gmail API
Data Layer
Airtable, Google Sheets
Hosting
n8n Cloud (secured with encrypted credentials)
Impact 70%
>50% +30%
ReductioninRFQTurnaround Time From4hourstounder1hour—cutting waiting time and accelerating decisions. Drop in Clarification EmailsAI-parsed RFQs and structured supplier replies eliminated follow-ups and guesswork. Growth in Supplier PoolAI-enriched sourcing expanded vendor reach, enhancing competition and pricing leverage.
7%
Cost Savings Per RFQ
Zero
Manual Entry Errors-
Real-time benchmarking enabled smarter negotiations and material-level cost optimizations. Every quote and product spec is now parsed, stored, and processed with machine accuracy.
Improved Team Efficiency & Procurement Intelligence StrategicFocus Restored:Teamsshiftedfromcopy-pastetasks to negotiation and vendor management. Live Cost Insights: Airtable dashboards reveal pricing fluctuations and supplier performance. Database That Learns: New materials are auto-added—keeping history current without effort.
Scalable Foundation for Procurement Innovation AI Supplier Scoring (Planned): ML to auto-rank vendors by quality and delivery record. Chat-Based RFQ Access: Slack or Google Chat to “Ask RFQ status” or “Show me cheapest vendor.” ERP Sync: Future flows will directly push POs to ERP and pull live stock for smarter ordering.
Transformation Summary By turning a fragmented, email-heavy RFQ process into a real-time, AI-supervised flow, we helped our client unlock speed, savings, and strategic clarity. RFQ processing now takes minutes—not hours—and procurement no longer runs on guesswork. This case shows how even non-tech teams can leverage smart automation, AI agents, and modular workflows to gain a competitive edge—one quote at a time.
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ARNA SOFTECH ArnaSoftechdelivers end-to-endsoftware solutions, including cloud engineering, data services, automation, and digital transformation. We help businesses scale, optimize, and innovate with our cutting-edge technology.
CONTACT US
[email protected] www.arnasoftech.com Indore, India
