# Agentic RFQ-to-Quotation

Enquiries arrive as unstructured email and messages. Turning them into quotations means reading intent, matching products, checking stock and applying commercial policy — work that is slow, repetitive and easy to get subtly wrong.

## About this page
- Canonical: https://quarkcs.io/automation-blueprints/rfq-to-quotation
- Type: AutomationBlueprint
- Published: 2026-09-06
- Updated: 2026-09-15

## Problem
Enquiries arrive as unstructured email and messages. Turning them into quotations means reading intent, matching products, checking stock and applying commercial policy — work that is slow, repetitive and easy to get subtly wrong.

## Agent Loop
- Trigger: An RFQ arrives by email, WhatsApp or the web form.
- Context: Customer record, item master, price lists, live stock, commercial history and past quotations.
- Agent: Interpret the request, resolve the customer, match products and report a confidence score for each match.
- Policy: Discount ceiling, credit hold, margin floor and match-confidence thresholds are evaluated deterministically, outside the model.
- Human checkpoint: Sales authority approves only low-confidence matches or threshold breaches.
- Action: Create the quotation, send it through the approved channel and schedule follow-up.

## Tools
- read_customer
- search_items
- read_price_list
- read_stock
- create_quotation_draft

## Flow
1. RFQ received
2. Parse and match products
3. Read customer, stock and price
4. Check margin and credit
5. Approve exception if needed (human approval)
6. Create quotation
7. Follow up and log

## Systems
- Email
- ERPNext
- Framework M
- WhatsApp Business API

## Framework M DocTypes
- Customer
- Contact
- Item
- Item Price
- Bin
- Quotation
- Communication