How to Process Unstructured RFQs using OpenAI RAG and Node.js

Procurement workflows rarely begin inside structured systems. They begin in emails. In PDFs. In scanned documents. Requests for Quotations (RFQs) arrive in inconsistent formats. Sometimes as attachments, sometimes as long email threads and often as poorly structured documents with no standard schema. This creates a fundamental problem for engineering teams building procurement platforms. How can a system handle data that is not built to be structured? This is where modern AI-driven architecture is needed. By combining Node.js, OpenAI RAG and a scalable AWS Architecture, it becomes possible to transform unstructured RFQs into structured and actionable procurement data. This blog explores how such a system is architected, the challenges involved in RFQ parsing and how a RAG pipeline enables intelligent procurement automation Structuring the Unstructured In traditional procurement systems, structured data is expected. Fields like: Item name Quantity Specifications Delivery timelines Prici