RFQ Response Documents Retrieval Agent, developed by ZBrain, is an enterprise-grade automation solution that optimizes the intake phase of the RFQ (Request for Quotation) process. In high-volume communication environments, where time-sensitive opportunities can be missed, this agent ensures that all RFQ-related emails and attachments are promptly identified and captured. By continuously monitoring designated inboxes, it eliminates the need for manual oversight and provides a reliable, scalable mechanism for surfacing potential opportunities in real time.
The agent integrates directly with E-mail platforms via secure APIs and leverages large language models (LLMs) to evaluate incoming messages with contextual awareness. Instead of relying on basic keyword filters, it interprets email content to detect true RFQ intent—even when phrasing or formatting varies. Once relevant emails are identified, the agent retrieves and parses attachments in common formats such as PDFs, Word documents, and spreadsheets. It then extracts structured data—such as line items, specifications, and response deadlines—normalizes it into clean, markdown-formatted text, and prepares it for seamless transition to downstream processing by the RFQ Screening Agent.
By automating both detection and document parsing, the RFQ Response Documents Retrieval Agent significantly reduces turnaround times while increasing the accuracy and consistency of RFQ intake. It enables procurement and sales teams to respond faster and more effectively, without the burden of manual inbox monitoring or document triage. With built-in support for exception handling and a feedback loop to continuously refine its classification model, the agent not only improves day-to-day efficiency but also scales RFQ handling capacity across the organization—ensuring no opportunity goes unnoticed or underutilized.
Accuracy
TBD
Speed
TBD
Sample of data set required for RFQ Response Documents Retrieval Agent:
Subject: RFQ Response Submission – ElectraTech Proposal for Atlas Manufacturing
Dear Ms. Jennifer Collins,
I hope this message finds you well.
On behalf of ElectraTech, I am pleased to submit our response to the Request for Quotation (RFQ) issued by Atlas Manufacturing Ltd. Please find attached the following documents as part of our comprehensive proposal:
We trust that this submission will meet your expectations and align with your project objectives. Should you require any clarifications or further information, please do not hesitate to reach out.
We appreciate the opportunity to participate in this engagement and look forward to the possibility of working with Atlas Manufacturing.
Warm regards,
David Reynolds
Business Development Manager
ElectraTech Inc.
+1 (312) 555-7490
david.reynolds@electratech.com
www.electratech.com
Attachments:
(1) Technical Plan.pdf
(2) Implementation Plan.pdf
(3) Pricing Plan.pdf
(4) Qualification & Experience Document.pdf
Sample output delivered by the RFQ Response Documents Retrieval Agent:
Vendor Details
The following documents were identified and extracted:
Automates scoring of RFQ responses, classifying vendor documents and updating evaluation results in a structured Google Sheet for seamless vendor selection.
Automates the process of evaluating and ensuring that new supplier catalogs align with procurement policies
Ensures smooth integration by mapping product data to the catalog, flagging of missing or inconsistent fields for manual review.
Automates the creation of standardized, accurate, and brand-aligned product descriptions and pricing formats across large catalogs.
Defines screening rules and evaluation criteria for finalized RFQs to streamline vendor response evaluation.
Automates RFQ creation by processing requirements, selecting templates, and ensuring compliance with organizational standards.