RFQ Response Documents Retrieval Agent

About the Agent

ZBrain RFQ Response Document Retrieval Agent automates vendor RFQ intake by filtering relevant emails, extracting and standardizing multi-format attachments, and converting them into metadata-rich documents, ready for seamless downstream evaluation without manual effort.

Challenges the RFQ Response Document Retrieval Agent Addresses

Manually processing RFQ emails is time-consuming and error-prone; teams must sift through messages, download attachments in various formats and manually extract critical details before evaluation can begin. Incomplete or malformed files create validation bottlenecks, while manual forwarding to screening systems introduces delays and inconsistencies. As RFQ volumes grow, these inefficiencies compound, risking missed deadlines and strained vendor relationships.

ZBrain RFQ Response Agent eliminates these pain points by auto-ingesting emails, using an LLM to confirm RFQ relevance, and validating, classifying, and extracting text from attachments using the best method. Extracted data is enriched with key metadata (RFQ number, project title, vendor name, contact details) and output as structured Markdown, then routed directly to the RFQ screening agent. This end-to-end automation removes manual bottlenecks, ensures data completeness, and accelerates procurement decisions with confidence and clarity.

How the Agent Works

ZBrain RFQ response document retrieval agent follows a structured, step-by-step process to automatically identify, extract, and prepare vendor-submitted RFQ response documents for downstream evaluation. Below is a detailed breakdown of how the agent streamlines the intake and pre-screening stages of the RFQ process.

Step 1: Email Ingestion and Relevance Checking

The agent begins by capturing incoming emails and validating whether the message is relevant to an RFQ submission.

Key Tasks:

• Email Trigger: A Gmail webhook activates the agent upon receipt of an incoming email.
• Email Field Extraction: A code component extracts essential details such as the subject, body text, and list of attachments.
• Relevance Check: An LLM analyzes the email content to determine whether the email pertains to an RFQ. Only relevant emails are passed forward.

Outcome:

• Automated RFQ Email Filtering: Non-relevant emails are filtered out, ensuring the workflow only processes valid RFQ submissions, reducing manual review efforts.

Step 2: Attachment Handling and Text Extraction

The agent examines each attachment in the email and extracts the necessary textual content for further processing.

Key Tasks:

• Attachment Processing: The agent processes each attached file individually in a loop.
• File Type Validation: The agent checks if the file is a supported format, PDF, Word (.doc/.docx), or Text (.txt). Unsupported types are flagged with an appropriate message.
• PDF Classification: If the attachment is a PDF, the agent determines whether it is a native (digitally readable) or scanned (image-based) PDF.
• Content Extraction:
  • Native PDFs: Text is extracted directly using a PDF-to-text utility.
  • Scanned PDFs: Converted into images and processed using a multimodal LLM to extract text.
  • Word/Text Files: Text is directly extracted.

Outcome:

• Accurate Multi-format Text Extraction: Each attachment is accurately interpreted and converted into usable plain text, regardless of input format.

Step 3: Key Metadata Extraction and Formatting

The extracted text is analyzed to retrieve key details and then structured into a standardized format for downstream processing.

Key Tasks:

• RFQ Detail Extraction: An LLM identifies and extracts key RFQ details from the text, such as:
  • RFQ Number
  • Project Title
  • Vendor Name
  • Contact Details
• Markdown Structuring: A dedicated LLM reformats the extracted text into well-structured Markdown, adding only formatting syntax without rewriting, summarizing, or omitting any content. This approach preserves the original structure and ensures clarity for subsequent processing stages.

Outcome:

• Metadata Enriched Structured Document: The extracted document is enriched with structured metadata and formatted in a consistent layout for efficient downstream consumption.

Step 4: Document Routing to Screening Agent

Once formatted, each document is routed to the downstream agent responsible for evaluation.

Key Tasks:

• HTTP POST Call: The agent sends each attachment individually via a POST request to the ZBrain RFQ response screening agent
• Input Transfer: The formatted content serves as the input for screening, allowing evaluation workflows to proceed without delay.
• Sequential Handling: Documents are processed one at a time to ensure precise alignment with the downstream agent’s input requirements.

Outcome:

• Efficient Evaluation Transfer: Processed documents are seamlessly transferred to the evaluation workflow, allowing the screening agent to begin scoring and validation.

Step 5: Submission Summary Compilation

Once all documents have been processed and routed, the agent compiles a consolidated summary for dashboard visibility.

Key Tasks:

• Summary Generation: A final LLM aggregates key metadata, document names and submission context from the processed attachments.
• Dashboard Output: The summary is displayed in the agent’s dashboard for review.
• Human Feedback Integration: Users review each submission summary, and their feedback iteratively fine‑tunes the agent, continuously increasing accuracy.

Outcome:

• Consolidated Submission Summary: A comprehensive submission summary is created, offering clarity on the number of attachments processed and the vendor-specific metadata, supporting visibility and downstream decision-making.

Why use RFQ Response Document Retrieval Agent?

• Time Efficiency: Automates the retrieval and processing of RFQ documents, reducing manual effort and accelerating response cycles.
• Accuracy: Extracts and preserves complete document content while accurately identifying key RFQ metadata.
• Scalability: Handles multiple attachments and high submission volumes, supporting enterprise-scale operations.
• Workflow Automation: Automatically routes processed documents to downstream agents, enabling end-to-end workflow automation.
• Error Reduction: Minimizes manual errors through automated classification, extraction, and validation steps.
• Transparency: Provides real-time visibility into processed submissions through dashboard summaries.