RFQ Response Screening Compiler Agent

About the Agent

ZBrain's RFQ Response Screening Compiler Agent automates the classification and evaluation of RFQ response documents across key categories like pricing plan, implementation plan, technical plan, and qualification plan. By leveraging a Large Language Model (LLM), it ensures faster, rules-based scoring and audit-ready outputs, streamlining vendor shortlisting while improving compliance and consistency.

Challenges the ZBrain RFQ Response Screening Compiler Agent Addresses

Manual RFQ screening is slow and error-prone, often causing inconsistent classifications, missed evaluation criteria, and delays in vendor selection. These issues create procurement bottlenecks, heighten compliance risks, and reduce transparency, especially as response volumes increase. Such inefficiencies extend procurement cycles, hinder data-driven decisions, and ultimately impact project timelines and vendor relationships.

RFQ Response Screening Compiler Agent delivers fast, objective, and auditable assessments by automatically categorizing and consistently scoring RFQ responses. Results are output directly into the appropriate Google sheet, minimizing errors and freeing procurement teams to focus on supplier relationships and strategic initiatives. By reducing manual intervention, the agent ensures every vendor is evaluated fairly and efficiently, boosting procurement agility, strengthening compliance, and enabling teams to focus on higher-value work.

How the Agent Works?

RFQ response screening compiler agent automates the classification and evaluation of RFQ responses across key categories. Leveraging an LLM, the agent classifies RFQ response document type, applies standardized scoring logic to vendor submissions, and compiles all evaluation results into structured, audit-ready reports. Below, we outline the detailed steps that define the agent's workflow:

Step 1: RFQ Response Details Intake and Classification

This step initiates the workflow. The agent receives input for each vendor RFQ response from upstream agents and ensures each response is routed to the correct evaluation category within the integrated Google Sheets.

Key Tasks:

• Structured Response Intake: The agent receives input for each vendor response—including document type (Implementation Plan, Pricing Plan, Technical Plan, or Qualification Plan), vendor name, and screening status—from the RFQ response screening agent, which analyzes all incoming submissions. It also receives the evaluation criteria from the RFQ response screening rules creation agent.
• Response Category Mapping: Leveraging an LLM, the agent reverifies the response type, ensures it aligns with one of the four response categories (Implementation Plan, Pricing Plan, Technical Plan, Qualification), and routes it to the appropriate Google Sheet tab. This step ensures accurate categorization and prevents misclassification from any upstream errors.
• Validation: Ensures that each type matches an allowed category; if an unrecognized or irrelevant type is received, the agent displays an appropriate message.

Outcome:

• Category Assignment: Each document type is accurately mapped to its designated Google sheet tab category, ensuring all subsequent evaluations apply the correct criteria.

Step 2: Response Evaluation

Once classified, the agent conducts a detailed, rules-driven evaluation using criteria created upstream by the RFQ response screening rules creation agent.

Key Tasks:

• Evaluation Criteria Retrieval: The agent references the ordered evaluation criteria from column names in Row 1 of the evaluation sheet, provided by the RFQ response screening rules creation agent for the specific category.
• Score Assignment: The agent uses an LLM to evaluate each vendor response strictly according to the screening status: Pass (1 point), Partial (0.5 points), Fail (0 points). If a criterion is present in headers but not in the screening status, its value is left blank and excluded from scoring.
• Blank/Missing Handling: Blank or missing responses in screening status are treated as Fail (0 points). If the criterion is not in screening status, the cell remains blank and does not count toward the score calculation.
• Overall Score Calculation: The agent computes the overall score as a percentage (Total Points Earned / Total Criteria Evaluated) × 100, rounding to the nearest integer and returning as a percent string (e.g., "94%").

Outcome:

• RFQ Response Scoring: Vendor responses are objectively scored against standardized, rules-based criteria, producing transparent results for downstream compilation.

Step 3: Output Generation

The agent compiles and structures all evaluation results for downstream review and reporting.

Key Tasks:

• Structured Output Creation: Consolidates each evaluated response into a clean JSON object, precisely matching Google Sheet columns.
• Comprehensive Reporting: Generates a report for each RFQ response that includes the document type, vendor name, screening criteria, and overall evaluation score (as a percentage).
• Automated Sheet Entry & Link Sharing: Populates scoring outputs directly into the appropriate Google Sheet tab (e.g., Implementation Plan, Technical Plan) and provides a direct link to the updated sheet for traceability.

Outcome:

• Streamlined Vendor Shortlisting: Procurement teams receive real-time reports containing evaluation scores, document type, vendor name, and direct access to the compiled results in Google Sheets, enabling rapid, transparent, and informed vendor selection.

Step 4: Continuous Improvement Through Human Feedback

The agent incorporates user feedback to refine evaluation accuracy and align with evolving procurement requirements.

Key Tasks:

• Feedback Collection: Allows users to review and annotate evaluation results for clarity, relevance, or alignment with procurement standards, helping flag unclear scoring, missing logic, or areas needing improvement.
• Feedback Analysis and Learning: The agent reviews submitted feedback to identify and address recurring issues, such as inconsistent scoring or overlooked evaluation criteria.

Outcome:

• Agent Enhancement: The agent continuously improves by incorporating human feedback, ensuring its evaluation process remains accurate, consistent, and aligned with changing business requirements.

Why use ZBrain's RFQ Response Screening Compiler Agent?

• Accelerated Vendor Scoring: Automatically classifies and evaluates RFQ responses, significantly reducing turnaround time for vendor shortlisting.
• Enhanced Evaluation Consistency: Applies LLM-driven scoring logic to ensure all vendor responses are assessed objectively and in line with procurement standards.
• Audit-ready Results: Delivers structured, machine-readable outputs with transparent scoring, supporting compliance and simplifying downstream audits.
• Reduced Manual Intervention: Minimizes the need for procurement teams to interpret responses or manage complex scoring logic manually.
• Scalable Processing: Efficiently handles large volumes of RFQ responses across multiple categories without compromising accuracy or speed.
• Enhanced Transparency for Stakeholders: Provides clear scoring and documentation, giving all stakeholders visibility into vendor decisions.