rfp-agent

Request for Proposal (RFP) Agent

Documentation for Devoteam's RFP Agent

Project Overview
Problem Statement
Current Scope
- Extended Capabilities via Gemini Enterprise
- Long-Term Vision and Objectives
System Architecture and Implementation
How to Add the RFP Agent to Gemini Enterprise
Bill of Materials

Project Overview

The Request for Proposal (RFP) Agent is a sophisticated AI-powered system designed to transform how organizations respond to new business opportunities. Built on Google Cloud, this agent automates the entire proposal lifecycle, from intelligently analyzing complex RFP documents to generating accurate, compliant, and compelling responses. By connecting to internal knowledge sources and leveraging natural language, the RFP Agent drastically reduces manual effort and empowers sales and proposal teams to focus on strategic differentiation and winning bids.

Problem Statement

Responding to RFPs presents several significant challenges for modern enterprises:

High Manual Effort: The process of reading, dissecting, and responding to lengthy RFP documents is incredibly time-consuming, diverting valuable resources from strategic tasks.
Inconsistent Quality: Without a centralized system, proposal quality can vary widely, and knowledge from past successful bids is often lost or difficult to access.
Risk of Non-Compliance: Manually tracking and verifying hundreds of mandatory requirements is error-prone and can lead to immediate disqualification.
Information Silos: Critical information needed for responses is often scattered across different systems (Drive, Confluence, past emails), making it difficult to assemble a complete and accurate proposal quickly.
Slow Turnaround Times: The lengthy manual process limits the number of RFPs an organization can pursue, leading to missed revenue opportunities.

Current Scope

The system is currently focused on automating the core components of the RFP response workflow:

Intelligent RFP Analysis: Automatically ingests and deconstructs RFP documents to identify key requirements, evaluation criteria, deadlines, and submission instructions.
Automated Response Generation: Drafts high-quality, context-aware answers by querying internal knowledge bases, including past proposals, technical documentation, and case studies stored in Google Drive and Confluence.
Compliance Verification: Systematically cross-references the generated proposal against all mandatory requirements to ensure 100% compliance and flags any unanswered sections.
Clarification Question Generation: Proactively identifies ambiguous, contradictory, or incomplete requirements within the RFP and suggests well-formulated clarification questions to send to the issuing authority.

Extended Capabilities via Gemini Enterprise

When deployed on Gemini Enterprise, the RFP Agent bridges the gap between drafting and action:

Automated Review & Notification: The agent can automatically route specific proposal sections to designated subject matter experts for review and approval via email or chat.
Autonomous Submission: Upon final approval, the agent can be configured to package the final proposal documents and submit them through the required procurement portal or email, ensuring on-time delivery.

Long-Term Vision and Objectives

The strategic roadmap for the RFP Agent is centered on enhancing its strategic advisory capabilities and deepening its integration into the sales ecosystem:

Advanced Bid Strategy Analysis: Incorporating AI-driven analysis to evaluate an RFP against historical win/loss data, providing a “bid/no-bid” recommendation based on the probability of success.
Competitor Intelligence Integration: Analyzing past successful bids from competitors (where publicly available) to identify winning themes and pricing strategies.
Dynamic Pricing & Resource Modeling: Integrating with internal financial and resource management systems to suggest optimal pricing and automatically outline the required project team and resources for the SOW.
CRM Integration: Achieving deep, bidirectional synchronization with CRM platforms like Salesforce to automatically create new opportunities, log proposal activity, and update sales pipelines based on RFP status.

System Architecture and Implementation

This project is engineered as a secure, enterprise-grade, and highly observable AI system. The architecture prioritizes seamless knowledge retrieval, robust quality assurance, and scalability.

Google Cloud Native: Powered by Google Cloud’s Vertex AI and Gemini models to ensure powerful, scalable, and instant data retrieval from your enterprise knowledge.
Rigorous AI Evaluation Framework: The agent’s performance is continuously validated using Rubric-Based Metrics, where a “Judge” LLM (Gemini) systematically evaluates the quality and accuracy of its outputs.
Comprehensive Quality Metrics: The agent is evaluated on a 0.0 to 1.0 scale across four key dimensions:
- Final Response Quality: Ensures the generated proposal text is correct, relevant, and properly formatted.
- Tool Use Quality: Verifies the accuracy of function calls when querying knowledge bases in Google Drive, Confluence, or other APIs.
- Hallucination: Checks the factuality of all claims by ensuring they are properly grounded in your source documents.
- Safety: Ensures all generated content is compliant with corporate policies and free of inappropriate language.
Observability and Debugging: All evaluation logs are securely stored in a Google Cloud Storage (GCS) bucket, providing a detailed source of truth for debugging and performance tuning. High-level evaluation summaries are also available directly within the Vertex AI Console.

How to Add the RFP Agent to Gemini Enterprise

This guide walks administrators through the process of adding our RFP Agent directly from the Google Cloud Marketplace to your organization’s Gemini Enterprise environment.

Prerequisites

You must have the Discovery Engine Admin role in your Google Cloud project.
You must have an existing Gemini Enterprise app set up in your Google Cloud console.

Part 1: Add the Agent via the Google Cloud Console (For Admins)

Google Cloud provides a streamlined flow to add Marketplace agents directly into your Gemini Enterprise app.

Step 1: Navigate to Gemini Enterprise Log in to the Google Cloud console and go to the Gemini Enterprise page.

Step 2: Select Your App Click the name of the specific Gemini Enterprise app to which you want to add the RFP Agent.

Step 3: Add a Marketplace Agent In the left-hand navigation menu, click Agents. Then, click the + Add Agents button at the top of the screen.

Step 4: Choose the Agent Type In the “Choose an agent type” section, locate the Agents via Marketplace option and click Add.

Step 5: Search and Select Search for RFP Agent in the Marketplace search bar. Click on our agent in the results, then click Next.

Step 6: Authenticate and Finish Review the agent details and click Next. If prompted, enter the required authentication details (such as OAuth credentials for Google Drive) and click Finish.

By default, newly added agents need to be shared before your team can see them.

Step 1: Open User Permissions Still on the Agents page in the console, click the Display name of the RFP Agent you just added. Then, click the User permissions tab.

Step 2: Add Users Click Add user. In the dialog box, you can grant access to specific individuals (User), specific teams (Group/Workforce identity pool), or your entire organization (All users).

Step 3: Save Permissions Assign the appropriate role and click Save. The agent is now authorized and live for your selected users

Bill of Materials

This project provides a comprehensive overview of the software dependencies and infrastructure components that constitute the DGC Internal RFP Agent project.

### 📦 Software Components

The project is structured as a multi-service application with two primary Python-based services.

#### 1. RFP Agent Service (ai/services/rfp-agent) Core engine for RFP analysis and agentic workflows.

Dependency	Purpose
`google-adk[a2a,bigquery]`	Google Agent Development Kit with A2A and BigQuery support
`google-cloud-aiplatform`	Vertex AI SDK for Gemini and Agent Engines
`a2a-sdk`	Agent-to-Agent communication
`fastapi` / `uvicorn`	API framework and ASGI server
`pydantic`	Data validation and settings management
`structlog`	Structured logging for observability
`opentelemetry-api`	Distributed tracing and telemetry
`auth0-python` / `python-jose`	Authentication and JWT handling
`httpx`	Asynchronous HTTP client

#### 2. DCR Service (ai/services/dcr) Service for Data Clean Room (DCR) operations and state management.

Dependency	Purpose
`google-cloud-firestore`	NoSQL database for session and state persistence
`google-cloud-pubsub`	Asynchronous messaging and event handling
`google-cloud-iam`	Programmatic IAM management
`google-auth`	GCP authentication and credential handling
`fastapi`	Lightweight API interface
`cryptography`	Secure data handling and encryption

### ☁️ Infrastructure Components (GCP)

The infrastructure is managed via Terraform and deployed to Google Cloud Platform.

Component	Resource Type / Module	Description
Compute	Cloud Run	Serverless execution for the RFP Agent and DCR services.
Database	Firestore	State persistence for DCR and session management.
Messaging	Pub/Sub	Event-driven architecture for service communication.
Storage	Cloud Storage	Buckets for document storage and RAG knowledge bases.
Security	Secret Manager	Secure storage for API keys, Auth0 secrets, and credentials.
IAM	Service Accounts	Dedicated identities for services following least-privilege.
CI/CD	Cloud Build	Automated build and deployment pipelines.
Registry	Artifact Registry	Container image storage for Cloud Run services.
Governance	Cloud Resource Manager	API enablement and project-level configurations.

### 🛠️ Development & Tooling

Package Manager: uv (for fast, reproducible Python environments).
Infrastructure Manager: Terraform (v1.0+).
Containerization: Docker (multi-stage builds for slim images).
Linting/Formatting: ruff (respecting Google Python Style Guide).