Enterprise RAG & Agentic Workflows for SOW Automation

Overview

In research and consulting institutions, authoring Statements of Work (SOWs), grant applications, and complex RFP responses often requires hundreds of hours mining historical proposals, past performance metrics, and staff credentials. I designed and deployed an internal Retrieval-Augmented Generation (RAG) and agentic workflow platform to streamline this lifecycle.

Technical Architecture

• Vector Knowledge Base: Chunked, embedded, and indexed past grant proposals, past performance reports, researcher CVs, and subject-matter publications into a searchable vector database.
• Agentic Pipeline (Claude Code & Python): Built modular agents that retrieve relevant institutional precedent, cross-reference methodology requirements, and draft structured sections following agency formatting standards.
• Enterprise Governance & Ethics: Established strict data boundary policies ensuring proprietary partner information and internal research data remained secure and compliant with ethical standards.

Results

• 80% Turnaround Reduction: SOW and initial RFP draft generation dropped from 10 hours to under 2 hours.
• Higher Consistency: Standardized compliance, budget rationale alignment, and institutional voice across research divisions.