Government proposal writing is one of the most time-consuming processes in federal contracting. A single proposal can take weeks of effort - parsing hundreds of pages of RFP requirements, mapping compliance matrices, writing technical volumes, and ensuring every section meets evaluation criteria. We built Propose-AI to change that.

Propose-AI is a multi-agent AI system that takes an RFP document as input and generates a compliant first-draft proposal. Not a template. Not bullet points. A structured, section-by-section draft that maps directly to the RFP's evaluation criteria.

The Problem with Government Proposals

Federal RFPs are dense. A typical solicitation includes the Statement of Work, evaluation factors, compliance requirements, CDRLs, and dozens of attachments. Proposal teams spend the first week just reading and decomposing the RFP before any writing begins.

The real bottleneck isn't writing ability - it's the cognitive load of keeping hundreds of requirements in context while producing coherent, compliant content. This is exactly the kind of problem where AI agents excel.

Architecture: Four Specialized Agents

Rather than using a single monolithic LLM prompt, we designed Propose-AI as a system of four specialized agents, each with a distinct role:

1. RFP Shredder Agent

The first agent ingests the raw RFP document and decomposes it into structured data - extracting evaluation criteria, compliance requirements, section mappings, and key terms. It produces a structured JSON representation that downstream agents consume.

2. Content Strategist Agent (RAG-Powered)

This agent uses retrieval-augmented generation to pull relevant content from your organization's past performance library, technical capabilities database, and previous proposals stored in Pinecone. It maps retrieved content to each RFP section and generates draft narratives.

3. Compliance QC Agent

Every generated section passes through a compliance validation agent that cross-references the content against the extracted RFP requirements. It flags gaps, missing references, and non-compliant language - then sends sections back for revision.

4. Orchestrator Agent

The orchestrator manages the workflow - sequencing agent tasks, handling retries, merging outputs, and producing the final assembled proposal document. It ensures the iterative loop between content generation and compliance checking converges.

Tech Stack

Why a Desktop App?

Government contractors often work with sensitive but unclassified (CUI) proposal content. A desktop application built with Electron gives teams a local-first experience while still leveraging cloud AI services. Documents stay on the user's machine; only the text chunks needed for AI processing are sent to Bedrock.

Results

In testing with real RFPs, Propose-AI reduces the time from RFP receipt to first-draft proposal from 2-3 weeks to under 48 hours. The generated drafts aren't final - they still need human review and refinement - but they provide a compliant structural foundation that eliminates the blank-page problem.

The compliance QC agent catches requirement gaps that human reviewers often miss on first pass, particularly in complex RFPs with cross-referenced evaluation criteria.

What's Next

We're expanding Propose-AI with pricing volume generation, automated past performance narrative matching, and support for multi-volume proposals with consistent cross-referencing. The goal is to make the proposal process faster for small businesses competing for federal contracts.

Interested in AI-Powered Proposal Tools?

We build custom multi-agent AI systems for government contractors and federal agencies.

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