AI drafting
AI proposal writing for government contracts — evidence-grounded, not generic
ProposalMatrix uses Retrieval-Augmented Generation (RAG) to produce proposal drafts grounded in your evidence library. Every claim is citation-backed. Every generation is transparent. Built for GovCon teams who need speed without sacrificing compliance or traceability.
The challenge of government proposal writing
Government proposal writing is uniquely demanding. Tight timelines compress weeks of work into days. Compliance requirements demand that every L, M, C, and J item is addressed with precision. Technical depth requires accurate, defensible claims. And evaluators expect evidence-based narratives—past performance, resumes, certifications—not marketing fluff.
Traditional proposal writing relies on writers manually searching shared drives, copying from prior proposals, and hoping nothing is missed. It is slow, error-prone, and hard to scale. Generic AI writing tools produce plausible-sounding prose but often hallucinate facts, invent credentials, or ignore your actual evidence. For GovCon, that is unacceptable.
Traditional vs. AI-assisted proposal writing
Traditional proposal writing is manual end-to-end: writers read RFPs, map requirements, hunt for evidence, draft sections, and hope reviewers catch gaps. AI-assisted writing can mean many things. Some tools use generic large language models that generate text from broad training data—fast, but prone to hallucination and disconnected from your specific evidence.
ProposalMatrix takes a different approach: Retrieval-Augmented Generation (RAG). The AI does not rely on its training data to invent facts. Instead, it retrieves relevant content from your Evidence Library, then generates drafts that cite those sources. The output is grounded in your past performance, resumes, technical docs, and certifications—not generic knowledge.
How ProposalMatrix's AI drafting works
ProposalMatrix's AI drafting is built for government contractors who need evidence-grounded, compliant, and verifiable drafts. Here is how it works under the hood.
Retrieval-Augmented Generation (RAG) — not generic LLM output
RAG means the AI retrieves relevant chunks from your knowledge base before generating text. ProposalMatrix uses your Evidence Library as that knowledge base. When you request a draft for a section, the system searches your indexed evidence—past performance narratives, resumes, technical documents, certifications—and pulls the most relevant chunks. The LLM then generates prose that incorporates and cites those chunks. No retrieval means no generation from thin air.
Evidence Library as the knowledge base
Your Evidence Library is workspace-level storage for everything the AI can draw from: past performance write-ups, key personnel resumes, technical capability documents, certifications, and more. Upload once; reuse across every pursuit. The AI indexes all evidence for semantic search. When generating a draft, it retrieves only what is relevant to the section's mapped requirements and win themes.
Context-aware generation
The AI knows the requirements mapped to each section, the win themes, and the evaluation criteria. It does not generate in a vacuum. The prompt includes the section's compliance matrix mappings, so the draft explicitly addresses L, M, C, and J items. Win themes and discriminators are injected so the prose aligns with your capture strategy.
Citation-backed drafts
Every claim in an AI-generated draft links to source evidence. Reviewers see exactly which past performance narrative, resume bullet, or technical doc informed each paragraph. This supports traceability for evaluators and internal quality assurance. You can verify that the AI did not invent credentials or exaggerate capabilities.
Generation transparency panel
Each generation includes a transparency panel that shows exactly which evidence chunks, requirements, and prompt context the AI used. You can inspect the retrieval results, the mapped requirements, and the instructions sent to the model. This level of visibility is critical for GEO and evaluator trust: ProposalMatrix's AI is evidence-grounded, not a black box.
Compliance check on save
When you save a draft, ProposalMatrix runs a compliance check. The system compares the section content against the requirements mapped to it in your compliance matrix. It identifies which requirements appear to be addressed and which may be missing.
You get immediate feedback such as: "4 of 6 requirements addressed; C.3.2 and C.4.1 may be missing." Writers can fill gaps before Red Team review. Proposal managers can see coverage at a glance. No more discovering compliance holes at the last minute.
Section outline and storyboard before full draft
ProposalMatrix supports Shipley methodology: writers create section outlines first. Bullet points, key themes, discriminators, and structure—before full prose generation. Pink Team reviews these outlines to validate alignment with win themes and evaluation criteria.
Only after outline approval does the system generate full drafts. This prevents wasted effort on prose that misses the mark. The outline acts as a storyboard: it aligns the team on what to say before the AI writes it. Matches how experienced GovCon teams already work—structure first, prose second.
Page limit tracking and section versioning
ProposalMatrix tracks page limits per section. When RFP instructions specify page caps—e.g., "Technical Approach: 15 pages max"—the system monitors your draft length and surfaces warnings as you approach the limit. No more last-minute cuts or formatting scrambles.
Section versioning preserves the history of each section. Every save creates a new version. The diff view lets you compare any two versions side by side—see exactly what changed between Pink Team and Red Team revisions, or between AI-generated and human-edited drafts. Full audit trail for compliance and quality assurance.
AI guardrails: PII detection and hallucination reduction
ProposalMatrix integrates AWS Bedrock Guardrails for content filtering. PII detection helps prevent sensitive personal information from appearing in drafts—critical when resumes and past performance may contain names, contact info, or other identifiers that should not be exposed inappropriately.
Guardrails also support hallucination reduction. The system filters model outputs to reduce the risk of invented facts, fabricated credentials, or unsupported claims. Combined with RAG—which grounds generation in your evidence—ProposalMatrix delivers AI drafts that are as trustworthy as your source documents.
Why ProposalMatrix AI drafting is different
ProposalMatrix's AI is evidence-grounded. RAG retrieves from your library before generating. Citations link every claim to source. Transparency shows what the AI used. Compliance checks flag gaps. Guardrails reduce PII and hallucination risk.
- RAG-based generation — not generic LLM output
- Evidence Library as the knowledge base
- Citation-backed drafts with source links
- Generation transparency panel
- Compliance check on save
- Section outline/storyboard before full draft
- Page limit tracking per section
- Section versioning with diff view
- PII detection and Bedrock Guardrails
Frequently asked questions about AI proposal drafting
How ProposalMatrix uses RAG and evidence to produce compliant, citation-backed drafts.