AI proposal drafting for federal bids
What separates drafting grounded in your company record from a general-purpose writing assistant, and where the boundary sits.
AI proposal drafting software writes federal proposal narrative — technical approach, management plan, past performance write-ups, executive summaries — from your own company record rather than from general knowledge. The distinction that matters is grounding: a general-purpose assistant produces fluent text about your industry, while a proposal drafting tool produces text that cites your contracts, your win themes and your staff, against the specific evaluation factors in Section M. The second distinction is where the model runs. An unsubmitted federal proposal contains your pricing strategy and technical approach; if drafting sends that to a commercial AI endpoint it has left your security boundary. VETR Proposal drafts against the organization library and runs every model call inside AWS GovCloud on Amazon Bedrock.
What to look for in AI proposal drafting software.
Written to be useful even if you end up buying something else.
It drafts from your record, not the open web
Ask what the draft is grounded in. A tool that has ingested your past performance, capability statements and win themes writes something an evaluator can score. A tool that has not writes competent, generic prose that says nothing a contracting officer can credit you for.
It writes against the evaluation factors
Federal narrative is scored against Section M, not judged on style. Drafting that is aware of the evaluation factors — and of which factor the section under the cursor is answering — is a different tool from one generating paragraphs on a topic.
Where the model runs, and whether that can be bypassed
This is the question to ask first and most buyers ask last. Which cloud, which region, and can proposal content reach a third-party model provider under any configuration? "We do not train on your data" is a different and weaker promise than "your data never leaves this boundary".
It is honest that a draft is a draft
Generated narrative needs a human who owns it. A tool that presents output as finished, with no review step and no visibility into what it drew on, is transferring risk to you at the moment of submission. Look for the review workflow, not just the generate button.
VETR against those criteria.
Every capability below is shipped and in use — not roadmap.
Bedrock-only, enforced in code
In production the provider factory pins Amazon Bedrock and ignores bring-your-own API keys entirely, so there is no configuration that routes proposal content to an external model provider. Embeddings are bound the same way. This is an authorization-boundary control, not a setting.
Grounded in the organization library
Drafting draws on your past performance records, win themes and company profile, so the narrative cites contracts you actually hold. Past performance is ranked for relevance to the solicitation rather than pasted in whole.
Written against the parsed solicitation
Because the RFP has already been parsed into a compliance matrix, drafting happens against the requirement the section is answering rather than against a blank page with a topic heading.
A model choice, gated and opt-in
The platform default is Amazon Nova Pro. Professional plans may opt into Anthropic Claude Sonnet, also inside GovCloud. Both are in-boundary; neither is an external egress path.
The claims above are documented in more detail on the security and in-boundary AI page, with downloadable evidence in the trust center. The scoring methodology is published in full as the VETR Framework, and the capability list lives on the features page.
Drafting a federal proposal section
The sequence that produces narrative an evaluator can score.
Parse the solicitation first
Drafting against a parsed RFP means each section is written to a known requirement and evaluation factor rather than to a heading.
Fill the company library
Load past performance, win themes and capability statements. This is what the draft is grounded in; a thin library produces generic narrative.
Draft section by section
Generate against the requirement the section answers, with the relevant past performance surfaced alongside.
Send it to color-team review
A draft is an input to review, not an output of the process. Pink, Red and Gold reviews score it against the evaluation factors.
Questions buyers actually ask.
Is it safe to use AI to write a federal proposal?
It depends entirely on where the model runs. An unsubmitted proposal contains your pricing strategy and technical approach; sending that to a commercial AI endpoint moves it outside your security boundary regardless of the provider's retention policy. Ask which cloud and region the inference happens in, and whether any configuration can route content to a third-party provider. VETR runs inference inside AWS GovCloud on Amazon Bedrock and the provider factory ignores external keys in production.
Does AI-drafted proposal content need to be disclosed?
There is no government-wide requirement to disclose AI assistance in a proposal as of this writing, but individual solicitations increasingly include their own terms, and some agencies ask. Read Section L for the specific bid. The safer working assumption is that you are responsible for every word you submit regardless of how it was produced.
Will AI drafting write my whole proposal?
No, and a tool that claims it will is describing a risk rather than a feature. Drafting produces a grounded first pass against a known requirement, which is the slow part; the judgement about what is true, what is defensible and what wins stays with the proposal team. That is why the review workflow matters as much as the generation.
Which AI model does VETR use?
The platform default is Amazon Nova Pro on Amazon Bedrock. Professional plans can opt into Anthropic Claude Sonnet, also served through Bedrock inside AWS GovCloud. Both run in-boundary.
Keep reading.
See it on your own solicitation.
Upload an RFP and watch VETR build the compliance matrix. Fourteen days, no card up front.