---
title: "Proposal and RFP first drafts: AI Use Case"
description: "Draft proposals and questionnaire answers from your approved past answers, so the team edits a strong first draft instead of starting blank."
url: https://lidvizion.ai/use-cases/proposal-and-rfp-drafting/
site: Lid Vizion
index: https://lidvizion.ai/llms.txt
---

[Use cases](https://lidvizion.ai/use-cases/) · Sales

# Proposal and RFP first drafts

Draft proposals and questionnaire answers from your approved past answers, so the team edits a strong first draft instead of starting blank.

[Talk through this workflow](https://lidvizion.ai/contact/?need=ai-workflow&from=%2Fuse-cases%2Fproposal-and-rfp-drafting%2F#write) · [Automate the repeatable](https://lidvizion.ai/services/automation/)

Where it starts

## The trigger.

An RFP, security questionnaire or proposal request arrives.

How it runs today

- Answers are copied from old documents and go stale.
- Subject experts rewrite the same answers every time.
- Deadlines force rushed, inconsistent responses.

The workflow

## Four bounded steps.

Each step has an owner and a stop rule.

1. 01 / Collect
   Keep approved answers, product facts and case material in one library.
2. 02 / Match
   Each question is matched to the closest approved answers, with the source.
3. 03 / Draft
   A first response is drafted and gaps are flagged as unanswered.
4. 04 / Review
   An owner edits and approves; new approved answers go back into the library.

Where people stay in control

## Approval points.

- Nothing is submitted without an owner’s approval.
- Unanswered or low-match questions go to a subject expert.

How we would measure it

## Against a baseline.

Measured before and after, on the same work.

### Hours per response, before and after.

### Share of answers approved without major edits.

### Responses submitted on time.

Related

## Go deeper.

- [Automate the repeatable](https://lidvizion.ai/services/automation/): Give recurring work an owner, a trigger, and a stop rule.
- [Embeddings and feature vectors](https://lidvizion.ai/resources/basics/embeddings-feature-vectors/): How models turn text, images and records into numbers that can be compared.
- [Build an RFP answer library](https://lidvizion.ai/guides/build-an-rfp-answer-library/): A four-step playbook to gather your approved proposal and questionnaire answers into one owned library, draft first responses from it, and keep it current after every submission.
- [How to Use AI for Proposals and RFPs](https://lidvizion.ai/answers/how-to-use-ai-for-proposals-and-rfps/): How can a company use AI to answer proposals and RFPs safely?

More use cases

## Other places to start.

[All use cases](https://lidvizion.ai/use-cases/)

- [Lead intake to CRM](https://lidvizion.ai/use-cases/lead-intake-crm/): Sales New inquiries are enriched, summarized and written to the CRM with a suggested next step, and the right person is alerted.
- [A website agent that qualifies leads](https://lidvizion.ai/use-cases/website-lead-qualification-agent/): Sales A chat agent on your site answers questions from approved content, asks the qualifying questions your team would ask, and books or routes the lead with a summary.
- [Sales call preparation brief](https://lidvizion.ai/use-cases/sales-call-prep-brief/): Sales Before each booked call, assemble a one-page brief from the CRM, the inquiry and public company information, so the seller starts the call prepared.

An illustrative workflow pattern, not a result from a specific customer.

Bring the messy version

## One workflow. A better way to run it.

Show us the tools, the handoffs, and the part that keeps slowing your team down.

[Let’s map your workflow](https://lidvizion.ai/contact/) · [Read our approach](https://lidvizion.ai/approach/)