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AI RFP response drafted from your approved answers

First drafts of every RFP section, with sources

An agent reads the RFP or security questionnaire, drafts each answer from your approved content and marks what your team still has to write or sign off.

Book a free consultation How we build agents
rfp-desk

  1. Bid managerYour team

    A new RFP and a security questionnaire came in for the logistics tender. Can the agent draft the answers?

  2. Proposal agentAgent

    1. docs.read_rfp questions, format and deadline
    2. library.search_answers approved past responses
    3. kb.search_docs product and security docs
    4. draft.write_sections answers with their sources

    Answers are drafted with sources. One question asks for a service level you have never offered, so that one is yours.

    Needs a personNew commitment to a customerService level beyond your standard terms
    Decided by Bid manager

  3. Proposal agentAgent

    1. library.save_answer approved answer for next time

    Your decision is in the draft, and the new answer is saved to the library for the next request.

Example data. An agent at work in a team channel, with a person approving the step you choose.
Mixam: conversion to a final sale from about 20% to about 40%
Where agents help

Every RFP restarts the hunt for answers you already wrote.

Agents draft from your approved answers, and your team owns every commitment.

  1. 01

    Where it stallsExperts answer the same questions in every bid.

    What we do An agent drafts each answer from your approved past responses and cites where it came from.

  2. 02

    Where it stallsOld answers go out with outdated facts.

    What we do Each draft cites its source, so a reviewer sees which document an answer came from and how old it is.

  3. 03

    Where it stallsDeadlines leave no time to review promises.

    What we do The agent marks new commitments and missing answers, so review time goes where the risk is.

What we build

What we build for RFP responses

Whatever comes in, every project is built from the same parts below, set up for your files and your systems.

Requests we answer

  • RFPs
  • RFIs
  • Security questionnaires
  • Due diligence questionnaires
  • Public tenders
  • Vendor assessments
  • Compliance forms
  • Proposal sections

How we build it

  • Answer drafting with sources

    Each question answered from your approved responses and documents, with the source cited.

  • Checks on every draft

    Code checks each draft for unanswered questions, format limits and answers without a source.

  • Document and CRM connections

    Your answer library, document store and CRM connected with the access rights you set.

  • Sign-off rules your bid team agrees

    Which answers stop for a person, agreed with your bid team and enforced in the system.

Not seeing your case? Bring the questionnaire that takes your team longest and we start from that.

Tell us about your workflow
How we build agents

Agents that work as part of your team

They work in the tools your team already uses, take on whole tasks and stop for a person where you decide.

  1. Routines: agents that start on a schedule, on new GitHub issues or on an API delivery.
  2. Connected systems: GitHub, Linear, Notion, Slack, Jira and other tools an agent may call, each connected with its own sign-in.
  3. Approvals log: each action that waited for a person, with who asked, who decided and when.
Routines: agents that start on a schedule, on new GitHub issues or on an API delivery.
Connected systems: GitHub, Linear, Notion, Slack, Jira and other tools an agent may call, each connected with its own sign-in.
Approvals log: each action that waited for a person, with who asked, who decided and when.

Screens from the workspace our agents run in, with example data.

Where it runs

Teams and industries that use it

Each page shows where agents fit in that team or industry.

Engineering in the open

We help build what agentic AI runs on

Open standards, open-source tooling and published research, all public, so you can check our engineering before you hire us.

Shaping agentic AI

Agentic AI Foundation

Vstorm was the first agentic AI engineering company to join the Agentic AI Foundation and received its 2026 Open Agentic AI Innovation Award.

Read the announcement

Open source

We build open-source agent tooling that other engineering teams install

10M+ PyPI downloads
4,000+ GitHub stars
33 public repositories

Framework contributors

When the framework breaks, we fix it at the source

We maintain our own open-source agent framework and contribute fixes to the frameworks our agents run on, through official partnerships with their teams. Breaking changes reach us before they reach your codebase.

Research

Our engineers and PhDs publish peer-reviewed research

Peer-reviewed papers on agent architectures, evaluation and AI security, and an open benchmark anyone can rerun against our results.

Track record

Frontier AI teams now build on what we shipped first

Our client agents were in production before most of today's agent frameworks had a stable release. You get a team that already knows what breaks between a promising demo and a system people rely on every day.

Case studies
All case studies
Engagement models

Ways to start

Each path starts with a free call with a founder or senior AI engineer. A build goes ahead only after its Proof of Value meets the agreed threshold.

Start with one workflow

We map one workflow with your experts and agree a success threshold before any build.

Best whenYou want the success test agreed before you commit to a build.

Book a free consultation
  1. Process redesign and build Best whenYou want one team accountable for the outcome.
FAQ

AI RFP response, answered

What is AI RFP response software? +
AI RFP response uses an agent to read a request for proposal or a security questionnaire, find the closest approved answers and documents, and draft each section with its source. Your bid team edits the draft and decides on anything new the customer would be promised. The agent also marks questions it cannot answer from your content, so none goes out blank by accident.
Which bids suit RFP response automation first? +
Security and due diligence questionnaires, where most questions repeat from bid to bid and approved answers already exist. Start there and measure how much of each draft your team keeps unchanged. Then move to full RFPs with commercial and technical sections. A clean library of past answers decides the quality of the drafts, so it is worth tidying first.
Have you built AI RFP response before? +
We have no published case on RFP responses yet. The closest builds draft replies from approved content. Senetic's inbound sales email is answered in real time, in the customer's language, across 151 countries. For an engraved die maker, pricing went from about 30 minutes to a few clicks, and time of work fell 72% across pricing and database operations.
How do you stop the agent from promising something you cannot deliver? +
The agent drafts only from content your team has approved, and every answer cites its source. When a question asks for something your content does not cover, such as a new service level or a custom term, the agent leaves it open and flags it for the bid manager. Scope, terms and promises to the customer stay with your team.
How do you know it works before the full build? +
We agree a success threshold with your team before the build and test the agent on your own cases in a Proof of Value. It measures accuracy and running costs, and the build goes ahead only when the threshold is met. Corrections from your team go back into the test set, so every later version is checked against them.
Who owns the system, and which models does it run on? +
You own the code, prompts, evaluation sets and rules by contract. The agents run in your environment on hosted or open-weight models. The model sits behind an interface in code, so you can change it when accuracy, speed or your hosting rules change, without rebuilding the agent.
Start with one workflow

Test one workflow on your own data

A Proof of Value shows accuracy and running costs before the full build.

Book a free consultationRead case studies
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