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Every SE team has the same archive — last year’s RFPs, last quarter’s security questionnaires, the architecture answers written six different ways across six different deals. An RFP Agent reads each new question and pulls the best prior answer, adapts it to the deal context, and flags anything that needs human judgment.

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When this pays off

Same answers, six versions

The data-residency answer exists in 12 prior RFPs, all slightly different. New responses pick whatever’s easiest to find.

Questionnaires take days

An 80-question security questionnaire eats two SE days. The deal slows because the document is the bottleneck.

Inconsistent answers across deals

Security or compliance answers vary by who wrote them, not by what’s true. Risky for trust and contracts.

Archive is unsearchable

“Have we answered this before?” requires a 20-minute hunt across Drive, Notion, and Slack.

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The shape of this use case

An RFP Agent takes a question + opportunity context and returns a drafted answer with sources.

Inputs

Question text, opportunity context (segment, geography, deal size), prior-answer archive.

Sources

Past RFP and security questionnaire responses, policy docs, architecture artifacts, product changelog.

Output

A drafted answer with citations to source documents and a confidence indicator — high-confidence answers ready to send, low-confidence flagged for SE review.

Delivery

Drafted into the response doc (Google Doc, Notion, Loopio export), posted in Slack for SE review on flagged answers.

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Where to start

Two ways in, depending on whether you want something running today or built to your exact spec.

Clone a pre-built Agent

Open the AI RFP Response Generator. More in the Marketplace.

Build your own

Start from scratch in the builder, or by describing it in Claude Code or Cursor with Programmatic GTM.
Either way, these are prompts your SEs can use on day one:
  • “What’s our standard answer to ‘describe your data residency options’? Cite past responses.”
  • “This question is asking about SOC 2 controls around access. Pull the right answer and adapt it for an enterprise EU customer.”
  • “Walk me through these 30 questions from Acme’s questionnaire — flag the ones that need real SE attention.”

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Where to take it

Once it’s running, deepen it in three moves:

Give it a playbook

Shape it with a prompt, your prior-response archive in Knowledge, and Bulk Schedule.

Automate it on signals

Wrap it in a workflow that fires on a trigger.

Let it improve

Feed back which answers customers accepted into the Agent’s evals so it promotes the strong ones.

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Common pitfalls

The Agent confidently states a control you don’t have. Force citations to actual policy / past-answer documents and fail visibly when the question isn’t covered.
Last year’s accurate answer is this year’s misrepresentation. Tag prior responses with dates and have the Agent prefer recent over old when in conflict.
A “yes” to a feature in mid-market might be a “yes, but” in enterprise. Have the Agent read the opportunity record and adapt — not just look up the question.