AI AGENTS

Draft RFP Answers from Knowledge Library on Slack Request

When a sales engineer posts an RFP question in Slack, an agent searches your curated Confluence answer library and drafts a cited response back in the thread.

CategoryAI Agents
Enginepaperclip
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerSales engineer posts RFP question in Slack channelSlack
  • ActionSearch curated Confluence answer libraryConfluenceConfluence
  • LogicRank passages and check they answer the question
  • ActionDraft grounded answer with citations via OpenAIOpenAI
  • OutputReply in Slack thread with cited draftSlack

What it does

Turns ad-hoc RFP question handling into a one-message workflow. A teammate pastes a buyer's question into a designated Slack channel; the agent retrieves the most relevant approved answers from your Confluence knowledge library, drafts a tailored response, and replies in-thread with inline source citations so anyone can verify the claim before it ships to the buyer.

When to use it

Use it when proposal and sales-engineering teams field a steady stream of one-off RFP and security-questionnaire questions and you want consistent, on-message answers drawn from a single source of truth instead of tribal knowledge.

How it works

  1. 1A message in the `#rfp-questions` Slack channel triggers the run, carrying the buyer's question text.
  2. 2The agent searches the curated Confluence answer-bank space for passages matching the question.
  3. 3It evaluates whether the retrieved passages actually answer the question, ranking by relevance.
  4. 4OpenAI composes a concise draft answer grounded only in the retrieved passages, appending citation references.
  5. 5The agent replies in the originating Slack thread with the draft plus links to each Confluence source page.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect SlackChannels, DMs, threads, mentions.
  2. 2
    Connect ConfluenceSpaces, pages, blueprints.
  3. 3
    Connect OpenAIModels, embeddings, files.
  4. 4
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  5. 5
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  6. 6
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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