AI & RAG

Reply to Inbound RFP Security Questions Grounded in Confluence

When a security questionnaire email arrives in a shared Outlook mailbox, retrieves answers from your Confluence policy space and sends a drafted reply with footnoted citations…

CategoryAI & RAG
Enginesim
Difficultyintermediate
Triggerevent
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerSecurity questionnaire email arrives in OutlookOutlook
  • ActionExtract individual questions from email body
  • ActionRetrieve answers from Confluence policy spaceConfluenceConfluence
  • ActionCompose grounded answers with citationsOpenAI
  • LogicFlag low-confidence answers for human handling
  • OutputCreate footnoted draft reply in OutlookOutlook

What it does

Handles security questions that arrive as free-form email rather than a spreadsheet. It reads the inbound message, pulls grounded answers from your Confluence policy space, and prepares an Outlook reply where each answer cites the Confluence page it came from.

When to use it

Use it when buyers ask a handful of ad-hoc security questions by email and your authoritative answers live in a Confluence security space. Ideal for sales engineers who want a citation-backed draft reply waiting in their inbox instead of digging through pages manually.

How it works

  1. 1A new email matching security-questionnaire criteria arrives in the shared Outlook mailbox.
  2. 2The body is parsed to extract each distinct question.
  3. 3Each question is retrieved against the indexed Confluence policy space.
  4. 4The model composes answers strictly from the retrieved Confluence content.
  5. 5A confidence check flags any question with weak retrieval for a human to handle.
  6. 6A draft reply with per-answer Confluence page footnotes is created in Outlook, ready to review and send.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect OutlookMail, calendar, contacts.
  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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