AI & RAG

Grounded Email Replies from Confluence + Dropbox

Reads inbound support email, retrieves matching answers from both the team's Confluence space and Dropbox docs, and drafts a cited reply for human review instead of sending blind.

CategoryAI & RAG
Enginesim
Difficultyadvanced
Triggerevent
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew email received in monitored mailboxGmailGmail
  • ActionSearch Confluence team space for relevant pagesConfluenceConfluence
  • ActionSearch Dropbox folder for supporting documentsDropboxDropbox
  • LogicMerge, rank, and flag low-confidence threads
  • ActionDraft cited reply via OpenAIOpenAI
  • OutputSave draft reply to the email threadGmailGmail

What it does

Turns inbound email questions into draft replies grounded in your team's knowledge. For each incoming message it searches the configured Confluence space and the linked Dropbox folder, assembles the best supporting passages, and writes a draft answer with OpenAI that cites where each fact came from. The draft is saved back as a reply for a human to approve and send.

When to use it

Use it for inbound queues where answers must come from documented policy across two repositories and a person should sign off before anything goes out. Good for partner support, internal helpdesks, and regulated responses.

How it works

  1. 1A new email arriving in the monitored mailbox triggers the flow.
  2. 2The flow searches the Confluence team space and the Dropbox folder in parallel for relevant material.
  3. 3A logic step merges and ranks hits, flagging the thread for manual handling if confidence is low.
  4. 4OpenAI drafts a reply using only retrieved passages, with citations.
  5. 5The draft reply is written back to the email thread for human review.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect GmailRead, draft, send, label.
  2. 2
    Connect ConfluenceSpaces, pages, blueprints.
  3. 3
    Connect DropboxFiles and folders.
  4. 4
    Connect OpenAIModels, embeddings, files.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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