HR & RECRUITING

Anonymized Exit-Interview Attrition Trend Board

Transcribes each completed exit-interview Zoom recording, extracts attrition drivers with no names or identifying details.

CategoryHR & Recruiting
Enginesim
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerZoom exit-interview recording completedZoomZoom
  • ActionFetch meeting transcriptZoomZoom
  • ActionExtract and anonymize attrition driversOpenAI
  • LogicReject output if any identifying detail remains
  • OutputAppend themed drivers to Notion trend boardNotionNotion

What it does

When an exit-interview Zoom meeting finishes recording, this workflow pulls the transcript, uses an LLM to extract the departing employee's stated reasons for leaving, strips every name and identifying detail, and appends the de-identified drivers to a Notion trend board. Over time the board shows which attrition themes (compensation, manager, growth, burnout) are rising or falling — without ever exposing who said what.

When to use it

Run this when you conduct exit interviews over Zoom and want aggregate insight into why people leave, but legal or trust concerns mean you cannot attribute quotes to individuals. It replaces manual note-typing and protects confidentiality by design.

How it works

  1. 1A Zoom recording-completed event fires for a meeting tagged as an exit interview.
  2. 2The transcript is fetched from Zoom.
  3. 3An LLM categorizes the conversation into standard attrition-driver buckets and rewrites findings as anonymous, non-identifying statements.
  4. 4A guard step rejects the output if any name, team, or unique detail survives, sending it back for another redaction pass.
  5. 5Clean, themed drivers are appended to the Notion trend board with a date and department-level tag only.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect ZoomMeetings, recordings, transcripts.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect NotionPages, databases, comments.
  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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