SOCIAL MEDIA

Classify creator email replies as consent granted, denied, or unclear

Triggers on creator replies to permission requests, classifies the response as granted, denied, or needs-clarification, updates the Airtable consent status.

CategorySocial Media
Enginesim
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerCreator reply arrives in GmailGmailGmail
  • ActionClassify reply intent and conditionsOpenAI
  • LogicBranch on granted, denied, or unclear
  • ActionUpdate consent status on recordAirtableAirtable
  • OutputNotify team on a clear grantSlack

What it does

This workflow reads inbound replies to your rights requests and decides what the creator actually said. It classifies each reply as consent granted, denied, or ambiguous, matches it back to the right clearance record, and updates the consent status. Clear grants trigger a Slack ping so the content can move toward reshare.

When to use it

Use it once outreach is live and replies are landing in your inbox. It removes the manual triage of "did they say yes?" and keeps your consent ledger trustworthy, which matters when legal asks for proof of permission.

How it works

  1. 1A new Gmail message in the rights-requests label triggers the run.
  2. 2OpenAI classifies the reply intent as Granted, Denied, or Needs Clarification and extracts any conditions the creator attached.
  3. 3A logic branch routes by classification.
  4. 4The matching Airtable row is updated with the new consent status and a quote of the relevant reply text for the audit trail.
  5. 5On a clear grant, a Slack message notifies the social team that the asset is cleared.
  6. 6Denied or unclear replies are flagged in Airtable for human follow-up.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect GmailRead, draft, send, label.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect AirtableBases, tables, views, automations.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  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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