MARKETING

Inbound Blocklist Bounce Investigator

When a delivery-failure email lands in a monitored Gmail postmaster inbox, it parses the bounce reason, classifies blocklist or reputation rejections with an LLM.

CategoryMarketing
EngineSim + Paperclip
Difficultyadvanced
Triggerevent
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew email in postmaster inboxGmailGmail
  • ActionRead full bounce body and headersGmailGmail
  • ActionClassify rejection with LLMOpenAI
  • LogicKeep only blocklist/reputation hits
  • ActionOpen Airtable investigation ticketAirtableAirtable
  • OutputEscalate true blocklist hits to SlackSlack

What it does

Providers bury the real reason a domain is being rejected inside cryptic bounce-back emails. This workflow reads those delivery-failure messages as they arrive, uses an LLM to classify whether the rejection is a true blocklist or reputation block versus a routine bounce, and routes only the reputation-threatening ones to a human.

When to use it

Use it when bounce notifications and postmaster replies land in a dedicated Gmail inbox and you want signal, not a flooded folder. It surfaces the rare but urgent 'you are on a blocklist' message that hides among ordinary mailbox-full bounces.

How it works

  1. 1A new email in the monitored Gmail postmaster inbox triggers the run.
  2. 2A Gmail read pulls the full bounce body and headers.
  3. 3An OpenAI step classifies the rejection type and extracts the provider and any named blocklist.
  4. 4A logic step keeps only true blocklist or reputation rejections.
  5. 5An Airtable ticket is created with the parsed provider, reason, and a remediation hint.
  6. 6A Slack escalation posts the high-severity hits so the team can start delisting immediately.

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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