CUSTOMER SUPPORT

Auto-draft a README troubleshooting entry from clustered Intercom conversations

Watches resolved Intercom conversations, clusters recurring issues over a rolling window, and when a problem crosses a frequency threshold.

CategoryCustomer Support
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNightly schedule over rolling 7-day window
  • ActionFetch resolved conversations from IntercomIntercomIntercom
  • ActionCluster conversations by root cause and count occurrencesOpenAI
  • LogicKeep only clusters above recurrence threshold
  • ActionDraft titled troubleshooting section per clusterOpenAI
  • OutputCreate unpublished README draft for editor reviewReadMeReadMe

What it does

It turns repeat support pain into documentation. The workflow groups recently resolved Intercom conversations by root cause, and when the same issue shows up often enough to matter, it writes a clean troubleshooting entry and stages it in README as an unpublished draft for a human to approve.

When to use it

Use it when the same questions keep landing in your inbox and your docs never quite catch up. Good for support teams who want self-serve deflection without manually mining tickets every week.

How it works

  1. 1A schedule fires on a rolling window (e.g. nightly over the last 7 days).
  2. 2It pulls resolved conversations from Intercom for that window.
  3. 3An OpenAI step clusters them by underlying issue and counts occurrences.
  4. 4A logic gate keeps only clusters above the recurrence threshold.
  5. 5For each qualifying cluster, OpenAI drafts a titled troubleshooting section with symptoms, cause, and fix steps.
  6. 6The draft is created as an unpublished page in README, ready for editor review.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect IntercomConversations, contacts, articles.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect ReadMeAPI docs, changelog, auth.
  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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