CUSTOMER SUPPORT

Cluster Unanswered Intercom Questions into Draft Help Articles

Scans the past week of Intercom conversations for questions agents marked unresolved or had no canned answer for, clusters the recurring ones.

CategoryCustomer Support
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWeekly schedule fires
  • ActionPull last 7 days of closed Intercom conversationsIntercomIntercom
  • LogicKeep only questions with no saved reply or gap tag
  • ActionCluster similar questions and drop clusters seen <3 timesOpenAI
  • ActionDraft a help article for each surviving clusterOpenAI
  • OutputPost drafts to Slack review channelSlack

What it does

Every week this workflow pulls closed Intercom conversations, isolates the customer questions that had no matching saved reply or were tagged as a knowledge gap, and groups semantically similar questions together. Clusters that recur three or more times become candidate articles. For each one it drafts a help-center article (title, summary, step-by-step answer) and posts it to a reviewer for sign-off before anything is published.

When to use it

Use it when your support team keeps answering the same off-script questions by hand and your help center never catches up. It turns the noise of repeated tickets into a prioritized backlog of articles worth writing, ranked by how often each gap actually appeared.

How it works

  1. 1A weekly schedule fires the run.
  2. 2Pull the last 7 days of closed Intercom conversations and their first customer message.
  3. 3Filter to conversations with no linked saved reply or a "knowledge-gap" tag.
  4. 4Use OpenAI to cluster the questions and keep clusters seen 3+ times.
  5. 5For each surviving cluster, draft an article with title, summary, and answer.
  6. 6Post each draft to a Slack review channel with approve/edit buttons.

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 SlackChannels, DMs, threads, mentions.
  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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