CUSTOMER SUPPORT

Front Macro Gap Miner: Find Repeated Questions With No Macro

On a weekly schedule, this clusters Front conversations that had no good macro match, drafts proposed new macros for the top recurring themes.

CategoryCustomer Support
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWeekly schedule
  • ActionPull unmatched and low-confidence conversations from FrontFront
  • ActionCluster by intent and rank by volumeOpenAI
  • ActionDraft candidate macros for top clustersOpenAI
  • ActionFile proposed macros in Notion review databaseNotionNotion
  • OutputSend approval digest to SlackSlack

What it does

It mines a week of Front conversations to surface questions agents keep answering from scratch because no macro fits. It clusters the unmatched threads by topic, drafts a candidate macro for each high-frequency cluster, and routes the proposals for human approval — turning your support backlog into a self-improving macro library.

When to use it

Use it when your macro coverage is stale and agents free-type the same answers repeatedly. Run it as a recurring review so the library grows from real demand, not guesswork.

How it works

  1. 1A weekly schedule triggers the run.
  2. 2The past week's conversations flagged "needs-manual" or low-confidence are pulled from Front.
  3. 3OpenAI clusters them by intent and ranks clusters by volume.
  4. 4For each top cluster, the model drafts a reusable macro with variable placeholders.
  5. 5Proposed macros are written to a Notion review database with example threads attached.
  6. 6A Slack digest lists the proposals and their frequency for the lead to approve.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect FrontShared inbox, conversations.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect NotionPages, databases, comments.
  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.

Run this workflow in your colony.

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