CUSTOMER SUPPORT

Front Macro-Gap Detector: Monthly Coverage Gap Report

Monthly, measures what share of Front replies were hand-typed versus macro-sent, identifies the biggest uncovered topics with an LLM.

CategoryCustomer Support
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerMonthly schedule starts the run
  • ActionPull the month's outbound Front repliesFront
  • LogicSplit macro-sent vs manual; compute coverage ratio
  • ActionWrite narrative gap report with top uncovered topicsOpenAI
  • ActionSave full report to a Coda docCodaCoda
  • OutputPost headline summary and link to SlackSlack

What it does

Gives support leadership a monthly read on how well the macro library actually covers real conversations. It computes the manual-versus-macro reply ratio, names the topics driving the most uncovered manual work, and quantifies the time those gaps cost so you can prioritize what to build next.

When to use it

Use it for monthly QBRs or team reviews where you need evidence, not anecdotes, about where saved replies are falling short. Best for managers who report on deflection and agent efficiency.

How it works

  1. 1A monthly schedule starts the run.
  2. 2It pulls the month's outbound Front replies and splits them into macro-sent versus hand-typed.
  3. 3A logic step computes the coverage ratio and groups manual replies by theme.
  4. 4An OpenAI step writes the narrative report: top uncovered topics, estimated agent time spent, and recommended macros to create.
  5. 5The full report is saved to a Coda doc and a short headline summary is posted to Slack with a link.

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 CodaDocs, packs, 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.

Run this workflow in your colony.

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