CUSTOMER SUPPORT

Front Macro-Gap Detector: Weekly Reply Clustering

Each week, pulls the agent replies your team typed by hand in Front, clusters the repetitive ones with an LLM.

CategoryCustomer Support
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWeekly schedule fires
  • ActionFetch last 7 days of outbound Front repliesFront
  • LogicDrop replies already sent from a macro; keep hand-typed
  • ActionCluster manual replies by intent and label each groupOpenAI
  • LogicKeep clusters with 5+ occurrences, rank by volume
  • OutputPost ranked macro candidates to Slack for claimingSlack

What it does

Finds the answers your agents keep retyping from scratch in Front and turns them into macro proposals. It scans the past week of outbound replies, ignores anything already sent from an existing macro, groups near-duplicate manual answers into themes, and surfaces the highest-volume clusters so you can decide which ones deserve a canned response.

When to use it

Run it when your saved-reply library has gone stale and agents are clearly hand-writing the same answers over and over. Good for support leads who want a data-backed, recurring nudge instead of guessing which macros to build.

How it works

  1. 1A weekly schedule fires the workflow.
  2. 2It pulls the last 7 days of outbound Front messages and filters out replies that were sent from an existing macro, keeping only hand-typed ones.
  3. 3An OpenAI step embeds and clusters the manual replies by intent, then drafts a one-line label and frequency count per cluster.
  4. 4A logic step keeps only clusters seen 5+ times, sorted by volume.
  5. 5The ranked candidate list is posted to a Slack channel where a teammate reacts to claim each one for macro creation.

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 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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