TICKET MANAGEMENT

Nightly Duplicate-Cluster Digest for Front Inboxes

On a nightly schedule it pulls the day's open Front conversations, fuzzy-clusters them by similarity.

CategoryTicket Management
Enginesim
Difficultyintermediate
Triggerschedule
Steps7
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNightly schedule fires
  • ActionList today's open Front conversationsFront
  • ActionEmbed each conversation with OpenAIOpenAI
  • ActionCluster embeddings by similarity in PostgresPostgreSQLPostgres
  • LogicDrop singleton clusters
  • ActionLabel and recommend merges per clusterOpenAI
  • OutputEmail ranked cluster digest to support leadGmailGmail

What it does

Once a night this workflow groups the day's open Front conversations into clusters of likely duplicates and delivers a single ranked digest. Instead of catching duplicates one at a time, the support lead reviews every suspected merge group in a single morning sweep.

When to use it

Use it when real-time alerts would be too noisy and you'd rather batch-review duplicates. It is ideal for teams that do a daily inbox grooming pass and want clusters, not individual pings.

How it works

  1. 1A nightly schedule trigger fires after business hours.
  2. 2The workflow lists all open conversations from the target Front inboxes for the day.
  3. 3OpenAI embeds each conversation's subject and opening body.
  4. 4The embeddings are written to Postgres, where a similarity query clusters conversations into candidate groups above a confidence cutoff.
  5. 5A filter drops singleton clusters so only real duplicate groups remain.
  6. 6OpenAI drafts a short label and merge recommendation for each cluster.
  7. 7The digest is emailed to the support lead with conversation links grouped by cluster and ordered by cluster size.

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 PostgresAny Postgres URL — query, write, migrate.
  4. 4
    Connect GmailRead, draft, send, label.
  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.

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