TICKET MANAGEMENT

Auto-merge near-duplicate Zendesk tickets on creation

When a new Zendesk ticket is created, embed its subject and body, search recent open tickets for a close semantic match, and if confidence is high.

CategoryTicket Management
Enginesim
Difficultyintermediate
Triggerevent
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew Zendesk ticket createdZendeskZendesk
  • ActionEmbed subject + first commentOpenAI
  • ActionVector similarity search over recent open ticketsPostgreSQLPostgres
  • LogicTop match score >= 0.92?
  • ActionMerge new ticket into originalZendeskZendesk
  • OutputNotify assigned agent in SlackSlack

What it does

Catches duplicate support tickets at the moment they are created, before two agents start working the same issue. It compares each new ticket against recently opened ones and, when it finds a strong match, merges the duplicate into the original and leaves a clean audit trail.

When to use it

Use it on high-volume inboxes where the same outage or bug spawns dozens of near-identical tickets in minutes. It keeps the queue clean and prevents duplicate replies to the same customer or topic.

How it works

  1. 1A new ticket created in Zendesk fires the trigger.
  2. 2OpenAI generates an embedding from the subject and first comment.
  3. 3Postgres runs a vector similarity search against embeddings of open tickets from the last 72 hours.
  4. 4A logic step checks the top match score against a 0.92 threshold; below it, the flow exits and the ticket stays untouched.
  5. 5Above threshold, Zendesk merges the new ticket into the matched original, copying the requester comment.
  6. 6A Slack message notifies the assigned agent with both ticket links and the match score.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect ZendeskTickets, queues, knowledge base.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Connect PostgresAny Postgres URL — query, write, migrate.
  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.

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