TICKET MANAGEMENT

Detect Duplicate Intercom Conversations and Merge with Customer Notice

When a new Intercom conversation opens, finds an existing open conversation from the same customer about the same issue, merges them, and replies once so the customer knows their…

CategoryTicket Management
Enginesim
Difficultyintermediate
Triggerevent
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew Intercom conversation openedIntercomIntercom
  • ActionFetch contact's other open conversationsIntercomIntercom
  • ActionScore same-issue likelihood with OpenAIOpenAI
  • LogicOnly continue if duplicate confidence is high
  • ActionMerge new conversation into the originalIntercomIntercom
  • OutputReply once confirming the threads were combinedIntercomIntercom

What it does

Stops the same customer from having two parallel support threads. On every new Intercom conversation, it checks whether that contact already has an open conversation about the same topic, and if so merges the new one into the original and posts a single friendly reply explaining the consolidation.

When to use it

Use it when customers reply by email and accidentally start a fresh thread, or message twice in a panic. Best for teams drowning in split conversations that fragment context and inflate ticket counts.

How it works

  1. 1A new Intercom conversation triggers the flow.
  2. 2Fetch the contact's other open conversations from Intercom.
  3. 3Send both threads' text to OpenAI to judge whether they describe the same issue and return a confidence score.
  4. 4Branch: if confidence is high, proceed to merge; otherwise stop and leave the new thread untouched.
  5. 5Merge the new conversation into the oldest matching one via the Intercom API.
  6. 6Reply on the surviving thread letting the customer know their messages were combined and a teammate is on it.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect IntercomConversations, contacts, articles.
  2. 2
    Connect OpenAIModels, embeddings, files.
  3. 3
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  4. 4
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  5. 5
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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