CUSTOMER SUPPORT

Intercom Rage Detector with Front Senior Handoff

Detects rage signals in a live Intercom chat and hands the conversation to Front with a pre-drafted de-escalation reply waiting for a senior agent.

CategoryCustomer Support
Enginesim
Difficultyadvanced
Triggerevent
Steps6
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew Intercom conversation replyIntercomIntercom
  • ActionClassify rage and cancellation intentOpenAI
  • LogicBranch: rage signals detected?
  • ActionDraft de-escalation replyOpenAI
  • ActionOpen Front conversation with transcriptFront
  • OutputAssign Front thread to senior recovery inboxFront

What it does

Monitors live Intercom conversations for rage signals such as all-caps, repeated punctuation, threats to cancel, or profanity. When it trips, it transcribes the chat into a Front conversation and attaches a pre-written de-escalation reply, moving the upset customer from a bot or junior queue to a senior human fast.

When to use it

Use it when your front-line chat runs in Intercom but your senior recovery team works escalations in Front. It bridges the two so angry chats don't sit waiting for a bot to give up.

How it works

  1. 1A new Intercom conversation reply fires the trigger.
  2. 2An LLM classifies the message for rage and cancellation-intent signals.
  3. 3A branch lets calm chats continue in Intercom and routes only rage-flagged ones onward.
  4. 4The model drafts a de-escalation reply tuned to the customer's specific complaint.
  5. 5A Front conversation is opened with the transcript and the draft attached.
  6. 6The Front thread is assigned to the senior recovery inbox for approval and send.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect IntercomConversations, contacts, articles.
  2. 2
    Connect FrontShared inbox, conversations.
  3. 3
    Connect OpenAIModels, embeddings, files.
  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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