CUSTOMER SUPPORT

Route Angry Front Threads to a Save Specialist

Scores the sentiment of every inbound Front reply in real time and, when a thread turns hostile or threatens cancellation, reassigns it to a named save specialist and pings them…

CategoryCustomer Support
Enginesim
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew inbound reply on Front conversationFront
  • ActionClassify sentiment and anger score with OpenAIOpenAI
  • LogicBranch: anger score over threshold or cancel intent
  • ActionReassign conversation to save specialist and tag at-riskFront
  • OutputPost thread link and quote to Slack save channelSlack

What it does

Watches a Front shared inbox for new customer replies, runs each one through an OpenAI sentiment classifier, and intercepts threads that spike into anger, frustration, or cancellation intent. Those threads are pulled out of the general queue, assigned to a designated save specialist, tagged `at-risk`, and surfaced in a Slack channel with the offending quote so a human can respond within minutes instead of waiting in line.

When to use it

Use it when your shared inbox mixes routine questions with the occasional furious customer, and those high-stakes threads get buried behind dozens of ordinary tickets. Ideal for support teams that have one or two strong de-escalation people you want angry threads to land on automatically.

How it works

  1. 1A new inbound message arrives on a Front conversation and fires the trigger.
  2. 2OpenAI classifies the message into a sentiment label and a 0-100 anger score.
  3. 3A branch checks whether the score crosses your anger threshold or contains cancel/refund language.
  4. 4If it does, Front reassigns the conversation to the save specialist and applies an `at-risk` tag.
  5. 5Slack posts the conversation link plus the triggering quote to the save channel for immediate pickup.

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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