TICKET MANAGEMENT

Flag reopened Front tickets whose sentiment turns negative

When a resolved Front conversation is reopened, scores the new customer reply for sentiment and effort.

CategoryTicket Management
Enginesim
Difficultyintermediate
Triggerevent
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerFront conversation reopenedFront
  • ActionFetch new reply + prior close sentimentPostgreSQLPostgres
  • ActionScore sentiment and effort with OpenAIOpenAI
  • LogicDid sentiment flip to negative?
  • OutputAlert support channel in SlackSlack

What it does

Catches the conversations that quietly go bad. A ticket you already closed gets reopened, the customer fires off a frustrated reply, and it lands at the back of the queue like any other message. This workflow watches for reopens, compares the new reply's tone against how the ticket felt at close, and raises a flag the moment a satisfied resolution turns sour.

When to use it

Run it on any Front inbox where reopens are common and a repeat failure is far more damaging than a first contact. Ideal for support leads who want to intercept churn-risk threads before they sit unanswered.

How it works

  1. 1Front emits a conversation-reopened event via webhook.
  2. 2The flow pulls the latest inbound message plus the sentiment recorded at the previous close from Postgres.
  3. 3OpenAI scores the new reply for sentiment and customer-effort signals.
  4. 4A logic step checks whether sentiment dropped from neutral/positive to negative.
  5. 5If it flipped, an alert with the diff and a deep link goes to the support Slack channel; otherwise the run ends quietly.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect FrontShared inbox, conversations.
  2. 2
    Connect PostgresAny Postgres URL — query, write, migrate.
  3. 3
    Connect OpenAIModels, embeddings, files.
  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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