TICKET MANAGEMENT

Intercom SLA burn-rate escalation before breach

Scans open Intercom conversations on a schedule, projects each ticket's time-to-breach from its current reply pace.

CategoryTicket Management
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerEvery 15 minutes
  • ActionFetch open Intercom conversations with SLAIntercomIntercom
  • LogicForecast time-to-breach from reply pace
  • LogicKeep only tickets projected to miss deadline
  • ActionReassign + tag predicted-breach in IntercomIntercomIntercom
  • OutputPost escalation summary to SlackSlack

What it does

Every 15 minutes this workflow reads open Intercom conversations with an active SLA, calculates how fast each one is burning through its remaining response budget, and flags the conversations whose projected resolution time exceeds the deadline. Predicted-to-breach tickets get reassigned to a senior agent and tagged so they jump the queue before the SLA timer actually expires.

When to use it

Use it when your support team only finds out about misses after the breach email lands. It moves the alarm earlier — surfacing at-risk tickets while there is still time to act, not after the damage is done.

How it works

  1. 1A schedule fires every 15 minutes.
  2. 2It pulls all open Intercom conversations that have an SLA attached, with timestamps and current state.
  3. 3A forecast step computes elapsed time vs. SLA target and projects time-to-resolution from each conversation's reply cadence.
  4. 4A branch keeps only conversations whose projected finish lands after the deadline with a configurable safety margin.
  5. 5For each at-risk conversation it reassigns to the senior queue and applies a "predicted-breach" tag in Intercom.
  6. 6A summary of escalated tickets is posted to the support channel.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect IntercomConversations, contacts, articles.
  2. 2
    Connect SlackChannels, DMs, threads, mentions.
  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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