CUSTOMER SUPPORT

Predict Front SLA breaches and page on-call before deadline

Scans open Front conversations on a schedule, projects which ones will miss their reply SLA based on time remaining and queue velocity.

CategoryCustomer Support
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerEvery 5 minutes (schedule)
  • ActionFetch open conversations with SLA timersFront
  • LogicCompute time-to-breach, flag at-risk
  • LogicDedupe against already-paged tickets
  • ActionOpen PagerDuty incident per at-risk ticketPagerDutyPagerDuty
  • OutputPost heads-up to Slack channelSlack

What it does

Instead of alerting after an SLA is already blown, this workflow looks ahead. Every few minutes it pulls open Front conversations, computes time-to-breach against each conversation's SLA target, and triggers a PagerDuty incident only for the ones projected to miss the deadline while there is still time to act.

When to use it

Run this when your support team has firm reply or resolution SLAs (for example, 4-hour first response on priority tickets) and you want on-call paged in the pre-breach window rather than after a miss has already hurt your numbers.

How it works

  1. 1A schedule trigger fires every 5 minutes.
  2. 2Front is queried for all open, unassigned-or-pending conversations with their SLA timer state.
  3. 3A logic step computes minutes-to-breach and flags any conversation inside the danger threshold (e.g. under 30 minutes remaining and no agent reply yet).
  4. 4Flagged conversations are deduped against ones already paged this cycle.
  5. 5PagerDuty creates or updates an incident per at-risk conversation, with the deep link and time remaining.
  6. 6The incident summary is posted to the team Slack channel as a heads-up.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect FrontShared inbox, conversations.
  2. 2
    Connect PagerDutyIncidents, on-call, escalations.
  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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