CUSTOMER SUPPORT

Spot incident spikes in Front and escalate to PagerDuty

Classifies inbound Front conversations by topic, and when several outage-related tickets cluster in a short window it raises a PagerDuty incident and broadcasts a heads-up…

CategoryCustomer Support
Enginesim
Difficultyadvanced
Triggerevent
Steps5
Setup~25 min

How it runs

The automated pipeline, trigger to output.

  • TriggerNew Front conversationFront
  • ActionClassify outage signal with OpenAIOpenAI
  • LogicIncident tickets exceed spike threshold?
  • ActionOpen PagerDuty incidentPagerDutyPagerDuty
  • OutputAlert engineering Slack channelSlack

What it does

Individual Front conversations are classified by an LLM, and outage or degradation reports are counted in a rolling window. When the count of incident-type tickets crosses a threshold within that window, the flow concludes a likely live incident is underway, opens a PagerDuty incident, and alerts the engineering channel so the team mobilizes before the queue floods.

When to use it

Use this when a real outage shows up first as a wave of customer messages rather than a monitoring alert. It turns clustered support signals into a paged incident automatically.

How it works

  1. 1A new Front conversation triggers the flow.
  2. 2OpenAI classifies the message and flags whether it describes an outage or degradation.
  3. 3A logic step increments a rolling count and checks it against the spike threshold.
  4. 4When the threshold is crossed, a PagerDuty incident is created with the matching tickets summarized.
  5. 5A heads-up with the incident link is posted to the engineering Slack channel.

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 PagerDutyIncidents, on-call, escalations.
  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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