CUSTOMER SUPPORT

Intercom warm-touch when a customer's background jobs keep failing

Runs on a schedule, finds customers whose background jobs have repeatedly failed in the app database.

CategoryCustomer Support
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerSchedule: every few hours
  • ActionAggregate failed jobs per customer in PostgresPostgreSQLPostgres
  • LogicClassify failure type, drop accounts with open tickets
  • ActionLook up owner contact + planPostgreSQLPostgres
  • OutputOpen proactive Intercom conversationIntercomIntercom
  • ActionPost batch summary to SlackSlack

What it does

Queries the application database on a cadence for accounts with a cluster of failed jobs (imports, syncs, exports) in a rolling window, then proactively opens an Intercom conversation with an offer to help — catching slow-burn problems that never spike loudly enough to trip a log alert.

When to use it

Best for failures that accumulate quietly: a customer whose nightly sync has silently failed three days running, or whose CSV imports keep erroring on a bad column. Use it when the pain builds gradually rather than all at once.

How it works

  1. 1A schedule trigger runs the check every few hours.
  2. 2Postgres aggregates failed jobs per customer over the rolling window and returns accounts above the failure count.
  3. 3A logic step classifies each by failure type to pick the right message variant and excludes accounts with an open ticket already.
  4. 4Postgres looks up the owner contact and plan tier.
  5. 5Intercom opens a proactive conversation with a type-specific message and a one-click reply to escalate.
  6. 6A Slack note posts the batch to the support channel so an agent can watch for replies.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect PostgresAny Postgres URL — query, write, migrate.
  2. 2
    Connect IntercomConversations, contacts, articles.
  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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