CUSTOMER SUPPORT

Front SLA risk ledger to Postgres with trend alerts

On a schedule, records every open Front conversation's predicted SLA risk into a Postgres ledger.

CategoryCustomer Support
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerEvery 15 minutes (schedule)
  • ActionList open Front conversations with deadlinesFront
  • LogicScore and tally breach-risk buckets
  • ActionInsert snapshot into Postgres ledgerPostgreSQLPostgres
  • LogicCompare recent snapshots for rising trend
  • OutputPost staffing alert to Slack if trending upSlack

What it does

Builds a time-series record of SLA pressure instead of just reacting to single threads. Each run snapshots all open conversations, scores each one's breach risk, and writes the rollup into a Postgres table. It then compares the latest snapshot against the prior few to detect a rising backlog and warns the team while there's still time to add coverage.

When to use it

Use it when individual reassignments aren't enough and you need to see whether the inbox is structurally falling behind — a surge, an understaffed shift, or a creeping backlog that one-off handoffs won't fix.

How it works

  1. 1A schedule fires every 15 minutes.
  2. 2List open Front conversations with their SLA deadlines.
  3. 3Score each as breaching-soon, at-risk, or healthy and tally the buckets.
  4. 4Insert the tallied snapshot (timestamp, inbox, counts) into the Postgres ledger.
  5. 5Query the last several snapshots; branch if breaching-soon count is trending up across them.
  6. 6When the trend is rising, post a staffing alert to Slack with the slope and current backlog.

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 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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