CUSTOMER SUPPORT

RMA Edge-Case Slack Approval Loop

When a return request falls just outside the warranty window or fails an automated check, the bot posts the case to Slack for an agent's one-click approve or deny.

CategoryCustomer Support
Enginesim
Difficultyintermediate
Triggerevent
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerReturn request fails automatic eligibility check
  • ActionPull purchase record and escalation reason from PostgresPostgreSQLPostgres
  • LogicFormat edge case with out-of-policy context
  • ActionPost approve/deny request to Slack channelSlack
  • ActionWrite approved RMA or denial to PostgresPostgreSQLPostgres
  • OutputPost final decision and RMA number to SlackSlack

What it does

Handles the gray-area returns that should not be fully automated. When a customer's request is borderline — slightly past the window, a missing serial, or a high-value item — the bot escalates it to a Slack channel with the full context and approve/deny buttons, then issues or rejects the RMA based on the agent's decision.

When to use it

Use this when blanket automation is too rigid but full manual handling is too slow. It keeps clear-cut cases automated elsewhere while giving agents fast, contextual control over judgment calls without leaving Slack.

How it works

  1. 1A return request that fails the automatic eligibility rule enters the workflow.
  2. 2The bot gathers the purchase record and reason for escalation from Postgres.
  3. 3A logic step formats the edge case with customer, order, and out-of-policy details.
  4. 4It posts an interactive message to a Slack channel with approve and deny actions.
  5. 5On the agent's response, the bot either writes an approved RMA record to Postgres or records a denial.
  6. 6The decision and any RMA number are delivered back to the requesting channel.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect PostgresAny Postgres URL — query, write, migrate.
  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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