CUSTOMER SUPPORT

Refund Eligibility Guardrail on Stripe Refunds

When a refund is issued in Stripe, it verifies the refund against your policy window and amount limits and posts any out-of-policy or oversized refund to Slack for supervisor…

CategoryCustomer Support
Enginesim
Difficultybeginner
Triggerwebhook
Steps5
Setup~5 min

How it runs

The automated pipeline, trigger to output.

  • TriggerStripe refund created (webhook)StripeStripe
  • ActionRetrieve charge date, amount, and initiatorStripeStripe
  • ActionCheck refund against policy window and limitsOpenAI
  • LogicBranch: compliant vs. out-of-policy
  • OutputFlag violations to Slack supervisor channelSlack

What it does

It watches refunds as they happen and checks each one against your refund rules — time since purchase, percentage refunded, and per-agent limits. Compliant refunds pass silently; anything outside the rules is surfaced immediately so a supervisor can question it before it becomes a pattern.

When to use it

Use it as a backstop when agents can issue refunds directly and you need an after-the-fact compliance check without slowing the team down. It catches the refund that was technically allowed in the tool but not by policy.

How it works

  1. 1A Stripe `refund.created` webhook triggers the flow with the refund and linked charge.
  2. 2The flow retrieves the original charge date, amount, and who initiated the refund from Stripe.
  3. 3An OpenAI step evaluates the refund against your written policy window and amount thresholds.
  4. 4A logic branch separates compliant refunds from out-of-policy or oversized ones.
  5. 5Compliant refunds are logged and the flow ends quietly.
  6. 6Violations are posted to a Slack supervisor channel with the charge details, the policy rule broken, and the agent who issued it.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect StripeCustomers, subscriptions, payments.
  2. 2
    Connect OpenAIModels, embeddings, files.
  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.

Run this workflow in your colony.

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