CUSTOMER SUPPORT

Refund Pre-Check Copilot for Support Agents in Chat

A chat agent that takes a customer email or order ID, validates refund eligibility against live Stripe charge and subscription data.

CategoryCustomer Support
Enginepaperclip
Difficultyintermediate
Triggerchat
Steps5
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerAgent sends refund question in chat
  • ActionResolve identifier to Stripe customer and chargesStripeStripe
  • ActionRetrieve subscription for prorationStripeStripe
  • LogicDecide full, prorated, or declined and draft rationaleOpenAI
  • OutputReturn recommendation and reply wording in chat

What it does

This is an interactive copilot for support agents. An agent drops a customer email or order ID into chat and asks whether a refund is allowed. The agent pulls the matching Stripe charge plus any active subscription, checks refund/dispute state, amount, and prorated subscription rules, then returns a recommended action: full refund, prorated refund, or decline — with a customer-ready explanation the agent can adapt.

When to use it

Use it for ad-hoc refund questions that don't start as a structured ticket — live chat, phone follow-ups, or escalations where the agent just needs a fast, policy-grounded answer and suggested wording.

How it works

  1. 1An agent sends a refund question with a customer identifier in chat.
  2. 2The agent resolves the identifier to the Stripe customer and active charges.
  3. 3It retrieves any subscription to compute prorated refund eligibility.
  4. 4Policy logic determines full, prorated, or declined and drafts the rationale.
  5. 5The recommendation and customer-ready wording are returned in the chat reply.

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
    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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