CUSTOMER SUPPORT
Daily Refund Edge-Case Digest for Supervisors
Runs each morning to scan the last 24 hours of refund requests across Front, classifies the borderline ones against policy.
How it runs
The automated pipeline, trigger to output.
- TriggerDaily morning schedule
- ActionPull last 24h refund conversations from FrontFront
- ActionScore each case against refund policyOpenAI
- LogicFilter: keep only edge cases
- OutputPost prioritized digest to SlackSlack
What it does
It produces one daily roundup of refund cases that deserve a human second look. Instead of supervisors hunting through inboxes, they open a single Slack digest ranked by risk, each item carrying the customer context and the specific policy concern.
When to use it
Use it when refund volume is high enough that real-time flagging would be noisy, but edge cases still slip through. A once-a-day digest gives supervisors a focused review queue without a constant stream of pings.
How it works
- 1A scheduled trigger runs every morning at a set time.
- 2The flow pulls the last 24 hours of refund-tagged conversations from Front.
- 3An OpenAI step reviews each one against your refund policy and scores it as routine or edge case.
- 4A logic filter drops routine cases and keeps only those needing review.
- 5The kept cases are sorted by risk and summarized into a single digest with links back to each Front conversation.
- 6The digest is posted to a Slack supervisor channel to start the day's review queue.
Set it up
What you configure once, before turning it on.
- 1Connect FrontShared inbox, conversations.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect SlackChannels, DMs, threads, mentions.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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