Refund policy: under $250 auto-approve, $250–$1,000 manager sign-off, over $1,000 ops director.
You're viewing a read-only demo workspace · sample data only
Your live brain runs against your sources, your rules, your people
Acme Refunds Co's brain — read it like the AI does.
This is what your workspace looks like once it's populated: sources indexed, rules drafted from your data and approved, chat that cites every claim, a challenger that flags the high-stakes calls, and an approval queue for autonomous AI tool calls. Click a citation chip — every fact traces back to a source you uploaded. Ask the brain a question — the demo returns the same answer shape your live brain will.
Five sources · 2,837 facts indexed
Each connected app or uploaded handbook gets parsed into structured facts the AI can retrieve. Re-syncs are clean — existing facts get refreshed in place, not duplicated.
118 threads about refund decisions, including the 2 escalations on lawsuit threats this month.
Resolved tickets with refund outcomes — paired ticket → resolution shape used by the strategist.
Escalation paths, voice/tone do-and-don't, the 3-strikes rule for refund denials.
Threaded conversations with high-value customers — pricing exceptions, retention saves, churn signals.
Four rules drafted from those sources · re-verified daily
Two shapes: plain policies (refund thresholds, hours, voice) and step-by-step playbooks (pricing exceptions, incident response). Confidence is the verifier's match against the cited facts. Anything below 0.7 gets paused and routed to the owner.
- ·Under $250: auto-approve, log in /admin/refund-log, send standard apology + refund-issued email.
- ·$250 – $1,000: route to a Customer Success Manager. Rep adds context. Manager has 2 business hours to decide.
- ·Over $1,000 OR mention of lawsuit, attorney, regulator, or 'small claims': route to ops director. Stop using AI to draft the response — escalate to human conversation.
- ·Step 1: Confirm the customer's annual run-rate from Stripe. Quote-time discount cap = 15% if run-rate > $20k, 10% otherwise.
- ·Step 2: If they ask for a multi-year deal, tag the manager — those need legal review and we don't draft the LOI from chat.
- ·Step 3: Log the exception in #pricing-log with the deal slug. Quarterly we audit for drift.
- ·Step 4: If the discount exceeds the cap, the deal goes to the founder via email — never via Slack DM.
- ·Trigger phrases: 'lawyer', 'attorney', 'small claims', 'BBB', 'fraud', 'I'll go public', 'YouTube', 'Reddit'.
- ·When matched, the AI never composes an outbound reply. It posts the thread + the trigger to #escalations and pings the on-call CSM.
- ·Owner gets a daily digest of these — 0 escalations in a week is itself a signal we audit.
- ·Reply hours: 7am–7pm Pacific, Mon–Fri. Off-hours auto-reply names the next available human window — never says 'we'll get back to you soon.'
- ·Voice: warm, direct, short sentences. No 'we apologize for any inconvenience this may have caused' filler. Reference the customer's order # in every reply.
Try a question. Every claim cites a source.
Five canned questions — pick one, watch the brain pull rules + facts and answer. Citations are live: click a chip to see the rule it traces back to. Your real workspace handles open-ended prompts; this demo is read-only.
Can I refund $400 without a manager?
No. Refunds between $250 and $1,000 require a Customer Success Manager sign-off per the tiered approval policy. You'll attach context to the ticket, the manager has 2 business hours to decide. Auto-approval only applies under $250.
In your workspace, citations open the actual source — the Slack thread, the ticket, the handbook page — at the timestamp the fact was extracted.
A second AI re-reads the answer before the customer sees it.
On high-stakes turns — refunds, lawsuits, hires, big-dollar amounts — a second AI runs the prompt with an adversarial system prompt and flags what the primary missed. Verdict blocked means the AI does not respond; the work goes to a human.
Customer with 11-month tenure asks for a $1,400 refund — order arrived smashed, photos attached.
- ·Issue the refund in full. The order was damaged in transit and the customer has been with us nearly a year — retention math wins. Keep the response warm but tight.
- ·Apology + refund-issued + send a $25 credit toward a future order as a goodwill gesture (within the auto-approve gift threshold per the playbook).
- ·Reply ETA: under 2 business hours. Tag #refunds with the order #.
- !Hold on — $1,400 is over the $1,000 threshold in the tiered-approval rule. This isn't an auto-approve. The rule says route to the ops director, not draft a response.
- !The damaged-in-transit pattern matched 4 similar tickets this month — recommend pulling the carrier's claim path before issuing the refund to recover cost. The Zendesk source has the carrier-claim template.
- !The $25 credit is fine in concept, but combined with $1,400 above-threshold this proposal needs a human ops director to sign off, not the auto-approval flow.
Autonomous AI tool calls go here before they fire.
When an AI agent has the keys to issue a refund, send an email, or change a price, the proposed action lands here first. Risk-scored. Auto-approved when the policy allows; manager-reviewed when it doesn't. Blocked + escalated when challenger says no.
| Actor | Proposed action | Risk | Age | Status |
|---|---|---|---|---|
| Customer Success Rep (Sarah · clone) | Send refund-issued email — $187 · order #ACME-44218 | risk 0.21 | 4 minutes | auto-approved |
| Sales Rep (AI) | Reply to inbound — pricing exception 12% on $32k annual | risk 0.62 | 11 minutes | pending — manager review |
| Customer Service Rep (AI) | Issue refund — $1,400 above-threshold (held by challenger) | risk 0.95 | 23 minutes | blocked — ops director paged |
| Customer Success Rep (AI) | Send churn-save email — $499/mo subscription, 18-month tenure | risk 0.34 | 1 hour | pending — manager review |
Yours runs against your sources, your rules, your people.
We onboard every workspace with a hands-on call so day one ends with rules live, citations clicking, and your team using it.