Beta Results · Illustrative Scenarios

What actually happens when rules replace guesswork

We're early-stage. We don't have 500 enterprise case studies. What we do have is honest data from our internal beta testing and real scenarios that reflect what Shopify DTC teams deal with every week.

Internal Beta Metrics

Internal Beta · 60-Day Test
847 complaint emails processed
91s median time from email open to action confirmed
76% of complaint emails matched a SOP rule on first analysis
0 duplicate order cancellations (idempotency held across 847 sessions)

Data from CS-AI Agent internal beta testing on a real Shopify store. Not independently audited.

Scenario 01

The angry refund email at 11pm on a Friday

Before Without CS-AI Agent

Customer: mia.chen@example.com Friday 11:14 PM
"I ordered this 3 weeks ago and it STILL hasn't arrived. I want a full refund RIGHT NOW. This is the worst online shopping experience I've ever had. If I don't hear back tonight I'm disputing with my bank."
11:15 PM Agent sees email, doesn't know if order is eligible for refund
11:17 PM Opens Shopify admin in another tab, searches for customer name
11:21 PM Finds order, checks status (UNFULFILLED), tries to remember refund policy
11:24 PM Writes response manually, decides to approve refund from memory
11:28 PM Sends reply. No record of who made the decision or why.
Total time: ~14 minutes  ·  Policy compliance: Unknown — agent recalled rules from memory  ·  Audit trail: None

After With CS-AI Agent

Customer: mia.chen@example.com Friday 11:14 PM
Same email. Same situation. Different outcome.
11:14 PM Agent opens email. Gmail sidebar activates automatically.
+3 sec System fetches Order #4821 from Shopify: UNFULFILLED · $280
+6 sec Intent: REFUND · Sentiment: ANGRY · Risk: HIGH — rule cancel_unfulfilled_order matched
+9 sec Draft reply generated in Gmail. Pending action displayed: Cancel Order #4821
11:16 PM Agent reviews draft, clicks "Confirm & Execute." Order cancelled. Note logged: [CS-AI Agent] Cancelled by agent@brand.com — rule: cancel_unfulfilled_order
Total time: ~90 seconds  ·  Policy compliance: 100% — code-enforced, not memory  ·  Audit trail: Full — agent ID + rule ID + order ID + timestamp
Scenario 02

The complaint that's really a PR crisis in disguise

Not every complaint is about a refund. Some emails carry a different kind of risk: the customer who threatens a 1-star review on every platform, the one who mentions "my 50,000 followers," or the one who escalates from polite to furious in the same thread.

Without a system, these look the same as every other complaint in the queue. They get handled in queue order — which means the most dangerous email often waits the longest.

What CS-AI Agent sees when it opens this email:

"Your product is garbage and I'm going to make sure everyone on TikTok knows it. I have 40,000 followers and I post daily. Fix this TODAY or I'm going public."
Intent COMPLAINT / ESCALATION THREAT
Sentiment ANGRY 🔴
Escalation Risk HIGH — public reputation threat detected
SOP Match ⚠️ Escalation Review Rule — Human approval required
Draft Generated De-escalation tone · Acknowledges urgency · Offers resolution path
Without CS-AI Agent: Email sits in queue 4–8 hours. Negative review goes live before a response is sent. Chargeback filed. PR damage done.
With CS-AI Agent: High-risk flag surfaces email immediately. Agent responds within 30 minutes. De-escalation draft is already written. Complaint resolved before it goes public.
"

Why we built this instead of another AI chat widget

Most AI customer service tools solve the wrong problem. They make it faster to write a response. But the bottleneck in real Shopify DTC CS teams isn't writing speed — it's the 5 decisions that happen before the first word is typed:

  • Is this customer actually right?
  • What does our refund policy say about this order?
  • Is the order even cancellable at this point?
  • What's the right tone given how angry they are?
  • Who approved this, and can I prove it if there's a dispute?

These aren't writing problems. They're judgment problems. And judgment problems need rules, not suggestions.

CS-AI Agent puts your rules in the system first, then lets AI handle the drafting. Not the other way around.

— The CS-AI Agent Team

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