Customer ops · Production

Payment support ticket automation — 70% resolved without human agent

The problem

Koralplay's support team was overwhelmed with payment-related support tickets — failed transactions, refund requests, payment confirmations. Most were repetitive queries requiring database lookups and templated responses.

First attempt

First version tried to auto-resolve all tickets — created errors on edge cases customers hadn't experienced before. Added a confidence threshold: only auto-resolve tickets above 90% pattern match confidence. Below that, route to human with enriched context.

Workflow diagram · grounded in source
1
Support ticket
Trigger
2
N8n
Classification + routing
3
Payment API
Data lookup
4
Customer DB
Context
5
Support platform
Resolution
Reported outcome

70% of payment support tickets resolved without a human agent.
Support team capacity freed for complex issues and relationship management. 'It's a virtuous circle. With n8n, the business moves forward, customers get a better experience, and teams can focus on work that really matters.' — Koralplay team.

Reported metrics
Time saved70% ticket deflection
VolumeThousands/month
Running sinceFeb 2025
Reported stack
n8nPayment APISupport platformCustomer DB
Source
Koralplay case study: Automates 70% of payment support tickets with n8n (n8n.io)
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

70% of payment support tickets resolved without a human agent.

What tools did this team use?

n8n, Payment API, Support platform, Customer DB.

What results were reported?

Time saved: 70% ticket deflection; Volume: Thousands/month; Running since: Feb 2025 (source-reported, not independently verified).

What failed first in this deployment?

First version tried to auto-resolve all tickets — created errors on edge cases customers hadn't experienced before.

How is this customer ops AI workflow structured?

Support ticket → N8n → Payment API → Customer DB → Support platform.