Customer support · Production

Otter.ai auto-resolves 1,000+ support tickets and doubles CX team efficiency with Zapier and ChatGPT

The problem

Otter.ai's support queue accumulated unnecessary backlog from tickets reopened by low-signal customer replies, and the team had no scalable method to prioritize business-critical tickets without manual triage.

First attempt

Before automation, agents had to manually review and close thousands of unnecessary reopened tickets.

Workflow diagram · grounded in source
1
Low-signal reply reopens ticket
trigger
“Support tickets are often reopened when customers reply "thanks," creating an unnecessary backlog”
2
ChatGPT detects low-signal reply
ai_action
“Zapier and ChatGPT detect low-signal replies and auto-close tickets, logging internal notes in Zendesk”
3
AI analyzes and categorizes tickets
ai_action
“AI analyzes ticket content and domain, categorizing issues and flagging high-priority items automatically”
4
Enriched tickets routed to agents
routing
“More than 10,000 tickets were enriched and routed faster, speeding up response times for key users”
Reported outcome

Over 1,000 tickets were automatically resolved in three months, more than 10,000 tickets were enriched and routed faster, and CX team efficiency doubled.

Reported metrics
Auto-resolved support tickets1,000+
AI-prioritized tickets10,000+
CX team efficiency2X
Agent time freedfreeing up agent time
Show all 5 reported metrics
auto-resolved support tickets1,000+
AI-prioritized tickets10,000+
CX team efficiency2X
agent time freedfreeing up agent time
time and queue cleanlinesssaving us time and keeping our queue clean
Reported stack
ZapierChatGPTZendesk
Source
https://zapier.com/customer-stories/otter-ai
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Over 1,000 tickets were automatically resolved in three months, more than 10,000 tickets were enriched and routed faster, and CX team efficiency doubled.

What tools did this team use?

Zapier, ChatGPT, Zendesk.

What results were reported?

Auto-resolved support tickets: 1,000+; AI-prioritized tickets: 10,000+; CX team efficiency: 2X; Agent time freed: freeing up agent time (source-reported, not independently verified).

What failed first in this deployment?

Before automation, agents had to manually review and close thousands of unnecessary reopened tickets.

How is this customer support AI workflow structured?

Low-signal reply reopens ticket → ChatGPT detects low-signal reply → AI analyzes and categorizes tickets → Enriched tickets routed to agents.

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