customer_support · workflow
Otter.ai auto-resolves 1,000+ support tickets and doubles CX team efficiency with Zapier and ChatGPT
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.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Low-signal reply reopens ticket
Support tickets are reopened when customers reply with low-signal messages such as "thanks."
Tools used
ZapierChatGPT · partnerZendesk · partner
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.
What failed first
Before automation, agents had to manually review and close thousands of unnecessary reopened tickets.
Results
Time savedfreeing up agent time
Volume1,000+
Grounding & classification
Source type: vendor customer story
28 fields verified against source quotes.
document classificationsupport agentsupport ticketfailure mode describedmetric backednamed customerproduction runtime claimedtools describedvendor confirmedworkflow describedsoftwareautomation ratedeflection rateemployee productivityresolution time reductionvendor customer storycustomer supportticket triageautonomous resolutionextract classify route