customer_support · workflow
Assembled AI chat agent handles damaged-item claims, WISMO, and subscription changes end-to-end for Lulu and Georgia and Thrasio
Support teams were overwhelmed by repetitive, high-volume workflows — damaged item claims, order tracking inquiries, and subscription changes — requiring slow, manual agent effort and resulting in inconsistent handling.
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 · Customer inquiry arrives
Customer inquiries such as order status requests flood support queues, especially during peak periods.
Tools used
Assembled's AI chat agentAssembled's WFM
Outcome
Assembled's AI chat agent now handles damaged item claims, WISMO, and subscription changes end-to-end, freeing agents for high-value moments, reducing cancellations, and delivering agile staffing with preserved CSAT for Thrasio during Prime Day spikes.
What failed first
Legacy chatbots failed by being limited to scripts, trapping customers in loops, and leaving support teams to clean up the resulting mess rather than resolving issues end-to-end.
Results
Time savedmore agent time for high-emotion moments
Volumefewer cancellations
Grounding & classification
Source type: platform led case
35 fields verified against source quotes.
agentic workflowai agentconversational aiknowledge searchsentiment analysissupport agentknowledge basesupport ticketnamed customerproduction runtime claimedtools describedvendor confirmedworkflow describedecommerceretailcustomer satisfactiondeflection rateemployee productivityresolution time reductiontime savedplatform led caseappointment schedulingcustomer supportecommerce opsorder processingticket triageautonomous resolutionescalation workflowextract classify route