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Case Study: Flex doubles CSAT and cuts resolution time 50% during 4x rent-week spikes with Lorikeet

Flex Case StudySourced & dated by Case Study Desk
Key facts · TL;DR
Company
Flex
Industry
Fintech
Challenge
Accuracy-critical support spikes during rent-payment periods
Headline result
Flex doubled CSAT versus its previous support tool, handled 4x chat volume during rent week, and cut median conversation duration to resolution by 50% with Lorikeet.

Key results

2x
CSAT vs. previous support tool
4x
Chat volume during rent week
vs. rest of the month
50%
Lower median resolution time
conversation duration to resolution

The challenge

Flex helps consumers pay rent on time, driving high-volume support requests concentrated around rent-payment periods with strict accuracy requirements. Its previous AI tool could not match the policy precision and contextual understanding needed for housing-security-critical issues.

The solution

Flex deployed Lorikeet's AI agents with deep integration into its APIs, trained on company policies and workflows and configured with strict escalation logic for high-risk tickets.

We tested AI solutions head-to-head and Lorikeet was a winner in every metric.

LB
Lindsay Boland
Customer Service AI Product Lead, Flex

The results, in context

Flex reports doubling CSAT compared with its previous support tool, absorbing 4x the chat volume during rent week versus the rest of the month, and a 50% decrease in median conversation duration to resolution, freeing human agents for more complex cases.

Products used

Lorikeet Lorikeet AI Agent