Every week: pull from Zendesk, pull from Rippling, rebuild the Sheet — and start over when the data changed. Now a dashboard does it live.
A 335-person wellness company processing hundreds of support tickets daily across multiple locations, with compensation data in Rippling and ticket performance data in Zendesk living in entirely separate systems.
Answering whether higher-paid support agents actually resolved guest issues faster required manually pulling data from Zendesk and Rippling,...
“We were pulling from Zendesk, comparing it to Rippling, cross-referencing profiles, reformatting CSVs, and doing it all over again every single week.”
Wellness spa and bathhouse offering thermal pools, saunas, and bodywork treatments.
HR, payroll, and IT management platform for global workforce operations.
Bathhouse's Pay-performance analysis is part of this use case:
Related implementations across industries and use cases
Three locations, three silos—headcount was guesswork, reports came back blank. One typed query now replaces an hour of manual merging.
Comp data across five systems meant Taylor was a retrieval mechanism. One prompt built a live dashboard; four workflows run without her.
Managers spent days manually calling applicants. Now, AI screens and schedules instantly via text, keeping managers focused on guests.
Three locations, three silos—headcount was guesswork, reports came back blank. One typed query now replaces an hour of manual merging.
Comp data across five systems meant Taylor was a retrieval mechanism. One prompt built a live dashboard; four workflows run without her.
PAL's callers speak English, Tagalog, and Taglish in the same breath. A voice agent that keeps up replaced the IVR entirely.
Scattered data made AI features impossible to build. Unifying it unlocked search that understands traveler intent, not just keywords.
Generic AI training wouldn't reach workers across construction sites, studios, and sales floors. Each built a Custom GPT for their own role.
Recruiters juggled disconnected systems while managers fielded candidate calls from the back office. An AI now screens and engages 24/7.
Every week: pull from Zendesk, pull from Rippling, rebuild the Sheet — and start over when the data changed. Now a dashboard does it live.
A 335-person wellness company processing hundreds of support tickets daily across multiple locations, with compensation data in Rippling and ticket performance data in Zendesk living in entirely separate systems.
Answering whether higher-paid support agents actually resolved guest issues faster required manually pulling data from Zendesk and Rippling,...
“We were pulling from Zendesk, comparing it to Rippling, cross-referencing profiles, reformatting CSVs, and doing it all over again every single week.”
Wellness spa and bathhouse offering thermal pools, saunas, and bodywork treatments.
HR, payroll, and IT management platform for global workforce operations.
Bathhouse's Pay-performance analysis is part of this use case:
Related implementations across industries and use cases
Three locations, three silos—headcount was guesswork, reports came back blank. One typed query now replaces an hour of manual merging.
Comp data across five systems meant Taylor was a retrieval mechanism. One prompt built a live dashboard; four workflows run without her.
Managers spent days manually calling applicants. Now, AI screens and schedules instantly via text, keeping managers focused on guests.
Three locations, three silos—headcount was guesswork, reports came back blank. One typed query now replaces an hour of manual merging.
Comp data across five systems meant Taylor was a retrieval mechanism. One prompt built a live dashboard; four workflows run without her.
PAL's callers speak English, Tagalog, and Taglish in the same breath. A voice agent that keeps up replaced the IVR entirely.
Scattered data made AI features impossible to build. Unifying it unlocked search that understands traveler intent, not just keywords.
Generic AI training wouldn't reach workers across construction sites, studios, and sales floors. Each built a Custom GPT for their own role.
Recruiters juggled disconnected systems while managers fielded candidate calls from the back office. An AI now screens and engages 24/7.