Customer support · Production

Replify AI handles over 5,000 calls and saves 178 staff hours for SWEAT440 fitness franchise

The problem

SWEAT440's front desk-less model kept instructors on the training floor at all times, making in-studio call handling impossible. As the franchise expanded to 25 locations with over 120 more in development, routine question volume grew across the network, the contracted call center became costly, and response consistency varied across studios.

First attempt

The contracted call center could not scale cost-effectively with the growing franchise; costs rose and multi-unit franchisees felt the impact as operations expanded, until the system's limits were exposed.

Workflow diagram · grounded in source
1
Member contacts via channel
trigger
“answers member questions, resolves routine requests, captures leads, and runs follow ups across phone, text, chat, and email”
2
AI answers from knowledge base
ai_action
“The platform handles common questions about hours, schedules, policies, and membership details”
3
Route complex issues to staff
routing
“forwarding complex issues to staff”
4
Staff handles escalated request
human_review
“The connection supported clean handoffs for billing questions, class access issues, or concerns that required a manager”
Reported outcome

In the first six weeks, Replify AI handled over 5,000 calls and saved 178 staff hours.
Support costs fell by roughly 53%, instructors remained focused on coaching, and SWEAT440 gained a unified support layer prepared for rapid network expansion.

Reported metrics
Calls handled (first six weeks)over 5,000
Staff hours saved178
Support costsroughly 53%
Reported stack
ReplifyZoom
Source
https://www.replify.ai/case-studies/sweat440-uses-ai-scale-franchisor-to-franchisee-location-customer-service-sales-development
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

In the first six weeks, Replify AI handled over 5,000 calls and saved 178 staff hours.

What tools did this team use?

Replify, Zoom.

What results were reported?

Calls handled (first six weeks): over 5,000; Staff hours saved: 178; Support costs: roughly 53% (source-reported, not independently verified).

What failed first in this deployment?

The contracted call center could not scale cost-effectively with the growing franchise; costs rose and multi-unit franchisees felt the impact as operations expanded, until the system's limits were exposed.

How is this customer support AI workflow structured?

Member contacts via channel → AI answers from knowledge base → Route complex issues to staff → Staff handles escalated request.