Call center ai · Production

Retell AI automates 8,000+ monthly calls for Matic Insurance with 85–90% transfer success rate

The problem

Matic's call operations faced mounting inefficiency across 120,000 monthly calls: after-hours calls were dropped or mishandled by third-party vendors, agents spent 7–9 minutes per call on repetitive data collection before any consultative work, and scheduling delays caused missed appointments and lost high-intent leads.

First attempt

Third-party call center vendors handling after-hours calls delivered poor performance, with volume constraints causing missed calls, inconsistent messaging, and high-intent leads slipping through the cracks overnight.

Workflow diagram · grounded in source
1
Incoming call arrives
trigger
“Matic launched an after hours AI phone agent to handle all incoming after-hours traffic”
2
AI collects contact and insurance info
ai_action
“the AI phone agent collects basic contact and insurance info and schedules follow-up calls. This ensures that high-intent customers are never lost overnight and that Matic's representatives have all necessary context before following up.”
3
Appointment confirmation or rescheduling
ai_action
“The AI voice agent calls the customer exactly on time, confirms they're still available, reschedules if needed”
4
Data intake and lead qualification
ai_action
“The AI phone agent collected all 20–30 required data points, flags any disqualifying factors, and hands off only eligible leads to licensed agents”
5
Transfer to licensed human agent
routing
“transfers the call to a licensed human agent”
6
QA-driven improvement loop
feedback_loop
“There were five or six improvements we made just last week based on QA insights. This work is ongoing and essential.”
Reported outcome

Matic handled 8,000+ calls with AI in Q1 2025, achieved an 85–90% transfer success rate for appointment calls, automated ~50% of low-value tasks, maintained an NPS of 90 throughout the rollout, and saw 80% of customers complete AI-handled calls without requesting a human agent.

Reported metrics
Monthly call volume120,000
calls handled by AI in Q1 20258,000+
Transfer success rate for scheduled appointment calls85–90%
Call handling time reduction in data intake flows~3 minutes
Show all 11 reported metrics
monthly call volume120,000
calls handled by AI in Q1 20258,000+
transfer success rate for scheduled appointment calls85–90%
call handling time reduction in data intake flows~3 minutes
low-value tasks automated and reassigned~50%
NPS maintained throughout automation rollout90
customers completing AI calls without requesting a human80%
cost per policy and cost per transfersignificantly reduced
answer rate: AI vs. human agentshigher rate of answer rate using the bot
agent data-gathering time per call before automation7–9 minutes
call operations development focused on AI by early 202560%
Reported stack
Retell AITwilio
Source
https://www.retellai.com/case-study/matic-insurance-ai-call-automation-case-study
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Matic handled 8,000+ calls with AI in Q1 2025, achieved an 85–90% transfer success rate for appointment calls, automated ~50% of low-value tasks, maintained an NPS of 90 throughout the rollout, and saw 80% of customer…

What tools did this team use?

Retell AI, Twilio.

What results were reported?

Monthly call volume: 120,000; calls handled by AI in Q1 2025: 8,000+; Transfer success rate for scheduled appointment calls: 85–90%; Call handling time reduction in data intake flows: ~3 minutes (source-reported, not independently verified).

What failed first in this deployment?

Third-party call center vendors handling after-hours calls delivered poor performance, with volume constraints causing missed calls, inconsistent messaging, and high-intent leads slipping through the cracks overnight.

How is this call center ai AI workflow structured?

Incoming call arrives → AI collects contact and insurance info → Appointment confirmation or rescheduling → Data intake and lead qualification → Transfer to licensed human agent → QA-driven improvement loop.

Related call center ai cases