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How Matic Uses Retell AI to Automate Critical Insurance Call Workflows

~50%
low-value tasks automated
85-90%
transfer success rate
8000+
calls handled by AI
How Matic Uses Retell AI to Automate Critical Insurance Call Workflows
As we’ve explored ways to drive greater efficiency in our business over the past 18 to 24 months, leveraging AI technologies has become a key part of that strategy. By doing so, we’re able to reduce the cost of acquiring new customers, and at the same time, maintain, or even lower, the cost of retaining and servicing them throughout their lifecycle with us.
Brian Barker
Product Manager

About

Matic is a nationally recognized, multi-line digital insurance agency that partners with many of the largest mortgage servicers and financial institutions in the U.S. Through deep integration at key moments in the lending lifecycle, Matic provides seamless access to home, auto, and life insurance.

With 45+ A-rated carriers in its network, Matic delivers high-quality coverage to borrowers across the country, making insurance shopping more accessible and more aligned with the financial decisions homeowners are already making.

Matic’s call operations were under pressure. With 120,000 monthly calls, even small inefficiencies created significant operational overhead. The team faced several persistent problems:

  • Unreliable After Hours Coverage: Due to mismatched incentives, outsourced call centers missed opportunities and delivering inconsistent experiences after hours, putting Matic's customer satisfaction at risk.
  • Unproductive Calls: Matics agents spend the whole of their time listening to the phone ring, time sucked dialing into unanswered calls and voicemails.
  • Time Consuming Tasks: Essential tasks such as data collection took Matic's agents spend 7–9 minutes per call gathering data before even starting the quote process.

All of this was happening while insurance costs were rising nationwide. Matic needed a way to operate more efficiently—without sacrificing the service quality expected by their partners or their customers.

The AI voice agent platform they trusted to do so with? Retell AI.

At A Glance

  • Partnered with Retell AI to automate after-hours support, appointment confirmation, and data collection.
  • Implemented AI voice agents in under 2 months with high success rates.
  • Maintained a 90 NPS score while automating 50% of repetitive workflows.
  • Saw higher answer rates, reduced call time, and significant cost savings.

Use Case 1: After-Hours Coverage

Before Retell AI, Matic relied on third-party call center vendors to handle after-hours calls. The performance was poor and customer experience suffered with volume constrictions leading to calls being missed, inconsistent and ineffective messaging, and opportunities with interested leads slipping through the cracks.

With Retell AI, Matic launched an after hours AI phone agent to handle all incoming after-hours traffic. Available 24/7 all year round, 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.

Additionally this AI phone agent ensured that there was a consistent, branded first touch for every caller no matter the time, day, or call volume.

Use Case 2: Appointment Confirmation & Rescheduling

Scheduling delays and missed calls were costing Matic valuable opportunities. Agents couldn’t always dial out exactly at the scheduled time, and answer rates dropped when calls came late.

Retell AI solved this by handling scheduled call follow-ups automatically. The AI voice agent calls the customer exactly on time, confirms they’re still available, reschedules if needed, and transfers the call to a licensed human agent. With this powerful automation, Matic has seen

  • 3,000–4,500 scheduled calls handled every month, freeing up reps to focus on higher-value work.
  • 85–90% success rate in transferring calls to licensed agents.
  • Through A/B testing, consistently higher answer rates than human-led calls—largely due to the bot's ability to call at the exact scheduled minute.
“In Q1, we handled just about 8,000 calls with the AI operator bot. What we found, and we ran this as an A/B test, was that we actually saw a higher rate of answer rate using the bot. We attribute that a lot to the fact that the bot can make the call at the exact minute of that appointment being scheduled”

Use Case 3: Data Collection & Lead Qualification

Quote intake was one of the most repetitive and time consuming parts of Matic’s call operations. Agents were spending 7–9 minutes per call gathering details before even getting to the consultative part of the conversation.

With Retell AI, that entire process is now automated. The AI phone agent collected all 20–30 required data points, flags any disqualifying factors, and hands off only eligible leads to licensed agents.

  • Average time to collect data dropped from 9 minutes to ~6 minutes.
  • Agents save time by avoiding calls with ineligible or unqualified leads.
  • The system can be easily updated as product or eligibility rules evolve.

This workflow was invaluable in saving time and improving the quality of leads reaching Matic’s team, so they can focus on closing, not qualifying.

Implementation & QA

Matic’s telephony team led the integration of Retell AI. The initial build took just 1–2 months, including foundational integration work with Twilio, API endpoints, and internal prompt handling systems. Once the groundwork was in place, new AI use cases were rolled out rapidly in 1–2 sprint cycles.

By early 2025, more than 60% of the team’s call operations development was focused on AI.

But building was only half the equation. Matic also prioritized quality control:

  • Weekly QA sampling: A dedicated team member reviews 150+ calls per week.
  • Automated call reviews: An internal prompt checks transcripts for flow adherence.
  • Continuous iteration: Engineering, QA, and ops meet regularly to refine flows based on live feedback.
“There were five or six improvements we made just last week based on QA insights. This work is ongoing and essential.”

This proactive approach ensures the AI voice agents are consistently efficient and delivering a customer experience that’s on-brand, on-script, and constantly improving.

Retell's Impact

Matic’s use of Retell AI phone agents has produced meaningful, measurable results across the board:

  • 8,000+ calls handled by AI in Q1 2025 across all use cases
  • 85–90% transfer success rate for scheduled appointment calls
  • Higher answer rates for AI calls vs. human calls (from A/B testing)
  • Call handling time reduced by ~3 minutes in data intake flows
  • ~50% of low-value tasks automated and reassigned
  • 90 NPS maintained throughout automation rollout
  • 80% of customers complete AI-handled calls without asking for a human
  • Cost per policy and cost per transfer significantly reduced (exact metrics pending)

Takeaways

For teams exploring AI voice automation, Matic’s journey offers a clear playbook:

1. Start Small, But Smart

Matic began with a low-complexity, high-impact use case: after-hours call handling. The workflow was simple and the benefits were immediate: collect basic info, schedule a callback. This gave the team early wins, measurable ROI, and internal confidence to build momentum.

2. Never Compromise on Customer Experience

Because Matic operates through co-branded partnerships with mortgage lenders, every interaction is a reflection of another company’s brand. That meant AI couldn’t just be functional, it had to feel human. From voice quality to response accuracy, customer trust remained non-negotiable.

3. Validate with Data, Not Gut

Before scaling automation across the board, Matic ran controlled A/B tests to compare AI performance against human agents. In the case of appointment confirmations, the AI phone agent actually outperformed humans on answer rate. This was a definitive indicator that automation wasn’t just more efficient, it was more effective.

4. Invest Upfront to Move Faster Later

The team spent the first 1–2 months laying a strong technical foundation: integrating with Twilio, building a flexible prompt infrastructure, and aligning internal QA processes. This early investment enabled rapid iteration later, with new use cases launching in just 1–2 sprint cycles.

5. Repurpose, Don’t Replace

Perhaps most importantly, Matic didn’t approach AI as a headcount reduction strategy. Instead, they focused on repurposing human agents to handle higher-value interactions. Higher value tasks, like cross-sell opportunities and deeper consultative conversations, ramped up allowing Matic to provide deeper value for customers while maximizing ROI.

“We’re not replacing people—we’re giving them better things to do.”

Through their phased implementation of Retell AI's voice automation platform, Matic has successfully transformed their customer operations, reducing costs while maintaining excellent customer experience metrics.

By automating repetitive tasks, they've freed their human agents to focus on higher-value activities like consultative selling and relationship building.

For B2B leaders considering similar automation initiatives, Matic's experience demonstrates that thoughtful implementation of AI voice agents can deliver measurable operational benefits without sacrificing customer satisfaction. As AI voice technology continues to advance, companies that embrace these tools strategically can gain significant competitive advantages in efficiency, scalability, and customer experience.

Industry
Transforming customer service
Employee Range
201-500
Location
Ohio, US

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