Vapi raises $50M Series B
Read More →Major Automotive Marketplace Cut Their Call Center in Half. Revenue Doubled.
Customer: Leading Latin American Automotive Marketplace
Industry: Automotive Marketplace
HQ: Mexico City, Mexico
Funding: $1.68B
Region: Mexico
You've probably heard a dozen pitches about AI replacing call centers. Meanwhile, your company still has four floors of human agents.
That's exactly where this automotive marketplace was. Four floors of human agents across multiple cities handling 10,000 to 15,000 calls per day. The typical enterprise setup that burns cash while customers wait on hold.
But here's what actually happened: They reduced their call center footprint by over 50%. Revenue increased 200%. They now handle 450+ concurrent calls across 5 countries with AI agents.
The difference wasn't the AI models themselves. It was infrastructure that could handle real production scale.
The Real Problem: Building Relationships at Scale
This company isn't just Latin America's first tech unicorn. They're trying to own every touchpoint in the car-buying journey.
"We're transitioning from buying and selling cars to owning the relationships with our customers," their Chief Product & AI Officer explains. "That means being there for financing questions, maintenance decisions, and trade-in conversations, not just the initial sale."
Traditional call centers couldn't deliver that vision. Funnel-based IVR systems definitely couldn't. They needed agents that could handle context, remember conversations, and adapt to what customers actually needed.
What They Built Instead
The company connected their internal MCP services, LangGraph workflows, and observability pipelines through a comprehensive voice AI orchestration platform. Now each agent dynamically invokes the right capability based on real customer intent, not predetermined scripts.
"There's no funnel anymore," says their CPO. "Just user intents that trigger the right capability at the right time."
The technical architecture matters here. The platform handles:
- All inbound and outbound voice flows through advanced telephony and API orchestration
- Integration with internal services for car financing, verification, and scheduling
- Custom observability pipelines for real-time monitoring and performance metrics
- 1000+ localized agents across 5 countries
This isn't a simple chatbot integration. It's production infrastructure that handles 10,000 to 15,000 voice AI agent calls per day with enterprise-grade reliability.
The Numbers That Matter
| Metric | Impact |
|---|---|
| Revenue | +200% |
| Customer Acquisition Cost | -50% |
| Call Center Footprint | -50%+ |
| Monthly Calls | 1,700,000+ |
| Concurrent Calls | 450+ |
| Countries Deployed | 5 |
Based on internal company reporting
"Our top of funnel didn't change, but the depth of engagement did," their executive notes. "That's where the growth came from."
The Hard Parts They Actually Solved
Transforming a billion-dollar marketplace required more than just plugging in an API. The company faced real organizational challenges:
People transformation: The company gave a bold mandate: "You're either building agents, maintaining agents, or you no longer work here." Call center employees transitioned into AI experience builders.
Observability gaps: Early deployments revealed blind spots. They needed end-to-end monitoring across every touchpoint, which their chosen infrastructure provided.
Latency requirements: Voice conversations can't buffer like video. Sub-second response times are non-negotiable for customer acceptance.
"It's not just a system migration, it's a people migration," their executive explains.
What Makes This Actually Work
"80% of our business is AI agents. Our voice AI infrastructure lets us sleep at night," says the Chief Product & AI Officer.Product & AI Officer.
The company succeeded where others failed because they had infrastructure that scales. While competitors struggle with voice AI that breaks under load or sounds robotic at scale, their platform delivered:
Multi-team collaboration: Engineers own business logic while product and operations teams manage conversation flows. Everyone works on the same platform with their own access levels.
Production reliability: The system handles traffic spikes transparently. When promotions drive 10x normal call volume, the infrastructure scales automatically.
Continuous improvement: Every conversation feeds back into the system. Models improve, intents get refined, and capabilities expand based on real customer interactions.
The Expansion Playbook
The company is now rolling this model across all global markets. Next milestones include:
- Ultra-low-latency voice workflows for even more natural conversations
- Cross-channel memory so agents remember customers across voice, WhatsApp, and web
- Continuous evaluation loops tied to actual car purchase outcomes
"Funnels are for transactions. Relationships are for growth," their executive says. "The right infrastructure helps us build the systems that make that shift possible."
Key Takeaways
This automotive marketplace transformed their customer experience across Latin America using enterprise-grade voice AI infrastructure.
Implementation: Production deployment handling 450+ concurrent calls across 5 countries.
Results: 50% call center reduction, 50% lower customer acquisition cost, 200% revenue increase.
The success came from treating voice AI as critical infrastructure, not just another software integration. With the right platform foundation, AI agents became their primary customer interface while dramatically improving both efficiency and revenue outcomes.
