Automating phone surveys with AI opens up a world of possibilities—from reducing costs to gaining deeper insights through natural language conversations. But building a system that can deliver hundreds of concurrent calls, maintain high completion and response accuracy rates, and provide real-time analytics is no small feat.

This case study explores how we built a scalable telephony platform that combines conversational AI with a robust SIP infrastructure. The result is a cost-efficient, production-ready solution capable of running over 20 AI-driven survey calls simultaneously—with automated transcription, sentiment analysis, and full control over the voice pipeline.

The Challenge: Making AI-Powered Calling Reliable and Scalable

Traditional phone surveys rely on human agents or basic IVR systems, which can be slow, expensive, and limited in engagement quality. The goal here was different: create a fully automated system that could mimic natural, multi-turn conversations while optimizing for cost, scalability, and data quality.

That meant solving several complex challenges at once:

• Ensuring consistent call delivery across dozens of concurrent sessions
• Managing SIP routing, monitoring, and failovers at a granular level
• Integrating real-time voice interaction through an AI engine
• Automating transcription, sentiment tagging, and response parsing
• Reducing the cost per minute without sacrificing call quality
  

The platform had to be flexible enough for production use, reliable enough for daily operation, and powerful enough to surface valuable insights at scale.

How We Built It: A Scalable SIP Architecture for AI Telephony

The project began with a clear goal: design an end-to-end architecture that could reliably support AI-powered phone surveys and evolve with growing demands.

Telephony Foundation: From Twilio to BYOC

We initially deployed the platform using Twilio Elastic SIP Trunking, which provided a quick path to validate core functionality. But as call volume increased, the cost per minute became a limiting factor.

To gain more control—and reduce costs—we transitioned to a BYOC (Bring Your Own Carrier) approach, using FreePBX as the backbone and Jambonz for programmable call routing. This move gave us:

• Full ownership of call flow logic
• The ability to scale horizontally
• Access to low-cost SIP carriers
• Real-time monitoring hooks and error handling
  

Integrating Conversational AI

At the heart of the platform is Retell, a conversational AI engine built for voice interaction. This engine handles multi-turn dialogue, adapts to user input, and outputs structured responses.

We integrated a middleware layer that bridges SIP calls with Retell’s AI Agent, dynamically routing each session, managing timeouts, logging events, and formatting audio streams for optimal clarity and recognition.

Real-Time Monitoring and Post-Call Webhooks

Call quality and completion rates were top priorities. To manage this, with the FreePBX solution, we managed to integrate:

• Live call dashboards for monitoring active sessions
• Call recording and logging at the SIP level
• Immediate post-call webhooks delivering full transcripts, timestamps, call metrics, and sentiment analysis

This analytics layer enables near-instant feedback on how each call performed—crucial for both technical tuning and survey performance evaluation.

The Stack: Tools We Used

We selected a modular, cloud-native stack designed for flexibility, observability, and low latency:

• FreePBX – SIP trunk management and base call routing
• Jambonz – Programmable SIP logic and media handling
• Retell AI – AI Agent engine
• Make.com – Post-call webhook handing

All backend services are API-driven, allowing integration with third-party systems, dashboards, or automation tools.

Results: High Performance, Lower Costs, and Actionable Data

The platform achieved its original goals—and more. After several test cycles and production load scenarios, here’s what the system consistently delivered:

99.4% Call Completion Rate
Even during high concurrency spikes, the infrastructure handled call initiations and completions reliably.

99.2% Response Accuracy
The combination of high-quality audio handling and robust AI logic ensured highly accurate recognition of both closed and open-ended answers.

$0.01 Average Cost per Minute
Switching from Twilio to a BYOC setup dramatically reduced costs while maintaining performance standards.

20+ Concurrent Calls
The architecture scales horizontally, making it capable of running hundreds of AI conversations in parallel with no drop in quality.

Automated Post-Call Analytics
Every call generates a structured report including transcript, sentiment score, detected responses, and duration, enabling immediate insight into campaign performance.

What We Learned Along the Way

This project confirmed a few key principles when it comes to voice AI systems:

• Start with something stable, but plan to optimize
  Prototyping on Twilio helped validate our assumptions, but owning the SIP layer gave us the control we needed.
• The AI experience depends on infrastructure
  Even the most advanced AI fails if audio is poorly routed, cut off, or misconfigured. Infrastructure and AI must be designed hand in hand.
• Post-call data is critical
  Transcripts and response logs aren’t just nice to have—they’re essential for understanding how the system performs and how people engage.
• Flexibility = Longevity
  By using modular components (Jambonz, Retell, Node.js), we’ve made the system extensible. Adding new carriers, AI models, or survey logic is straightforward.

Could We Help You Too?

If you’re exploring AI-powered telephony, building a contact center from the ground up, or just want to modernize your survey workflows, we’d love to talk.

At Zarego, we specialize in designing and scaling custom software solutions across telecom, automation, and AI—always with a focus on performance, clarity, and long-term maintainability.

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