From Pilot to Production: Building Localised, Enterprise-Grade AI Voice Agents

When you build an AI voice agent, standard text-based LLM benchmarks only tell half the story. The moment an agent answers a phone call, you are no longer evaluating prompt quality: you are juggling real-time latency, telecommunication networks, regional accents, strict compliance frameworks, and unpredictable customer behaviour.

I recently hosted a panel in Singapore as part of our Twilio x Microsoft: The Agentic Edge series. Twilio and Microsoft share a deep global strategic partnership, combining Microsoft’s hyperscale AI infrastructure with Twilio’s trusted global communications platform to help developers build cutting-edge conversational experiences. I was joined by three industry leaders tackling these challenges on the front lines:

• Gabe Hollombe, Director of Solution Engineering (Startups / Asia) at Microsoft

• Lance Goh, Senior Manager, Solution Engineering at Twilio

• Robin Li, Senior AI Strategy & Partnership Director at WIZ.AI

Together, we broke down what it takes to bring an AI voice solution from a weekend demo to an enterprise-grade deployment handling millions of calls across the world.

Here are the key takeaways from our conversation.

1. Better Together: Why You Shouldn't Build the Whole Stack

One of the biggest traps for engineering teams is taking on what Lance aptly called the "growth tax": wasting precious developer hours building low-level infrastructure that isn't core to your unique business value.

"You shouldn't have to worry about buying and securing your own GPUs, fine-tuning infrastructure, identity management, or telecommunications carrier relationships. Focus on what makes your business unique."

— Gabe Hollombe, Microsoft

Combining specialised platforms creates a stack far greater than the sum of its parts:

• Microsoft Azure provides heavy-lifting backend power: hyperscale infrastructure, enterprise model hosting, compliant logging, data residency controls, and low-latency AI inference.

• Twilio handles communications ingress and egress: managing global carrier relationships, PSTN connections, text routing, and low-latency voice streaming.

• WIZ.AI builds directly on top of both, allowing their engineering team to direct 100% of their R&D towards Southeast Asian voice AI localisation, domain-specific banking workflows, and native language understanding.

By leveraging pre-built enterprise infrastructure, teams can spin up voice operations in new markets like Manila or Sydney in minutes by turning on existing integrations rather than spending months negotiating local carrier contracts.

2. Hyper-Localisation & Latency: Making Agents Sound Human

Building voice agents that sound natural in studio environments is straightforward. Making them sound authentic over an 8kHz telephone network across multilingual Asian markets is where the real engineering begins.

The "Golden Window" of Latency

Robin shared critical field data from WIZ.AI across Asia:

• The Target: The ideal conversational response window is between 500ms and 800ms.

• The Reality: Accounting for telecom transport, network hops, and LLM inference, achieving an average P90 latency under 2.0 seconds is currently acceptable.

Code-Switching and Local Dialects

In markets like Singapore, Malaysia, or India, speakers naturally blend languages mid-sentence (e.g., Singlish or Hinglish). Modern speech infrastructure, such as the Microsoft Voice Live API paired with Twilio's streaming channels, supports real-time language detection and dynamic code-switching without requiring hardcoded language toggles.
 

Managing Expectations Like a Human

If an agent needs to perform an agentic multi-document lookup or execute a backend API call taking two seconds, total silence feels jarring. Human customer service agents fill these gaps naturally: "Got it, give me just a second whilst I pull up your account information." Training your AI voice agent to use conversational holding phrases dramatically improves user trust during necessary background processing. Naturally, humans want these kinds of conversations. It’s all about setting the expectation.

3. Crossing the Chasm: From Pilot to Commercial Production

Why do so many impressive voice AI proof-of-concept (POCs) fail to reach commercial production? The priorities shift completely once you transition phases:

Defining Hard Success Metrics

Robin noted that for WIZ.AI's high-volume banking use cases (such as Promise-to-Pay notifications or account updates), success is defined by strict quantitative metrics:

1. Connection Rate: Carrier network reliability and reachability.

2. Recognition & Tagging Accuracy: Correctly capturing intent, payment amounts, and dates despite background noise.

3. Promise-to-Pay (PTP) Conversion: Measurable business outcomes compared to legacy outreach methods.

The Human Element

Deploying AI voice agents isn't about replacing human workers; it's about shifting their focus. By letting voice agents handle routine, repetitive calls 24/7, human teams can transition to high-empathy, complex workflows—such as financial restructuring, hardship negotiations, or specialised support—where human judgment is irreplaceable.
 

4. Developer Takeaway: AI Guardrails and Code Quality

During our audience Q&A, we tackled an internal engineering problem every modern software team is facing: How do you maintain code quality when developers are using AI code generation tools at scale?

Gabe and Lance offered two actionable practices for engineering leaders:

1. Cross-Model Code Reviews: Research shows different LLM families excel at spotting different types of bugs. If Model A wrote the code, use Model B  to perform the automated code review.

2. Strict Pull Request (PR) Guardrails: Implement pre-commit hooks and repo guardrails that enforce strict line-count limits on pull requests (PRs). Combined with GitHub’s newly released stacked PR workflows, smaller commits force both human reviewers and AI harnesses to evaluate code surgically rather than approving giant, unreviewable diffs.

Final Thoughts

Before writing a single line of code or prompt engineering your next voice agent, step back and ask the foundational question: What is the exact business outcome we are trying to achieve?

When you start with a clear problem, pair the right hyperscale infrastructure with specialised communication tools, and design for regional nuances, moving from a demo to millions of production calls becomes a structured engineering roadmap rather than a guessing game.

A huge thank you to Gabe Hollombe, Lance Goh, and Robin Li for sharing their expertise with our developer community during The Agentic Edge series.