The Challenge of Scaling Real-Time Voice AI in India

Bolna handles more than 500,000 voice calls per day, helping companies deploy conversational agents across India. Their customers rely on Bolna's platform for customer support, appointment scheduling, lead qualification, and other workflows where speed, reliability, and natural conversation directly affect outcomes.

As Bolna scaled to thousands of concurrent conversations, the team identified a critical gap in available voice technology.

“Our platform was built to handle the volume and reliability our customers needed, but our growth was being constrained by the quality and naturalness of our multilingual voice interactions," the team explained.

At their scale, voice intelligence had to deliver consistent, human-like performance in real time across Indian languages—not just in isolated use cases, but in the demanding production environments where Bolna was already operating reliably.

Why Production-First Voice Infrastructure Matters at Scale

When Bolna evaluated alternatives, two requirements were non-negotiable.

The first was latency and reliability under load. For live voice conversations, the platform needed to behave predictably during peak periods with thousands of concurrent calls. Without that consistency, it’s difficult to support real-time, multi-turn conversations where timing really matters.

The second requirement was language quality. For Bolna, Indian language performance isn’t a “nice to have”—it’s central to adoption.

India is one of the most complex voice AI markets globally, with 22 official languages, hundreds of dialects, and significant accent variation. Voice quality is often the difference between whether people trust the interaction, stay on the call, and actually complete the conversation.

“You can’t scale voice AI in India with English alone, and you can’t scale with synthetic-sounding regional languages that users immediately distrust.”

This is where Cartesia proved to be a strong fit. Its production-quality Hindi, Tamil, Telugu, and other Indian languages sound natural to native speakers. For Bolna’s customers deploying across different regions and demographics, this unlocked entirely new markets.

Putting Production-Grade Voice AI to the Test

Bolna deployed Cartesia to enhance the multilingual intelligence of a high-volume customer support automation system already serving one of its largest Indian enterprise customers. The choice was strategic: in an environment where both latency and language quality directly affected measurable business metrics, Bolna needed technology that could elevate conversational performance across multiple Indian languages.

Operating at scale with thousands of concurrent calls and real-time conversations with unpredictable flows, Bolna integrated Cartesia specifically to strengthen language capabilities while maintaining the clear success metrics the system already tracked, like call resolution rates, user satisfaction, and conversation completion.

Latency & Reliability Under Real Load

In this environment, predictable performance mattered more than peak benchmarks. Cartesia’s consistent, low-latency behavior allowed Bolna to support real-time conversations without compensating for timing inconsistencies.

That reliability proved critical for multi-turn conversations, where even small delays can disrupt the flow and affect whether users complete interactions.

Language Quality Users Trust

India is one of the most demanding voice AI markets in the world, and this deployment reflected that complexity. Cartesia’s production-quality Hindi, Tamil, Telugu, and other Indian languages sounded natural to native speakers, with pronunciation and cadence that matched real-world expectations.

This made it possible for Bolna’s customers to deploy voice experiences beyond English-only use cases across different regions and demographics.

Designed for Sustained Production Load

Since Bolna's infrastructure was already engineered to support sustained throughput and uptime under real production load, the team could deploy Cartesia's multilingual capabilities directly into live environments. With Cartesia enhancing language quality, conversations maintained consistent performance across longer interactions, and users were able to complete workflows without interruption.

The Results: Reliable Voice AI at Scale

Today, Bolna handles hundreds of thousands of calls per day, with peak periods supporting several thousand concurrent conversations. Since deploying Cartesia in production, Bolna has seen clear operational and customer-facing improvements:

• ~40% reduction in engineering time previously spent on latency and quality workarounds

• Higher conversation completion rates, with more users reaching successful outcomes on the first interaction

• Expanded voice AI deployment across multiple Indian languages, new use cases, and higher-concurrency environments

As the team describes it:

“We've built our infrastructure to operate at scale. Now with Cartesia's multilingual intelligence delivering human-like naturalness across the complexities of various Indian languages, our end users actually complete conversations. That's the metric that validates our platform's value and drives real adoption.”

Looking Ahead

With Cartesia's multilingual capabilities now integrated into production, Bolna is expanding into more Indian languages, higher call volumes, and increasingly complex conversational use cases—all built on the scalable infrastructure they've already established.

This includes multi-step interactions and regulated or high-stakes workflows in healthcare and financial services, where Bolna's platform reliability combines with Cartesia's language quality to meet requirements that were previously out of reach due to voice intelligence limitations alone.

Bolna is also building new orchestration capabilities leveraging their existing infrastructure alongside Cartesia's multilingual intelligence. Clear alignment on Cartesia's roadmap enables the team to plan long-term deployments with confidence.

As Bolna puts it, voice is becoming a primary interface, not a novelty. With their proven scale and Cartesia's language capabilities in place, the team has a complete foundation to continue building on as they grow.