How Vobiz.ai Is Building The Backbone Of India’s Voice AI Boom

There is no doubt that AI voice agents are having their moment. But most conversations around voice AI today revolve around large language models (LLMs) or conversational orchestration. However, beneath the flood of demos, enterprise pilots, and automated calling systems lies a far less glamorous layer quietly carrying the entire ecosystem: telecom infrastructure.

Every AI voice call still depends on the telecom infrastructure that decides whether the agent connects cleanly, responds quickly and maintains a usable conversation. But the voice AI stack was never designed for AI in the first place.

Latency, routing, spam detection, and call completion were built for human traffic, not machine-scale conversational calls. This mismatch is now becoming a serious bottleneck, and Bengaluru-based Vobiz.ai is trying to solve exactly that. 

Founded in 2025 by Suman Gandham and Vikash Srivastava, the Bengaluru startup is building an AI-first telephony infrastructure layer for companies deploying voice agents at scale. Rather than competing at the model layer, it sits underneath the stack and focuses on making those calls actually work in the real world.

The Bengaluru-based startup provides programmable APIs, lower-latency routing and AI-optimised media infrastructure tailored for conversational AI, helping enterprises connect agents to telecom networks more efficiently and reliably.

As voice AI moves into customer support, sales, collections and internal workflows, Vobiz.ai is betting that the next wave of voice AI will not be won only by better models, but by the infrastructure that makes them usable at scale.

The Aha! Moment

Before Vobiz.ai, Gandham had already spent years inside India’s startup ecosystem.

He previously founded consumer neobank Finin, which was later acquired by Open. After the acquisition, Gandham took a brief sabbatical before returning to build AI voice agents for BFSI use cases.

But during those experiments, he realised the larger bottleneck was not necessarily the AI models themselves. “Existing telephony infrastructure across the world is built for human-to-human calls and traditional call centres… They are not built for AI-to-human conversations,” said Gandham.

This insight eventually led him to partner with Srivastava, who had earlier worked across telecom infrastructure companies including Plivo and Bandwidth. Together, the founders began rebuilding the telecom layer specifically for AI-native communication systems.

However, the duo quickly hit a wall. To explain the problem, Srivastava pointed to ordinary video calls. “In a human conversation, if audio cuts for two seconds, you simply ask the other person to repeat. But in AI conversations, if the same thing happens, the model may interpret it incorrectly and respond with something completely different,” he added.

Latency creates another challenge. According to Srivastava, traditional telecom infrastructure can already introduce 300 to 500 milliseconds of delay. When combined with additional latency from speech-to-text (STT), text-to-speech (TTS), and LLM inference layers, the total response time can exceed 1.5 seconds.

“At that point, humans immediately recognise they are speaking with AI,” Srivastava said. This is where the real work began.

Decoding Vobiz’s Tech Stack

According to the founders, a typical voice AI stack consists of four layers: LLMs, STT systems, TTS systems, and telephony infrastructure. Focused on the last layer, Vobiz.ai is building a “single-hop” architecture designed to reduce delays, background noise and latency. 

It integrates with multiple AI orchestration and speech providers including OpenAI, Gemini, ElevenLabs, Cartesia, AWS Polly, and LiveKit. Rather than depending on a single provider, the company dynamically routes workloads across different models depending on performance, latency, language requirements, and use case suitability.

The startup also uses AI internally across parts of its telecom infrastructure stack for multiple functions:

• Real-time media optimisation
• Echo cancellation and noise suppression
• Packet routing optimisation
• Spam and answering-machine detection
• AI-ready programmable call controls
• Call streaming and transcription support

The startup claims this setup reduces telephony latency to under 80 milliseconds at P95 levels. For context, P95 refers to the maximum duration under which 95% of processes, requests or queries are addressed.

What has also helped the startup is its focus on developer experience. Instead of the lengthy provisioning cycles associated with legacy telecom providers, its clients can complete KYC, provision numbers, access APIs, and deploy integrations through a self-serve onboarding flow within minutes.

This ease of onboarding has helped fuel growth. Since launching in November last year, the platform has scaled from roughly 1 Lakh calls per month to more than 10 Lakh calls per day, while customer retention has remained at 98%.

This optimisation-first approach has already helped Vobiz.ai win early traction, but the more important picture is who is increasingly buying it.

The Infrastructure Bet 

Vobiz.ai’s first wave of traction came from India’s fast-growing voice AI startup ecosystem. Its customer roster includes companies such as Bolna, Sarvam AI, Razorpay, RevRag, Smallest.ai, Navana AI, and others, according to the founders.

Early on, nearly all of its business came from startups building conversational agents for enterprise clients, but that mix is now shifting toward enterprises buying directly.

The founders claim that the enterprise adoption for Vobiz solutions has accelerated steadily over the past few quarters, particularly across fintech, lending, insurtech, logistics, and real estate sectors. Today, enterprises account for roughly 30% of Vobiz.ai’s business, while AI voice startups contribute approximately 70% of the share.

The startup expects enterprise adoption to overtake startup-driven demand by the end of the year as more companies move from AI experimentation into production deployments.

The economics also fit the pattern of an infrastructure business. The startup’s gross margins are expected to stay in the 50% to 80% range, depending on usage patterns and contract structures. This puts Vobiz.ai in a category closer to API-first infrastructure companies than to application-layer AI startups.

This structure is reinforced by a revenue model designed to monetise both connectivity and higher-value software services. Its revenue model currently includes three primary streams: telecom numbers, usage-based call billing, and value-added services such as transcription, call streaming, and answering-machine detection.

The founders expect value-added services to eventually become the largest contributor to revenue over time. Unlike voice AI applications, which can often be built quickly using existing APIs and foundation models, telecom infrastructure requires deep domain expertise across networking protocols, real-time media systems, and cloud-scale architecture.

“You need people who understand telecom protocols, networking infrastructure, and modern cloud-native architectures together. That combination is very rare” adds Srivastava.

Instead of competing with voice AI startups, the startup positions itself as an infrastructure provider alongside global giants such as Twilio and Telnyx, and homegrown players like Exotel, and Plivo. But unlike legacy telecom infrastructure companies originally built for human communication, Vobiz.ai is winding itself specifically around AI-native workloads.

Beyond Voice Calls

While voice remains the company’s primary focus today, Vobiz.ai is already expanding into adjacent communication channels. The startup has begun offering WhatsApp calling and chat integrations, and plans to expand further into SMS and RCS (rich communication service) infrastructure. 

The broader ambition is to become a default AI communication layer across channels rather than just a voice infrastructure provider.

“So whether you want to communicate via calls, WhatsApp, messaging, or other channels, we want to be the infrastructure layer underneath,” Srivastava said.

Geographically, the company is also preparing for international expansion across the US, Europe, Middle East, Africa, and Asia-Pacific markets. Rather than competing head-on with incumbents in saturated US markets initially, the founders plan to focus on regions where AI telecom infrastructure remains relatively underpenetrated. 

Further, to assuage regulatory issues, the startup claims that it has designed its platform around international compliance requirements, including DPDP, GDPR, SOC2, and ISO standards. “We built the platform as an international product from day one,” Gandham said.

The timing could potentially work in Vobiz.ai’s favour. As enterprises increasingly shift from AI pilots to production deployments, the conversation around voice AI is beginning to move beyond flashy demos towards infrastructure reliability, latency, scalability, compliance, and deployment complexity.

And in that transition, Vobiz.ai and the telecom layer, beneath AI conversations, may just finally start to get noticed.

[Edited by Shishir Parasher]
[Creatives by Varshita Srivastava]

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