At the ConvoZen AI Summit 2026 in Bengaluru, enterprise conversational AI platform ConvoZen announced what it calls a full-stack, end-to-end conversational AI architecture built for India’s voice-first realities. The company unveiled two indigenous frontier models—Akshara for speech-to-text and Ragini for text-to-speech—positioning the models as purpose-built for telephonic, multilingual, real-world business conversations.
In conversation with AIM on the sidelines of the summit, Akhil Gupta, Founder of ConvoZen AI, said the journey began with a core challenge: cracking Indian telephony voice. “The most important part was getting the telephonic voice right so that we could transcribe accurately across Indian languages and seamlessly switch between contexts, dialects, and languages. That was the core, and that’s how we started building this model.”
Gupta explained that the models were initially developed for internal use at NoBroker before being productised. “Around 35% of our conversations are already happening on voice AI, powered by one of these fundamental speech-to-text models,” he added.
Over time, the team expanded beyond English and Hindi to multiple Indian languages, training on curated and annotated telephonic datasets drawn from years of real customer interactions. He added that deeply local, region-driven speech patterns shaped the design of both Akshara and Ragini, particularly in understanding conversational nuances that generic global models often miss.
Building a Telephony-First, Enterprise-Grade Stack
At the summit, ConvoZen introduced the Indic Conversational AI Voice Benchmark, designed to evaluate real-world B2C telephony interactions. “When we tested it against Sarvam and others on the same Indic dataset, we outperformed them,” Gupta said, underscoring the company’s confidence in handling noisy, 8 kHz telephony audio where many models struggle.
ConvoZen also detailed a four-layer enterprise AI architecture comprising Conversational AI Agents for omnichannel, multilingual engagement across voice, chat, and WhatsApp; Copilot AI Agents that assist human agents in real time; Supervisor AI Agents that enable 100% conversation monitoring for quality and compliance; and Customer 360 AI Agents that unify customer intelligence across touchpoints.
The company emphasised that Akshara is purpose-built for short, high-variability consumer speech, multi-dialect and code-switched conversations, and business-specific terminology, common in Indian telephony but challenging for studio-trained systems.
Beyond the models, ConvoZen highlighted its multi-layered architecture spanning the model, agent, and business orchestration layers. “Fundamentally, we have created a multi-layer platform. There’s a model layer, an agent orchestration layer, and a business orchestration layer,” Gupta said, adding that the stack is flexible enough to support SaaS, private cloud, or on-premise deployments, including in regulated sectors.
Data sovereignty emerged as a recurring theme at the Summit. “The philosophy is simple—your model is your model. The model deployed for an enterprise belongs to them. We don’t learn from that model,” Gupta said, stressing isolated deployments and enterprise-grade security.
In his session, Zaher Abdul Azeez, Senior Director of Data Sciences and Engineering at NoBroker, contextualised the launch within the broader voice AI landscape. He said the stack was built with “a deep understanding of what is truly required to make voice AI work,” adding that it was “not just about having a great transcript, but about ensuring all components stitch together seamlessly to create real business experiences.”
Azeez said it was “the first time we are launching these models as endpoints so developers building in the voice space can benefit,” particularly those struggling to adapt generic LLMs to Indian speech contexts.
With Akshara listening and Ragini speaking, ConvoZen is positioning itself at the centre of Bharat’s conversational AI shift, where voice systems are not just multilingual but deeply contextual, secure, and enterprise-ready.
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