When a customer calls your contact center from a small town in Maharashtra, they’re not speaking scripted English. They code-switch between Marathi, Hindi and English and use regional accents. They mention terms like “UPI,” “Aadhaar,” and local place names that most AI models have never encountered.
Most speech-to-text systems? Built in Silicon Valley. Trained on clean studio recordings. They fail catastrophically on real Indian phone calls.
That’s the problem ConvoZen just solved.
The Challenge: Speech AI Built for a Different World
India is fundamentally a voice-first market. Every month, millions of customer conversations happen across contact centers in banking, e-commerce, healthcare, and telecom.
Yet until recently, enterprise speech models trained on authentic Indian conversational data were extremely limited.
Most widely used global solutions were trained on clean, controlled recordings that do not reflect the complexity of real Indian conversations.
As a result, they often struggle with several realities of customer calls in India:
- Code-switched conversations where Hindi and English appear within the same sentence
- Regional accents and dialects across languages such as Tamil, Telugu, Kannada, and Marathi
- Local terminology including UPI payments, Aadhaar verification, and EMI discussions
- Telephony audio quality, where calls run on compressed 8 kHz networks rather than studio recordings
- Real-time responsiveness, where even small delays can disrupt live conversations
For enterprises, this created a costly trade-off. Many contact centers ended up paying for global speech models that struggled with accuracy, while also investing heavily in manual quality checks to correct errors.
ConvoZen’s Approach: Build for India First
To address these challenges, ConvoZen introduced two indigenous speech models designed specifically for Indian conversational environments: Akshara and Ragini. Together, they form the foundation of a voice AI stack built for real phone conversations in India.
Akshara: Speech-to-Text for Real Indian Conversations
Akshara is ConvoZen’s speech-to-text model trained on millions of real B2C phone conversations.
Unlike traditional models trained on clean recordings, Akshara is optimized for the complexity of contact center audio.
Key capabilities include:
- 32% fewer transcription errors compared to the next-best Indian speech model
- 55% lower error rates than leading global alternatives
- Support for nine Indian languages, including Hindi, Tamil, Telugu, Kannada, Marathi, Bengali, Gujarati, Punjabi, and Urdu
- Optimization for 8 kHz telephony audio, the standard for phone calls
- Strong understanding of code-switched conversations, where multiple languages appear in a single interaction
Performance was evaluated on ConvoZen’s Indic Conversational AI Voice Benchmark, a dataset built from authentic B2C phone interactions across industries. In these tests, Akshara delivered the strongest accuracy across multiple languages and real-world audio conditions.
Ragini: Making AI Voices Sound Natural
Understanding speech is only half the equation. For AI agents to interact effectively with customers, their responses must sound natural and conversational. That’s where Ragini comes in.
Ragini is ConvoZen’s multilingual text-to-speech model designed to produce natural, expressive voice responses across Indian languages.
Its capabilities include:
- Multilingual and code-switched responses, allowing AI agents to move seamlessly between languages
- Conversational tone and rhythm, avoiding the robotic cadence typical of traditional systems
- Accurate pronunciation of Indian names, locations, numbers, and business terms
- Six enterprise-ready voice options, including both male and female tonalities
- Optimization for industries such as BFSI, automotive, healthcare, D2C, and edtech
In blind comparative evaluations across six languages, Ragini scored higher than both Indian and global providers in perceived naturalness and pronunciation accuracy.
Why This Matters: Real Business Impact
This isn’t just a technical achievement, it’s translating into measurable business outcomes:
Pilgrim (D2C brand) deployed ConvoZen’s conversational AI agents:
- 65% of conversations handled fully by AI
- 40% reduction in human agent dependency
- CSAT improved to 4.25/5
Jana Bank (fintech) used voice AI agents:
- 7% overall sales growth
- Improved conversion at scale
CARS24 (auto e-commerce) automated quality audits:
- 100% of QA now handled by AI
- Faster compliance checks, stronger oversight
NoBroker experienced:
- 25% improvement in call quality (NoBroker Interiors)
- 8% uplift in site visits (NoBroker Builder)
- 5% incremental growth in revenue
These aren’t marginal gains. These are the numbers that matter to CFOs and COOs.
Want the full story? Read the complete coverage on Press Trust of India.
Ready to see it in action? Book a demo with ConvoZen and see how Akshara and Ragini can transform your customer conversations.