Voice AI Platform Supports 216 Indian Languages for Customer Support
Shunya Labs has launched a multilingual Voice AI platform that handles speech recognition, natural language understanding, and generative AI across over 216 Indian languages and dialects. The platform lets customers interact with businesses and government agencies in their preferred language rather than defaulting to English.
For customer support teams, this addresses a practical problem: reaching users across India's linguistic diversity. The technology works for customer support, citizen services, healthcare, financial services, education, and enterprise communications.
How It Works
The platform combines three core capabilities to generate human-like conversations in regional languages. It processes spoken input, understands meaning, and generates responses-all within a single system.
Support agents can deploy the system to handle inquiries from speakers of Hindi, Tamil, Telugu, Marathi, Bengali, and hundreds of other languages and regional dialects without building separate systems for each one.
Why This Matters for Support Teams
Most customer support infrastructure still relies on English or a handful of major languages. This creates friction for users who prefer their native language and increases support costs when companies need multilingual staff.
A Voice AI for Customer Support system that handles regional languages reduces that burden. Support teams can focus on complex issues while the platform handles routine inquiries across language barriers.
The underlying Speech-To-Text technology also means users don't need to type-they can speak naturally, which improves accessibility for people with limited literacy or those using mobile devices on slow connections.
Current Applications
- Customer service for retail and e-commerce
- Government citizen services and helplines
- Healthcare appointment booking and symptom screening
- Financial services and banking support
- Educational content delivery
The platform is built for India's specific needs, where linguistic preference often determines whether users engage with a service at all.
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