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    Shunya Labs Launches Voice AI Platform Supporting 216 Indian Languages and Dialects

    Shunya Labs Launches Voice AI Platform Supporting 216 Indian Languages and Dialects

    The platform combines speech recognition, natural language understanding, and generative AI to enable human-like conversations in regional languages and dialects.

    Shunya Labs Launches Voice AI Platform Supporting 216 Indian Languages and Dialects
    Prachi Pradhan··Updated OCTOBER 19, 2026, 5:40 PM
    5 min

    AI company Shunya Labs has launched a new multilingual Voice AI platform that supports more than 216 languages and dialects to improve digital accessibility and communication across India.

    The company said the platform has been designed specifically for India’s diverse linguistic landscape and can help enterprises, government agencies, and businesses interact with users in their preferred languages rather than relying on English-only systems.

    According to Shunya Labs, the platform combines speech recognition, natural language understanding, and generative AI capabilities to deliver human-like conversations across multiple regional languages and dialects. The technology can be used for customer support, citizen services, healthcare assistance, financial inclusion programmes, education services, and enterprise communications.

    Speaking on the launch, Founder and CEO Ritu Mehrotra said, “India’s digital future will be shaped not just by technology, but by accessibility. We want every Indian to interact with technology naturally in their preferred language.”

    One of the key features of the platform is its CPU-first architecture, which allows organisations to deploy voice AI systems without depending heavily on expensive GPU infrastructure. According to the company, this enables enterprises to run voice AI applications on standard servers either on-premise or in the cloud.

    The approach is particularly focused on sectors such as banking, financial services, healthcare, customer support, and government services, where cost, latency, and data privacy are critical considerations.

    The company added that its proprietary speech recognition, translation, and voice generation models have been trained on Indic and Asian language datasets to improve accuracy across multiple languages.

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