Shunya Labs has launched an Enterprise Sovereign AI platform for organisations seeking to deploy voice AI while retaining full control over data, models and operations.
The launch comes as enterprises increasingly reassess cloud-dependent AI strategies amid tighter data protection regimes and growing concerns around data leakage, vendor lock-in and regulatory exposure. As AI systems move from experimentation to mission-critical infrastructure, organisations are seeking sovereign alternatives that allow them to operate AI within their own technical and jurisdictional boundaries.
Shunya’s platform is designed to function as a foundational sovereign AI layer, enabling deployment across private enterprise environments, including fully isolated and air-gapped systems. The company said the platform delivers real-time voice processing with sub-100 millisecond latency and high transcription accuracy, while ensuring that no data leaves the organisation’s control.
The platform is designed for regulated and high-trust sectors such as banking and financial services, healthcare, defence, contact centres and large enterprise operations, where voice data often includes sensitive personal or operational information.
A key feature of Shunya’s approach is its CPU-first architecture, which removes dependence on specialised GPU infrastructure. This, the company claimed, significantly reduces the cost and complexity of deploying sovereign AI, making it viable not only for large enterprises but also for mid-sized organisations with strict data governance requirements.
“AI is increasingly becoming core infrastructure rather than a peripheral tool,” said Ritu Mehrotra, co-founder and CEO of Shunya Labs. “Enterprises want AI systems they can own, govern and operate themselves, instead of relying on opaque external platforms. Sovereign AI enables that shift.”
Security and compliance are built into the platform’s design, with encryption, isolated deployments and enterprise-grade governance features intended to align with global and Indian data protection standards.
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