Enterprise sovereign AI platform targets regulated workloads

Enterprises are increasingly reassessing how they deploy artificial intelligence, especially as regulatory frameworks tighten and AI systems shift from experimental tools to core operational infrastructure. Compliance requirements under laws such as the EU AI Act and India’s DPDP Act are forcing organisations to examine where data is processed, stored and controlled.

Concerns around data leakage, vendor lock-in and long-term dependency on external cloud providers are also growing. As a result, demand is rising for AI systems that can operate fully within enterprise-controlled environments rather than through third-party cloud services.

Enterprise sovereign AI platform enters the market

Against this backdrop, Shunya Labs has launched an enterprise sovereign AI platform designed to allow organisations to run high-performance voice AI entirely within their own infrastructure. The platform is positioned as a foundational AI layer rather than an application, aimed at enterprises that want to own and operate their AI systems end to end.

The platform supports deployment across private Cloud environments, on-premises datacentres, edge locations and fully air-gapped systems. According to the company, it delivers sub-100 millisecond latency and word error rates below three per cent without relying on any external data connectivity.

This architecture is intended to ensure zero data exfiltration, full jurisdictional control and complete ownership of AI pipelines. Such characteristics are critical for regulated and high-trust sectors, including healthcare, banking and financial services, defence, contact centres and internal enterprise operations.

CPU-first design lowers cost barriers

A notable aspect of the enterprise sovereign AI platform is its CPU-first architecture. By avoiding reliance on GPU-heavy infrastructure, the platform aims to significantly reduce deployment and operational costs. Shunya Labs claims this approach can lower costs by up to twenty times compared to GPU-dependent models.

This design choice is intended to make sovereign AI deployments feasible not only for large enterprises but also for mid-sized organisations that require strong privacy guarantees, predictable performance and cost efficiency.

The platform’s modular structure allows enterprises to deploy the same AI stack across different environments—public Cloud, private Cloud, on-prem, edge or air-gapped—without architectural changes.

Infrastructure-level security and compliance

Security and compliance are embedded at the infrastructure layer of the platform. It supports TLS encryption for data in transit, AES-256 encryption for data at rest, customer-managed encryption keys and isolated deployment models.

The platform is designed to align with enterprise and regulatory standards, including HIPAA, SOC 2 Type II, ISO 27001, GDPR and India’s DPDP Act. This positioning reflects a broader enterprise shift toward building AI systems that are auditable, controllable and compliant by design rather than through add-on controls.

AI as owned infrastructure, not rented service

Commenting on the launch, Ritu Mehrotra, Co-Founder and CEO of Shunya Labs, said enterprises often rely on rented AI services from cloud providers, a model she described as workable for pilots but fragile for core systems. She argued that as AI becomes infrastructure—similar to databases or operating systems—organisations will increasingly demand direct ownership and operational control.

Shunya Labs positions its enterprise sovereign AI platform as a response to this shift, targeting organisations building long-term AI capabilities in a regulatory and risk-conscious environment where control and transparency are becoming as important as performance.

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