Indian AI-native transformation foundry Arinox AI and agentic AI company KOGO, unveiled what they describe as India’s first sovereign AI product. Even as conversations around artificial intelligence (AI) agent adoption gather steam, many enterprises may not pay adequate attention to parallel risks around data security, integrity, as well as costs. At the India AI Impact Summit 2026, Indian AI-native transformation foundry Arinox AI and agentic AI company KOGO, unveiled what they describe as India’s first sovereign AI product — a state-of-the-art system built around the concept of ‘AI in a box’.
With CommandCORE, Arinox AI and KOGO are betting on a counterintuitive AI future — private, sovereign and physically compact. The system is designed to compute locally, without relying on the internet. They’ve partnerships with Nvidia and Qualcomm for its agentic stack, the latest CommandCORE iteration runs on Nvidia hardware.
“The future of AI is private, on an enterprise level too. You simply cannot farm out your intelligence. The only way an organisation can exponentially increase its own intelligence and learning is by keeping AI private. It must own the AI,” explains Raj K Gopalakrishnan, CEO and Co-Founder of KOGO AI.
At its core, this proposition of “AI in a box” is as much ideological as it is technical, pushing conversation beyond large language models (LLMs) and GPUs. Organisations using public foundational models aren’t just processing prompts, but exposing operational insight. “Sensitive industries, when they share data with foundational models and cloud based AI services, are also sharing intelligence,” he adds. Agentic AI deployments must contend with dual threat perceptions of security and privacy. Information, Gopalakrishnan insists, changes everything. “The moment you provide context, you are providing intelligence”.
An AI Threat Landscape 2025 analysis by security platform HiddenLayer points out that 88% of enterprises are concerned about vulnerabilities introduced through third-party AI integrations, including widely used tools such as OpenAI’s ChatGPT, Microsoft Copilot, and Google Gemini. In August last year, an MIT report noted that 95% of generative AI pilots at companies failed to take off, with privacy being a factor.
There are four key layers for a private AI in a box solution. First, custom hardware from Nvidia. Second, KOGO’s agentic OS atop which sits an Enterprise Agent Suite has more than 500 connectors for enterprise workflows, and leveraging open-source models for sovereign AI. Variations include Nvidia’s Jetson Orin-class edge systems for field deployments, DGX Spark for compact on-premises development, and enterprise data centre configurations including Nvidia RTX Pro 6000 Blackwell Server Edition graphics. “This box is designed to cut through complexities of hardware, software and application layers, which an enterprise would have to independently orchestrate. It’ll do focused workloads, repeatable tasks, and can expand to large clusters for an entire workflow,” points out Angad Ahluwalia, chief spokesperson of Arinox AI.
Scalability is achieved by linking multiple units together. Enterprises can choose from three model configurations for now, with more iterations expected in the coming months, according to Ahluwalia. Pricing starts at ₹10 lakh. CommandCORE’s small option can run a model between 1 billion to 7 billion parameters, ideal for enterprises to deploy a handful of agents for batch processing or even human resource onboarding processes. The medium model ranges between 20 billion to 30 billion parameters, for complex agents with inference.
“As AI adoption expands across regulated and sensitive environments, organisations need accelerated computing platforms that can operate entirely on-premise and under strict security controls,” says Vishal Dhupar, Managing Director, Nvidia India. “The very large ones, equivalent to Nvidia’s DGX clusters based on Grace Blackwell series, are powerhouses that can do enterprise wide transformation,” Ahluwalia explains. For context, Nvidia documentation notes that two such DGX units, when interconnected, handle models up to 405 billion parameters.
Why does a private, secure and local AI system matter beyond a sovereignty argument?
For Gopalakrishnan, this answer is also economic. He points to an example of commercial EV charging and battery swap stations, each of which can generate up to 30TB of daily data. “If there are 1000 stations owned by the same organisation and they have to send all this data to the cloud, think of the cost,” he says.
The alternative is edge processing. “A small device sitting in every station without needing internet, they’ll probably send just 200GB data to a cloud instead for processing.” In other words, filter and process locally, transmit selectively, and reduce both bandwidth and cloud compute costs. Arinox and KOGO hope to find traction particularly in sensitive sectors such as finance and banking, government services and defence.
