Abhinav Sinha spent years watching OYO navigate the messy realities of hyperscale growth across dozens of markets. Now he's betting that experience translates to a different kind of infrastructure challenge: building the GPU-powered backbone that Asia-Pacific's AI economy desperately needs.
His startup, Nava, announced a Series A round of approximately $22 million in April 2026, led by Greenoaks with participation from RTP Global and Unicorn India Ventures. The funding coincided with a rebrand from the company's original name, Kluisz—a shift that signals something more than cosmetic. Nava is pivoting hard toward what it calls "neocloud" infrastructure, a vertically integrated platform spanning data centers, GPU compute, orchestration tools, and developer services.
The financing brings Nava's total haul to roughly $31.6 million, including a $9.6 million seed round closed last July. That's modest by the standards of data center buildouts, which can consume capital at an alarming clip. But Sinha and his co-founders—Vamshidhar Reddy, a former McKinsey partner who previously worked at AMD, and Abhijeet Singh, ex-VP of Cloud at Jio—seem intent on deploying it methodically across India and Southeast Asia.
Singapore as the Launchpad
Nava has planted its regional headquarters in Singapore, a choice that reflects both aspiration and pragmatism. The city-state offers access to capital, talent, and a regulatory environment friendly to cloud infrastructure players. From there, the company plans to roll out GPU clusters and AI data center capacity across the region, targeting enterprises that need to train and deploy models locally.
The platform will offer GPU-as-a-service alongside bare-metal compute options, with an emphasis on compliance and data residency—two factors that matter considerably more in markets like India, where regulatory scrutiny around data localization has intensified in recent years.
Nava's hiring spree tells the story of a company in build mode. Recent LinkedIn activity shows new hires in data center strategy, including executives with stints at ByteDance and CBRE. That's the operational backbone you'd expect from a team trying to compete with entrenched players and well-funded upstarts alike.
The Founders and the Believers
Sinha, Reddy, and Singh incorporated the venture in April 2025 under the legal name CTM Labs Private Limited, operating out of Bengaluru. The seed round attracted a mix of institutional and angel backers: RTP Global and Unicorn India Ventures anchored the round, joined by Blume Founders Fund and Climber Capital. Angels included OYO founder Ritesh Agarwal, Dr. Ritesh Malik, and real estate developer Aditya Virwani—a sign that Nava's pitch resonated across different pockets of India's entrepreneurial ecosystem.
Greenoaks, which led the Series A, has built a reputation for concentrated bets on infrastructure companies. RTP Global, which spearheaded the seed, doubled down. Unicorn India Ventures, which closed its third fund at INR 1,200 crore in February, also returned.
Racing Against Time and Capital
Nava is entering a market that's both enormous and brutally competitive. APAC data center capacity is expected to more than double over the next five years, with investment requirements projected to approach $900 billion by 2030, according to estimates from Moody's. Construction costs have surged—rising more than 15% year-over-year in some markets, per Cushman & Wakefield data published in April—driven by insatiable demand for AI-ready infrastructure.
Indian competitors aren't sitting still. Yotta announced plans in February to deploy over 20,000 NVIDIA Blackwell Ultra GPUs. E2E Networks acquired a 1,024-GPU B200 cluster for its Chennai facility in January. Airtel Nxtra continues scaling its footprint nationwide.
The rebrand from Kluisz to Nava marks a deliberate departure from the company's earlier software-led private cloud positioning. The new identity reflects GPU-centric ambitions. Whether Nava can execute on that vision—deploying physical data center capacity and GPU clusters while competing for enterprise AI workloads—will depend on how quickly it can turn capital into operational infrastructure.
And in a market where demand is outpacing supply, perhaps more than the founders expected, speed matters as much as strategy.
