OrtCloud plans AI infrastructure rollout after $2.2m funding round

The startup targets database services and GPU deployments across cloud systems.

Metroo Pte. Ltd., which operates as OrtCloud, plans to roll out artificial intelligence (AI) infrastructure and cloud computing upgrades over the next three months after closing a $2.2m (US$1.7m) pre-seed funding round in April.

“These developments are being rolled out progressively over the next three months,” Metroo CEO and co-founder Enyegue Carl Dimick told Singapore Business Review.

The rollout includes database infrastructure services, graphics processing unit (GPU)-enabled deployments for AI workloads, and orchestration layer upgrades aimed at improving scalability and performance. Dimick said the database product is targeted for release in 2026.

The funding round was led by Golden Gate Ventures Pte. Ltd. with participation from Antler Innovation Pte. Ltd.

“The funding raised will be used in OrtCloud's product development and infrastructure for scaling usage across the Asia-Pacific and US markets, expanding its go-to-market strategies and hiring key roles in these regions,” Dimick said in an emailed reply to questions.

Founded in 2024 by Dimick and Pranay Jain, OrtCloud develops cloud infrastructure designed for AI applications and other computing workloads requiring stable, isolated environments.

The platform aims to address unpredictable infrastructure costs, workload interference in shared systems, and the complexity of managing cloud and on-site computing environments.

Dimick said these issues become more significant with AI apps that run continuously and require long-term computing stability.

To address this, OrtCloud provides isolated virtual machines that operate as separate execution environments, letting workloads run independently.

Over the next five years, the company aims to position itself as an infrastructure layer for persistent workloads across cloud and on-site systems.

“Our goal is to redefine how infrastructure is consumed, moving from shared, usage-based systems to dedicated, predictable environments that scale with modern computing needs,” Dimick said.

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