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US-based Velaura AI has raised $110 million in a Series A funding round, pushing its valuation above $1 billion as investors increase their focus on low-power computing technologies designed to address growing energy constraints in artificial intelligence infrastructure.

Seligman Ventures led the financing, joined by new investors Capricorn Investment Group and Aramco-backed Prosperity7 Ventures. Existing backers, including Mayfield and Samsung Catalyst, also participated in the round.

Velaura AI Targets AI’s Power Constraints

Velaura AI develops energy-efficient silicon chips and software aimed at reducing the power requirements of advanced computing systems. The company is targeting a major challenge for the AI industry: limited access to electricity in hyperscale data centers.

Large cloud computing companies are expanding their data storage and computing capacity, but securing enough electrical power has become a significant obstacle, with projects facing lengthy delays.

The funding round, however, carries an unusual history. It was classified as a Series A even though the same entity had previously raised $153 million in a Series C financing under the name Auradine.

Company Rebranded as Velaura AI

The company changed its name to Velaura AI in March 2026 following a broad restructuring and a shift in strategic direction toward low-power computing.

The restructuring also brought changes to the management team, with Manu Gulati and Aditya Grover joining the company as founding advisors.

Titan Core Platform Focuses on Efficiency

Velaura AI is currently developing its Titan Core digital chip platform, which the company says can deliver two to four times higher performance per watt than conventional chips while maintaining processing speed.

The platform is being developed for applications where energy consumption and cooling requirements are critical considerations.

Velaura AI is targeting two primary markets: hyperscale data centers and intelligent physical systems, including robots and drones.

For these applications, reducing power consumption can be particularly important because computing performance must be balanced against limited power availability and the need for efficient cooling.