Press Network of India

WATER® raises $2.5M led by Endiya Partners to build Physical AI for Adaptive Surfaces

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WATER, a Physical AI company building adaptive intelligent surfaces, today announced a $2.5M round led by Endiya Partners, an early-stage venture capital firm focused on deep tech, enterprise AI, and healthcare and life sciences.

WATER is building intelligence into the surfaces people spend most of their day on. Its two products, FLOW™, an adaptive chair, and CAMA™, an adaptive bed, run on a shared foundational model called VORTEX, designed to sense, understand, and gently respond to the human body in real time. FLOW focuses on Kinetics: posture, movement and load during seated interaction. CAMA focuses on Somnics: sleep, recovery and respiration. The intent behind both products is measurable physiological benefit beyond comfort alone, which positions WATER well outside the traditional ergonomic furniture category.

While a wave of connected furniture and sleep-tracking products has emerged in recent years, most stop at sensing or offer only limited physical response. WATER’s approach combines sensing, decision-making, and closed-loop physical actuation across multiple surfaces, powered by one underlying model, a combination the company believes remains largely unaddressed in the category today.

“Our thesis is simple: intelligence in the physical world should upgrade the human being at the center of it,” said Teja Vinukollu, Founder and CEO of WATER. “We call that layer Bio Physical Intelligence. The body is constantly telling the surfaces it lives on what it needs, and VORTEX is the first model built to listen and respond. FLOW and CAMA are step one on the road to a world where every human being gets to be superhuman.”

WATER’s longer-term vision extends beyond FLOW and CAMA. The company sees the same

body-intelligence layer eventually powering fully adaptive environments, where seating, sleep surfaces, and the other physical systems around a person respond dynamically to that person’s needs, a shift from individual intelligent products toward intelligent environments as a whole.

“WATER represents exactly the kind of category-defining bet we look for,” said Sateesh Andra, Managing Partner at Endiya Partners. “This isn’t a smarter chair or a smarter bed. It’s a genuine attempt to build a foundational model of how the human body interacts with the surfaces it lives on, and that’s a much bigger and more durable idea. What excites us most is where this can go: the same intelligence that understands a person in a chair or a bed could eventually extend to entire adaptive environments, spaces that respond to people rather than the other way around. The team has shown real capital efficiency and real technical depth in getting here, and we’re excited to back them as they take this further.”

“Complex spaces require partners who are comfortable with complexity,” said Vinukollu. “From our very first conversation, Sateesh demonstrated that quality. He brings the right sparks to the discussion, and the Endiya team complements that energy with strong conviction and commitment.”

The round saw participation from Pullela Gopichand (Chief National Badminton Coach, India), Rohit Chennamaneni (Co-Founder, Darwinbox) and Nandan Reddy (Co-Founder, Swiggy), among other angel investors.

WATER has already built a CES-showcased, near-production-ready adaptive sleep system, developed with notable capital efficiency, an example of the multidisciplinary hardware and software talent India offers at a fraction of the cost required elsewhere. A foundational patent filing underpins the company’s approach to safety, a core design principle in a system built to act on the human body without disturbing it.

The funding will support WATER’s continued technical validation, including proving how its model’s understanding transfers across products and users, as the company works toward establishing itself as a foundational intelligence layer for adaptive surfaces broadly, with potential applications extending into automotive, aviation, and beyond.

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