The AI conversation of the last few years has mostly played out on a screen. ChatGPT and tools like it answer questions and draft documents, and that work has real value inside an office. A hospital room runs on something else. Someone still has to walk into the room, wipe down the bed rail, and run the UV light over the surfaces a patient will touch next.
Mohammad Noshad, co-founder and CEO of Shyld AI, has spent the past several years building AI for that second category, systems that carry out the response themselves, inside the room where it happens.
Before founding Shyld AI, Noshad spent several years researching AI at Harvard and had already founded and exited two companies. But a personal loss gave his work a more specific direction. After a close friend died from an infection contracted during a routine hospital stay, Noshad and his brother Morteza founded Shyld AI in 2022 to address a problem that conventional monitoring systems often leave to people to solve.
“We believe in an AI that takes action and gets something done,” Noshad has said. “Not just reporting or ambient intelligence. We call this Active AI.”
Why information AI stops at the screen
A language model can talk a nurse through a disinfection protocol, step by step, on a screen. A physical AI system can take a different approach, detecting a contamination risk and initiating a response inside the room itself.
Noshad’s thinking starts exactly at that gap. Most healthcare AI follows the same pattern as chatbots used in hospitals, producing a dashboard or an alert and handing the response to a person standing nearby. That alert might reach a nurse already occupied with another patient, or an environmental services worker several floors away. The information is correct, and the response still waits on someone to become free. A shared keyboard touched by a nurse, or a bed rail touched again minutes after a room is cleaned, creates a contamination risk that only a physical response can address. Shyld AI’s devices are built to close that gap themselves, sensing the risk and delivering the response inside the same room, in the same moment.
“For the first time, we’re building an AI that’s automating that,” Noshad has said. “It’s taking action instead of people going into the rooms and doing disinfection.”
Why bringing AI into the physical world matters
Hospitals have adopted digital tools to support clinical decisions, monitor operations, and flag potential problems. But physical environments introduce a different challenge. Identifying a problem is only part of the job. A system must also be able to act safely and reliably within the space where the problem occurs.
Shyld AI’s approach to that challenge uses wall-mounted devices equipped with onboard sensors and the company’s VERTEX foundation model, developed with NVIDIA. The system processes what it detects on the device itself, rather than relying on a hospital’s cloud network. This allows the units to continue operating during network outages and keeps video and patient data from leaving the room.
That architecture can also simplify the technology review process for hospitals. Rather than integrating directly with hospital records, the devices operate at the point of care, which can make their privacy and security implications easier for IT and compliance teams to assess.
Shyld AI’s reported procurement cycles of eight to ten weeks are considerably shorter than the twelve to eighteen months often associated with hospital technology purchases. The company’s on-device approach is an important part of its positioning as an edge-native AI platform, bringing intelligence to the point where action is needed rather than relying on a remote data center.
What physical AI adoption could look like
Noshad sees opportunities for the same approach wherever contamination control and operational readiness are essential. Hospitals are the starting point, but the underlying sensing and decision-making technology can be adapted to other environments with similar requirements.
Inside operating rooms, Shyld AI’s devices help track surgical readiness and flag missing instruments before a procedure begins. The aim is to identify potential delays before they affect the rest of the day’s schedule. The company has also begun working with pharmaceutical manufacturers, applying its approach to cleanroom environments where maintaining strict contamination controls is essential.
These applications build on the same core idea: AI should do more than identify a problem and notify someone. It should help carry out the response within the environment where the problem occurs.
That approach has already produced measurable results in a hospital setting. A peer-reviewed study conducted at Stanford Hospital’s Advanced Endoscopy Unit, co-authored by Noshad alongside Stanford researchers Monique T. Barakat and Timothy Angelotti and published in the American Journal of Infection Control, found that Shyld AI’s autonomous UV-C system reduced cumulative microbial bioburden by more than 93% compared with a room cleaned by hand.
The technology is now deployed across more than 30 U.S. hospitals. In May 2026, Shyld AI also announced a $13.4 million seed round led by Aulis Capital to support additional hospital installations and continued development of VERTEX.
“We’re moving the industry from passive AI to Active AI, technology that understands how hospitals operate and improves workflows in real time without adding burden to clinical teams,” Noshad has said.
For Noshad, the long-term opportunity is to make AI useful in environments where information alone is not enough. In hospitals, that means helping address contamination risks, supporting operating room readiness, and reducing the burden on clinical teams. The broader ambition is to bring intelligent, autonomous responses into physical spaces where safety and operational efficiency depend on what happens next.
About Shyld AI
Shyld AI is a healthcare technology company bringing physical agentic AI to hospital operations.
Founded by CEO and Co-Founder Mohammad Noshad, Shyld AI develops autonomous physical agents that streamline hospital operations like infection control, OR efficiency, and compliance, without adding workload for staff. Its technology combines AI with UV disinfection to reduce environmental contamination by up to 93%. Shyld AI is deployed across hospitals nationwide, improving clinical care, efficiency, and cost savings. To learn more, visit www.shyld.ai/
