Key Takeaways:

  • Shyld AI combines real-time sensing, onboard AI, and UV-C disinfection to detect and address contamination risks directly in hospital rooms.
  • Its VERTEX foundation model enables autonomous, same-second decisions without relying on cloud processing, reducing network dependency and keeping environmental data on-device.
  • Peer-reviewed Stanford research published in the American Journal of Infection Control found a more than 93% reduction in cumulative microbial bioburden, supporting Shyld AI’s approach to infection prevention.

Aulis Capital recently led a $13.4 million seed round into Shyld AI, one of the larger early-stage raises in healthcare AI to date, and the company is now putting that money toward scaling its hardware into more hospital rooms nationwide. For founder and CEO Mohammad Noshad, the round marks years of work that started somewhere far more personal than a funding milestone.

A close friend of Noshad’s died from an infection contracted during a routine surgical procedure, a loss that led him and his brother Morteza to found Shyld AI in 2022. Their goal from day one was to build patient safety directly into the physical spaces where care happens, rather than reporting on problems after they’ve already occurred. Healthcare-associated infections still contribute to roughly 72,000 U.S. deaths each year, according to current CDC estimates, a number Shyld AI is working to bring down at every hospital that installs its technology.

A Device Built to Act, Not Report

Most healthcare AI generates a dashboard or a summary and hands the response to a person, which still leaves the actual fix waiting on someone with the time to do it. Shyld AI took the opposite approach and built hardware that handles the response itself, inside the room, the moment it’s needed. Its wall-mounted units run continuously once installed and stay active around the clock.

Each unit combines onboard sensors with a precise UV-C light source inside a single compact housing. The sensors read the room in real time and catch the exact moments that increase contamination risk: a shared keyboard touched by multiple patients, a surface left exposed during a room turnover. Once the system flags one of those moments, it fires a calibrated dose of UV-C light that clears the surface within seconds.

The intelligence behind that response runs on VERTEX, a foundation model that Shyld AI built specifically for real-time decision-making in physical spaces. Developed alongside NVIDIA, VERTEX processes everything on the device itself through onboard GPU compute, rather than checking in with a hospital’s cloud systems before every decision. That local processing keeps the system running through network outages and keeps all environmental data on the device, with no video or patient information ever leaving the room. From a hospital IT standpoint, installing a Shyld AI unit looks much more like mounting a smoke detector than deploying a new piece of clinical software.

What the Research Shows and Who Built It

The clinical case underlying Shyld AI’s approach has undergone formal peer review. Researchers at Stanford University published findings in the American Journal of Infection Control documenting a reduction of more than 93% in cumulative microbial bioburden when Shyld AI’s system ran against a control room using standard manual disinfection. The team tracked surface contamination over several weeks inside one of the busiest clinical units at a major academic medical center.

That research has turned into real commercial traction. More than 30 hospitals currently run Shyld AI installations, and typical sales cycles close in eight to ten weeks, well ahead of the twelve to eighteen months hospital technology procurement usually takes.

“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 says.

Mohammad and Morteza built the company from complementary backgrounds. Mohammad completed his PhD in 2.5 years, spent several years as an AI researcher at Harvard, and founded and exited two prior companies before founding Shyld AI. Morteza holds a PhD in computer science from Stanford and built the technical architecture behind VERTEX.

Where the Technology Goes From Here

Infection control is Shyld AI’s starting point inside a hospital, but the company’s larger goal is to bring this kind of AI into any physical environment where contamination or delay carries a real cost. Inside operating rooms, Shyld AI’s systems already track surgical readiness, catch missing instruments before a case begins, and flag delays before they become expensive, work that matters in a setting where every minute of OR time runs into the hundreds of dollars.

The company has also begun working with pharmaceutical manufacturers, bringing the same architecture to cleanroom contamination control in sterile manufacturing environments. Money from the seed round is funding new hospital deployments and the engineering work needed to adapt VERTEX for these additional settings.

Noshad measures the company against the category he’s trying to define, one that didn’t exist before Shyld AI built it: action-based AI for physical infrastructure. What started with one personal loss now runs across dozens of U.S. hospitals, working to prevent the same outcome for someone else’s family.

To learn more about Shyld AI’s technology or schedule a demo, visit the official website.