Auddia Inc. (NASDAQ: AUUD) announced that its LT350 business has signed a non-binding Letter of Intent with a NYSE-listed medical real estate investment trust to host LT350's first pilot installation at a hospital property in the Dallas Fort Worth metropolitan area. The Medical REIT owns and manages approximately 200 medical facilities across the United States, including hospitals, ambulatory surgery centers, and medical office buildings. This collaboration represents a strategic move to deploy distributed AI infrastructure directly at healthcare facilities where latency and data security are critical concerns.
The LOI outlines the parties' intent to collaborate on deploying LT350's first solar-integrated, parking-lot-based AI micro-datacenter canopy. LT350's patented architecture integrates modular GPU, memory, and battery storage cartridges directly into the ceiling of its proprietary solar canopy, enabling high-performance AI compute to be deployed above existing parking lots without absorbing parking spaces or requiring new land acquisition. The Medical REIT's portfolio contains approximately 4,000,000 square feet of parking lot space that could potentially support up to 960 MW of training or 350 MW of inference compute capacity.
"Healthcare is one of the most latency sensitive and data security intensive environments for AI inference," said Jeff Thramann, M.D., CEO of Auddia and founder of LT350. "We believe this LOI represents a meaningful validation of LT350's potential to deliver secure, high-performance, on-premise inference compute directly adjacent to clinical operations." The pilot will focus on validating LT350's ability to deploy high-performance AI compute directly at the point of need while supporting HIPAA-aligned inference workloads and reducing grid impact through solar generation and battery buffering.
LT350 estimates that approximately 18 months of design, engineering, and testing work will be required following the closing of Auddia's proposed merger with Thramann Holdings to stand up the first LT350 canopy with its integrated GPU, memory, and battery storage cartridges. Because LT350 represents a new class of distributed AI infrastructure, the company believes this timeline reflects the rigor required to validate performance, safety, reliability, and compliance in a hospital environment. If the pilot is successful, LT350 expects to expand across the Medical REIT's broader portfolio of almost 200 medical properties as applicable.
Under its proposed business model, LT350 anticipates entering into site-specific lease agreements with property owners for the use of parking-lot airspace and canopy infrastructure. This structure enables LT350 to deploy distributed AI datacenters without requiring land acquisition while providing property owners with a new revenue stream tied to AI infrastructure. The company believes this model aligns incentives between LT350 and its real estate partners and supports scalable deployment across large property portfolios. For more information about LT350, please visit www.LT350.com.
While advancing the engineering and testing required for the pilot, LT350 intends to pursue additional partnerships with healthcare systems, logistics operators, research campuses, and other organizations seeking to deploy distributed AI compute in parking-lot environments. The company believes that LT350's ability to turn underutilized parking lots into solar-powered AI micro-datacenters represents a compelling opportunity for property owners seeking to generate new revenue, hyperscalers looking to deploy AI compute closer to end users, and enterprise customers seeking to deploy highly secure AI capabilities on premise without acquiring land, increasing grid load, or compromising operational space.
This development comes as Auddia progresses with its proposed business combination with Thramann Holdings, which would combine LT350 with two other businesses under the new McCarthy Finney holding company. The LOI is non-binding and does not obligate either party to proceed with the pilot, but it represents a significant step toward validating LT350's innovative approach to distributed AI infrastructure in critical healthcare environments where data sovereignty and deterministic performance are paramount for AI-driven clinical and operational workflows.
