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 bring distributed AI infrastructure directly to healthcare environments where data security and low latency are critical.
The LOI outlines plans to deploy LT350's solar-integrated, parking-lot-based AI micro-datacenter canopy, which integrates modular GPU, memory, and battery storage cartridges directly into the ceiling of its proprietary solar canopy. This architecture enables high-performance AI compute to be deployed above existing parking lots without absorbing parking spaces or requiring new land acquisition. Jeff Thramann, M.D., CEO of Auddia and founder of LT350, stated, "Healthcare is one of the most latency sensitive and data security intensive environments for AI inference. 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, support HIPAA-aligned inference workloads, reduce grid impact through solar generation and battery buffering, preserve all parking functionality, and demonstrate the operational and economic advantages of distributed inference. 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 components. 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 successful, LT350 expects to expand across the Medical REIT's broader portfolio of almost 200 medical properties. These properties include hospitals, outpatient facilities, and medical office buildings—locations where proximity, data sovereignty, and deterministic performance are critical for AI-driven clinical and operational workflows. Thramann added, "We view this pilot as the first step in a broader strategy to bring distributed AI infrastructure to healthcare campuses nationwide. Hospitals and medical facilities are among the highest-value inference environments, and we believe LT350 is uniquely positioned to serve them."
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. While advancing engineering and testing 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. For more information about LT350, please visit https://www.LT350.com. Additional information about Auddia is available at https://www.auddia.com.
