Proprietary technology turns any parking lot into a revenue generating datacenter delivering AI inference at the edge without absorbing parking spaces

Supports the fastest, most secure, and lowest cost inference runs for the highest paying customers handling the most sensitive data

LT350 accounts for approximately 50% of McCarthy Finney's $250 million DCF valuation

BOULDER, Colo., Feb. 25, 2026 (GLOBE NEWSWIRE) -- Auddia Inc. $(AUUD)$ ("Auddia" or the "Company"), today announced a comprehensive strategic overview of LT350, a distributed AI compute business engineered to address two of the most urgent constraints in the AI infrastructure market: GPU underutilization and grid-constrained datacenter deployment. LT350 is one of three new businesses that would be combined with Auddia in the new McCarthy Finney holding company if Auddia's recently announced business combination with Thramann Holdings, LLC ("Thramann Holdings") is completed.

LT350 represents a breakthrough in AI infrastructure design protected by 13 issued and 3 pending patents, creating a defensible, highly differentiated deployment platform for distributed AI infrastructure. Unlike large, centralized datacenters, LT350 aims to deploy a network of small, interconnected datacenters across parking lots without absorbing any parking lot space. Instead of utilizing containerized or ground mounted micro data centers, LT350 integrates modular GPU, memory, and battery cartridges directly into the ceiling of its proprietary solar parking-lot canopy, transforming the airspace above the parking lot into a revenue generating high performance AI compute datacenter optimized for inference.

"Hyperscalers built the training layer," said Jeff Thramann, CEO of Auddia and founder of LT350. "LT350 is building the distributed inference layer -- one that we believe will be faster to deploy, cheaper to operate, and dramatically more energy efficient, while generating premium revenue for premium inference compute services."

The Company believes LT350 creates numerous advantages in the datacenter space.

A New Infrastructure Model for the Inference Era

AI workloads are shifting from centralized training to real-time, distributed inference, creating demand for compute that is:

   -- Physically close to data sources 
 
   -- Less dependent on strained regional electrical grids 
 
   -- Faster to deploy 
 
   -- More cost predictable 
 
   -- Aligned with data sovereignty and compliance requirements for sensitive 
      data 

LT350's canopy-integrated architecture enables high-performance compute to be deployed directly at the point of need -- in the parking lots of hospitals, financial campuses, research parks, logistics hubs, and autonomous-vehicle depots -- without displacing parking or requiring new land acquisition.

"I believe LT350 solves the three constraints that define the next decade of AI infrastructure: latency, power, and land," said Thramann. "By integrating compute into the ceiling of a patented solar canopy, LT350 preserves all parking functionality while creating a new, revenue-producing layer of AI infrastructure above it. This is a structurally advantaged platform for the inference era for many reasons."

Designed for High-Value, Regulated, and Latency Sensitive Workloads

LT350's architecture is purpose-built for customers who require deterministic performance, physical data sovereignty, and proximity to operations. Target verticals include:

   -- Hospitals and health systems requiring HIPAA-aligned inference 
 
   -- Financial institutions needing low-latency model execution 
 
   -- Defense and aerospace organizations with strict isolation requirements 
 
   -- Biotech and research campuses running sensitive workloads 
 
   -- Autonomous-vehicle fleets needing local data offload and model updates 

By placing AI compute mere feet from these environments with secure, direct connections, LT350 delivers performance and assurance levels that management believes centralized cloud datacenters cannot match. Inference customers with the specialized compute requirements that match to what LT350 aims to deliver are typically the highest paying customers. LT350 is not competing with hyperscalers on price. Instead, LT350 complements hyperscalers by serving inference workloads that cannot be efficiently or compliantly handled in centralized cloud datacenters, thus competing in the space by providing the highest quality inference services for the highest sensitivity data.

Power-Sovereign Architecture for a Constrained Grid

LT350 supports the grid by integrating solar generation and battery storage directly into each canopy, enabling:

   -- Behind-the-meter power buffering 
 
   -- Peak-shaving 
 
   -- Curtailment resilience 
 
   -- Reduced interconnection requirements 
 
   -- Predictable long-term power economics 

This design aims to position LT350 to scale even as utilities, regulators, and hyperscalers face mounting grid constraints.

Parking-Lot Deployment: Faster, Cheaper, Zero Land Cost

LT350 deploys in existing parking lots, leveraging the elevated canopy ceiling to preserve all parking functionality. This creates three structural advantages:

   -- Zero land acquisition costs and readily available sites adjacent to the 
      best customers 
 
   -- No loss of parking as a non-revenue generating asset converts to revenue 
      generation 
 
   -- Faster deployment as zoning, permitting, and environmental hurdles are 
      minimized 

We believe the result is deployment in months, not years, with materially lower capex.

A New Economic Model for Inference Infrastructure

By combining modular GPU deployment, solar-plus-storage energy systems, and parking-lot-based datacenters, the Company believes LT350 delivers a fundamentally different cost and performance profile for AI compute:

   -- Higher utilization by matching GPU cartridge deployment to inference need 
 
   -- Higher revenue from delivering higher quality inference services 
 
   -- Lower energy costs from solar generation and off peak battery charging 
 
   -- Reduced grid impact from solar and batteries 
 
   -- Faster deployment due to parking lot availability and no infrastructure 
      upgrades 
 
   -- Improved resilience inherent in a distributed AI network 

For information about LT350, please visit www.LT350.com.

About LT350, LLC

LT350 is a distributed AI data center company with 13 issued and 3 pending patents on a proprietary solar parking lot canopy infrastructure platform that integrates modular battery storage and GPU cartridges into the ceiling of the canopy to turn any parking lot into an AI data center. LT350 aims to build the most secure, lowest latency, cost effective, and rapidly deployed network of distributed AI data centers at the edge by leveraging the use of underutilized parking lot space while strengthening the existing power infrastructure of local utilities.

About Auddia Inc.

Auddia, through its proprietary AI platform for audio, is reinventing not only how consumers engage with AM/FM radio, podcasts, and other audio content but also how artists and labels promote their music and gain access to mainstream radio audiences. Auddia's Discovr Radio is the first music-promotion platform to deliver artists guaranteed exposure to radio listeners. Auddia's flagship audio superapp, called faidr, delivers multiple industry firsts, including:

   -- Ad-free listening on any AM/FM music station 
 
   -- Content skipping across any AM/FM music station 
 
   -- One-touch skipping of entire podcast ad breaks 
 
   -- Integrated artist discovery experiences 

For more information, visit www.auddia.com

Cautionary Note on Forward-Looking Statements

Certain statements in this communication, other than purely historical information, may constitute "forward-looking statements" within the meaning of the federal securities laws, including for purposes of the "safe harbor" provisions under the Private Securities Litigation Reform Act of 1995, concerning Auddia, Thramann Holdings, and the proposed merger between Auddia and Thramann Holdings (the "Proposed Transaction") and other matters. These forward-looking statements include, but are not limited to, express or implied statements relating to Auddia's and Thramann Holdings' management expectations, hopes, beliefs, intentions or strategies regarding the future including, without limitation, statements regarding: the structure, timing and completion of the proposed merger by and between Auddia and Thramann Holdings, and the expected effects, perceived benefits or opportunities of the Proposed Transaction; the combined company's listing on Nasdaq after the closing of the Proposed Transaction; expectations regarding the structure, timing and completion of the financing needed to close the Proposed Transaction, including investment amounts from investors, timing of closing of the Proposed Transaction, expected proceed, expectations regarding the use of proceeds, and impact on ownership structure; the anticipated timing of the closing; the expected executive officers and directors of the combined company; each company's and the combined company's expected cash position at the closing and cash runway of the combined company following the proposed merger and any additional financing; the future operations of the combined company, including research and development activities; the nature, strategy and focus of the combined company; the development and commercial potential and potential benefits of any products and services of the combined company; the cash balance of the combined entity at closing; expectations related to the anticipated timing of the closing of the Proposed Transaction (the "Closing"); the expectations regarding the ownership structure of the combined company; the expected trading of the combined company's stock on Nasdaq under the ticker symbol "MCFN" after the Closing; and other statements that are not historical fact.

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February 25, 2026 06:00 ET (11:00 GMT)