Company: VEEAW
Filing Date: 2025-07-23
Form Type: S-1
Source: 0001213900-25-066815
Chunk: 93

Company: VEEA INC.
Filing Date: 2025-07-23
Form: S-1
Chunk 93
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 billion in 2025, and US$66.5 billion by 2030, with a 21.7% CAGR (2025–2030). It is important to differentiate between an investment into an Edge AI-enabling infrastructure company, such as Veea with VeeaONE platform, vs. a foundational AI model development company or other model developers (e.g., OpenAI or Edge Impulse), developing Large Language Models (“ LLMs”), Small Language Models (“ SLMs”), Agentic AI, federated machine learning, etc. In many cases, these models are freely made available on an introductory basis or longer-term in order to rapidly penetrate into a vast potential market. However, as headlined on a daily basis, AI models evolve at breakneck speed, typically holding the top spot for just a few weeks before newer, open-source variants emerge. 50 In contrast, once HCI is deployed, spanning compute, storage, LAN/WAN networking, including 5G, and virtualization, it forms a foundational, integrated backbone for IT/OT at the edge for many enterprises and even consumer use cases beyond Edge AI. It is the equivalent of the cloud computing at the edge, regardless of how and what applications are served up by the customers of AWS or Microsoft Azure that may come and go, the Cloud remains and evolves slowly. However, in the case of VeeaONE-based solutions, solutions are deployed to serve a primary AI-driven use case such as 5G-based broadband connectivity with AI-driven cybersecurity, an AI-powered building management system, AI-enabled precision agriculture, or a Smart City deployment. In general, VeeaONE platform is designed for delivering:

| ● | Long                                                                                     
 lifecycle with modular scaling, upgrades and low replacement frequency on a heterogenous 
 network.                                                                                 |

| ● | Simplified                                                                              
 operations, centralized management, and built-in redundancy ensuring high availability. |

| ● | Cost                                                                                       
 efficiency, resource consolidation, and resilience that support even heavy AI workloads    
 with a range of AI-accelerated third-party devices that are specifically designed to serve 
 the use case.                                                                              |

| ● | Adaptability,                                                                                   
 large number of open source models available to choose from for the edge use cases such         
 as frameworks that support edge-optimized inferencing, including TensorFlow Lite, ONNX Runtime, 
 PyTorch Mobile, NVIDIA TensorRT, Intel OpenVINO, or Edge-focused Models such as Latent AI,      
 Neural Magic or Edge Impulse.                                                                   |

| Feature