Company: VEEAW
Filing Date: 2025-08-14
Form Type: 424B4
Source: 0001213900-25-076086
Chunk: 93

Company: VEEA INC.
Filing Date: 2025-08-14
Form: 424B4
Chunk 93
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 Edge AI enables
a new generation of autonomous machines and experiences that were once confined to science fiction. We believe that the societal impact
will be profound. Edge AI market size in 2024 was on the order of US$20.8 billion, and is expected to reach US$24.9 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.

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.

<div align='center'>50</div>

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.