Company: VEEAW
Filing Date: 2025-08-06
Form Type: S-1/A
Source: 0001213900-25-072342
Chunk: 106

Company: VEEA INC.
Filing Date: 2025-08-06
Form: S-1/A
Chunk 106
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, on VeeaONE platform to                                  
 hospitality vertical, commercial buildings, MDUs, public housing, hospitals, nursing homes,      
 and various energy production and refinery facilities among many others. Combined with AirLynx,  
 for network slicing, and/or SecureConnect, for network connectivity with cubersecurity, it       
 can provide for data subscription services to individual building functions - paid by building   
 owners or property managers. Examples of industrial installations to date are commercial         
 buildings, various industrial facilities and precision agriculture and Smart Farming delivered   
 to the edge standalone or together with Microsoft Azure FarmBeats applications.                  |

58 Today widely adopted AI, mostly in the form of LLM, resembles mainframe computing in the early days of computing. However, for everyday enterprise, industrial and consumer use cases at the edge, AI is becoming distributed. Edge AI extends the Cloud AI to the Device Edge, or within a short range of Device Edge, while augmenting Physical AI, which is AI embedded into machines, robots, or physical systems, allowing them to move, sense, interact, and respond intelligently to the physical world. As further explained below, edge computing and Edge AI have become synonymous as a “single system” at the edge, which may be offered in one fully integrated product or separately but securely networked or meshed together, depending on the nature of the use case. In a nutshell, Edge Computing together with Edge AI at the Device Edge and Physical AI are mutually reinforcing. Together, they form the technological foundation for the next generation of intelligent autonomous machines — reshaping manufacturing, logistics, mobility, healthcare, and smart environments. The rapid expansion of connected devices and the exponential growth of data generation at the network edge have challenged traditional cloud-centric AI processing models. The combination of Edge AI and edge computing converged with networking, as represented by VeeaONE, is not merely an optimization of existing network models but a fundamental shift that defines the future of AI-driven digital transformations. VeeaONE’s Edge AI capabilities can make all of this possible, not in theory, but in practice today. The convergence of edge computing with Edge AI provides for:

| ● | AI                                                                  
 inferencing pipelines optimized for near-real-time decision making; |

| ● | Hierarchical                                                              
 AI architectures - combining local sub-models with central/global models; |

| ● | Distributed                                                                     
 federated learning techniques enabling privacy-preserving distributed training; |

| ● | Efficient                                                               
 model updates and versioning through secure cloud-edge synchronization; |

The primary use cases that may