Company: ARAI
Filing Date: 2025-07-15
Form Type: S-1/A
Source: 0001641172-25-019572
Chunk: 90

Company: Arrive AI Inc.
Filing Date: 2025-07-15
Form: S-1/A
Chunk 90
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 revenue streams

| 1. | Mailbox-as-a-service (MaaS) provides our ALM Access Points or                                                                             
 ALM mailboxes to both businesses and consumers through a single, monthly subscription fee. This turnkey service includes hardware,        
 software, support, maintenance, installation/uninstallation, and financing for long-term field assets. Our flexible payment structure     
 accommodates various business models in the evolving ALM Industry. In Q4 of 2024 we installed AP3 units (third generation Arrive          
 Points) for which we will provide MaaS in 2025. Arrive AI has a confidential agreement and pricing in place to provide these services     
 for compensation, with an East-Coast Specialty Pharm company. The terms and pricing are confidential and preliminary terms, intended      
 for both parties to learn from the pilot project how effective the terms are in providing sustainable benefits and economics over         
 the course of the first year of operations. Once Arrive AI and the customer have operationalized and learned from these initial services, 
 both parties plan to renegotiate terms and pricing in 2026 to produce new terms that are sustainable and can be disclosed to other        
 customers and the public.                                                                                                                 |
| 2. | Data monetization via models and insights generated by machine                                                                            
 learning and artificial intelligence (“ML” and “AI”). Machine learning facilitates our systems’ ability                                   
 to learn and improve from experience using data patterns, while artificial intelligence encompasses broader capabilities and models       
 to simulate human intelligence and decision-making. We plan to use both technologies distinctly:                                          |

| a. | Machine Learning: Primarily deployed in our AP4 and AP5 Access                                                                       
 Points devices for local IoT (Internet of Things) data processing, edge computing (inferencing) for environment and transactional    
 models, and interactions models for drones and robots.                                                                               |
| b. | Artificial Intelligence: Used more broadly to analyze and derive                                                                     
 insights from our network’s transactional and environmental data through complex AI models, but we will also leverage foundational   
 AI models like ChatGPT or LAMA for device based human interactions.                                                                  |
| c. | After 12-18 months of delivering MaaS, Arrive AI should have collected                                                               
 sufficient data to begin to better leverage a growing dataset with AI and ML models for enhanced services and insights for customers 
 and partners. – Note that to meet our data accumulation goals and maximize the effectiveness of our proprietary ALM models,          
 a minimum of 5,000 deployed and actively utilized ALM Access