Company: ARAI
Filing Date: 2025-06-17
Form Type: S-1
Source: 0001641172-25-015428
Chunk: 75

Company: Arrive AI Inc.
Filing Date: 2025-06-17
Form: S-1
Chunk 75
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 that we will        
 successfully develop or implement these tools, or that the ALM Platform will achieve the intended functionality or market acceptance.   |
| 3. | AI and ML technology leveraging two distinct applications of artificial                                                                 
 intelligence and machine learning: (1) edge-based inferencing models running directly on Access Points for real-time operations,        
 and (2) centralized AI systems analyzing network-wide data to generate business insights and ecosystem improvements. We have already    
 demonstrated initial AP4 AI capabilities to customers, with comprehensive implementation planned for 2025 delivery. We plan to develop  
 further advanced AI capabilities for AP5 in 2025 and 2026. The effectiveness and value of our AI/ML models are directly correlated      
 with delivery network utilization and the associated transactional data volume. We expect to accumulate 12-18 months of operational     
 and delivery data, reaching the critical mass necessary to begin improving the value of our proprietary ALM models and insights.        
 This unique and growing dataset will encompass transactional patterns, operational performance metrics, and environmental conditions    
 across our network, creating a valuable proprietary asset for improving autonomous last-mile delivery operations. Note that to meet     
 our data accumulation goals and maximize the effectiveness of our proprietary ALM models, we believe a minimum of 200 deployed and      
 actively utilized ALM Access Points, with an average daily volume of 3 deliveries, will be necessary to generate sufficient data        
 over 18 months of operations. If fewer units are deployed or the average utilization rate per unit falls below 50%, the rate of data    
 accumulation will slow, delaying both the expected timeframe for likely AI improvements and the monetization of the resulting insights. 
 These metrics—deployment scale and utilization rate—are critical to achieving the desired scale for AI/ML and network-wide              
 operational improvements.                                                                                                               |

Management believes that Arrive is pioneering
the emerging market for the automated exchange of packages and goods between people, robots, and drones with our autonomous last mile
(“ALM”) mailbox but there are no guarantees that we are right or will be successful in our efforts. We are advancing our
technology through structured customer and partner trials or pilots for which the Company receives no revenues. All pilots operate under
executed Statements of Work (SOW). Active customer agreements and SOWs differ in that SOWs outline specific deliverables and project
scope for pilot activities, such as unit deployment schedules, performance testing protocols, and feedback milestones, while active customer
agreements define broader terms of collaboration, such as pilot objectives