Company: ARAI
Filing Date: 2025-05-14
Form Type: DRS
Source: 0001641172-25-010170
Chunk: 76

Company: Arrive AI Inc.
Filing Date: 2025-05-14
Form: DRS
Chunk 76
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 company intends to develop proprietary ML and AI models specifically tailored to its unique data sets as operational data volumes increase. These efforts depend on collecting transactional, logistical, and locational data through AP deployments. For example, Arrive AI has demonstrated AI-based delivery chat capabilities for AP4 by using third-party foundational models. Further ML and AI development will occur during AP5’s evolution, contingent on achieving a critical mass of operational data.

We estimate an 18-month operational data collection timeframe will be required to reach critical mass for effective ML/AI training and improvements. Current estimates assume deployment of at least 200 actively utilized ALM Access Points, with a utilization rate exceeding 3 deliveries per day. Without achieving these deployment and utilization targets, critical mass may be delayed or unattainable. While machine learning and AI are expected to enhance operational efficiencies, Arrive AI also aims to leverage these technologies to generate additional revenue streams. For example, AI-enabled dynamic pricing, predictive maintenance, and tailored customer services are envisioned as revenue-generating applications within the ALM ecosystem.

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Machine Learning and AI Applications in Delivery, Pickup, Users, Environment, and Autonomous Logistics

Arrive AI plans to employ ML and AI algorithms across five key areas, but these plans are contingent on scaling operations and collecting sufficient data, which are not guaranteed. Below are examples of the data points and intended applications for each category, as well as associated risks:

1. Delivery

| ● | Data                                                                                           
 Collected: Package dimensions, delivery times, and customer identifiers.                       |
| ● | Planned                                                                                        
 Use: Classification algorithms could optimize package placement in ALM mailboxes, while        
 prediction models could anticipate delivery times to pre-emptively unlock mailboxes and reduce 
 theft.                                                                                         |
| ● | Associated                                                                                     
 Risks: Errors in predictions or classifications may result in misplaced deliveries, theft,     
 or customer dissatisfaction.                                                                   |

2. Pickup

| ● | Data                                                                                          
 Collected: Package return types (e.g., resell, refurbish, recycle) and pickup schedules.      |
| ● | Planned                                                                                       
 Use: Algorithms could classify return packages into categories to optimize reverse logistics. 
 Prediction models could anticipate return pickups to improve mailbox space management.        |
| ● | Associated                                                                                    
 Risks: Misclassification or delayed pickups could disrupt reverse logistics networks          
 or create customer dissatisfaction.                                                           |

3. Users

| ● | Data                                                                                          
 Collected: Usage frequency, delivery preferences, and transaction histories.                  |
| ● | Planned