Company: ARAI
Filing Date: 2025-04-18
Form Type: S-1/A
Source: 0001641172-25-005394
Chunk: 83

Company: Arrive AI Inc.
Filing Date: 2025-04-18
Form: S-1/A
Chunk 83
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 are not guaranteed. The platform envisions employing several ML applications, including classification, prediction, interpretation, and generation, to create value, enhance delivery performance, and increase revenue potential. These outcomes rely on sufficient deployment and utilization of ALM Access Points to collect the data necessary to train and refine these models.

Hybrid Development Approach and Proprietary Software

Arrive AI employs a hybrid development approach to create AI and ML capabilities. This approach leverages foundational third-party models from NVIDIA, Google, Azure, OpenAI, and AWS, along with open-source software like LAMA. At this time, none of Arrive AI’s software is proprietary, though the 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,