Company: ARAI
Filing Date: 2025-07-15
Form Type: S-1/A
Source: 0001641172-25-019572
Chunk: 87

Company: Arrive AI Inc.
Filing Date: 2025-07-15
Form: S-1/A
Chunk 87
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 failure to achieve results and goals.                                                                                                    |

AI and ML Plans and Capabilities

Arrive AI aspires to establish product-market
fit with AP5 and, at that point, anticipates being able to meet the needs of a large group of customers, consistent with projections
such as those described in ARK Invest’s Annual Innovation Report in the Autonomous Logistics section. Achieving this milestone
depends on Arrive AI developing a product that is easy to use, competitively priced, and supported by strong marketing and sales strategies.
Only after establishing product-market fit will Arrive AI pursue the next steps of scaling its business, data collection, and services,
with a goal of achieving sustainable revenue and growth by 2027. There is no guarantee that Arrive AI will successfully achieve these
objectives or that market adoption will occur as planned.

Arrive AI aspires to scale its operations in
a way that produces unique datasets that could potentially serve as the foundation for future monetization through Machine Learning (ML)
and Artificial Intelligence (AI). This goal is aspirational and dependent on achieving operational scale and adoption, which 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