Company: ARAI
Filing Date: 2025-03-24
Form Type: S-1/A
Source: 0001641172-25-000350
Chunk: 67

Company: Arrive AI Inc.
Filing Date: 2025-03-24
Form: S-1/A
Chunk 67
---
, excessive manual intervention, or technical incompatibilities. For example, an ALM Access Point might integrate automated delivery data validation and synchronization features, ensuring that users can share and retrieve up-to-date information effortlessly about a delivery, even across different platforms. Such features are designed to eliminate redundant steps and reduce the risk of errors, thereby fostering a smooth and uninterrupted exchange experience that is frictionless.

Arrive AIexpects to have three primary 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 monetizationvia 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. More details on our hybrid approach to AI and ML development and the timing of development and foundational data capture necessary for improvements beyond synthetic training are presented in the Business section

3. Operational platform fees