Empowering the Future of File Sharing

Discover the Power of Decentralization
and AI in Secure Data Transfer

Revolutionizing Data Transfer with Decentralization and AI

At WeSendit, our mission is to redefine file sharing and storage by integrating decentralization and artificial intelligence (AI) principles. In an era where data privacy and security are paramount, our platform stands as a testament to our commitment to these core values. By leveraging AI, we not only enhance the security and trust of our services but also provide our node Operators with the best possible protection against several unwanted types of data.

Our approach goes beyond mere file transfer; we aim to create a smarter, safer, and more seamless experience for our users and Web3 node system. Our AI-powered solution will safeguard our users and our network from unauthorized information, without infringing on individual privacy guidelines.

A key element of our ecosystem is the WeSendit node system, in which the transmitted data is broken down into fragments, encrypted and stored on different nodes. To protect the node operators and prevent misuse, each fragment is checked for irregularities using AI. The aim is to find suspicious content in the fragments. If there is any suspicion, the next fragment is retrieved and the process is repeated until the suspicion is confirmed or refuted. The use of a Large Language Model (LLM), which learns from the data transfer in the initial phase, further strengthens data control. With this approach, data can only be distributed to nodes that are authorized to store the fragments, which improves and later even guarantees the security and legality of the stored data.

Problem definition and solution


Traditional cloud storage and file sharing services face the daily challenge of preventing fraud, criminal activity and copyright infringement. Ensuring that no illegal content is distributed through their platforms requires extensive monitoring and control mechanisms, often at the expense of privacy and user experience. In addition to the challenges of centralized control and efficiency issues, this is a critical problem for Web2 providers.


WeSendit’s approach is to overcome these challenges through decentralization and the development of AI-supported solutions. Our goal is a platform that proactively identifies and ward off security risks without compromising user privacy. The decentralized structure strengthens data security and increases resistance to attacks and data loss. For our node network and therefore all the node operators as well as the current and future Web3 storage partners, such as Storj, Unigrid and Filebase, this development step provides a reliable basis for storing customers‘ data in a secure and efficient environment, free from concerns about data breaches and legal risks.


Building the
Future Together

Insights from the AI NVIDIA Conference
in San Jose

Exploring Partnerships and Innovations to Accelerate Our
Mission in AI and Decentralization


Fragment Discovery

By taking advantage of artificial intelligence, WeSendit does not analyse entire datasets. As our decentralized approach, otherwise, will be broken. Instead, with the fragment discovery approach, we ensure data privacy as long as there are no suspicious data segments.

Fraud and Anomaly Detection

By undergoing training and enhancing the parameters, the LMM will be capable of recognizing patterns and trends that indicate fraudulent activity or anomalies within the platform and node System. Through continuous learning, these systems can proactively identify potential threats and initiate countermeasures to prevent either node operators from storing malicious files or recipients from downloading illegal data in the first place.

Malware Detection

Fragment discovery helps us to detect suspicious content, but it also helps us to identify malware such as malicious code or ransomware, which is, because of encryption, not harmful to nodes but for recipients of the transfer.


Q2-Q4 2024: Planning phase: Problem definition and Concept creation

Goal: Build a solid basis with profound knowledge incl. circumstances


Q1-Q3 2025: Data Aquisition & Preparation: Plan and integrate API's

Goal: Serving the model with appropriate data from and to the application


Q4 2025-Q2 2026: Training and Model Development

Goal: Connecting platform, Nodes and WeSendit AI


From Q3 2026: MLOps: Refinement, Improvement, Monitoring

Goal: Continuous optimization and expansion of AI technologies


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