The rapid evolution of Artificial Intelligence is fundamentally tied to the availability of high-quality data. The acquisition and annotation of data are critical processes, as the volume and accuracy of data directly shape the effectiveness and precision of AI models.

The AI sector is confronting a range of challenges when it comes to data training. To resolve these, we must take a collaborative approach, combining technological innovation with regulatory standards. This will ensure both the quality and compliance of data training efforts. Only by addressing these issues can AI models more accurately and comprehensively reflect the complexities of the real world, driving further advancements and productivity transformations powered by AI technologies.

A Decentralized Full-Stack AI Data Solution powered by a Comprehensive Data Authentication and Authorization System

TSPACE is a full-stack AI data solutions platform, establishing a cross-border data authentication protocol, creating a permissionless hub for data collection, labeling, management, and trading. The logic starts from utilizing Web 3's crowdsourcing advantages for data labeling and collection. Subsequently, we leverage the core essence of blockchain technology to form a permissionless data authentication and trading system, solving the prevalent problems of data silos, data authentication chaos, and lack of professional annotators in the current data landscape. Data, much like the foundational layer of blockchain, is the bedrock upon which artificial intelligence is built. In the same way that Bitcoin is nothing without its ledger, AI models are mere vessels, requiring the lifeblood of data to function. The journey of an AI model’s understanding begins with its training data, which sets the upper limits on its potential and capabilities. It is no surprise that the vast majority of resources in AI development—as much as 80%—are spent not on the models themselves, but on the data. Streamlining this process is not just a matter of efficiency but a necessity for the advancement of AI.

TSPACE steps in as a full-stack AI data solutions platform that merges the principles of decentralization, as seen in Bitcoin, with the transformative potential of AI. By utilizing blockchain technology, TSPACEestablishes a decentralized protocol for data authentication, building a cross-border, permissionless ecosystem for data collection, labeling, management, and trading. This approach is a direct counter to the centralized control of data in the Web 2 world, offering a more equitable and secure alternative.

Through the use of cryptography and smart contract technology, TAGGER has developed a decentralized data authentication system that transcends borders. Building on this foundation, we have created a comprehensive data solutions platform that fuels AI data innovation with AI-assisted tools and data certification technology. We invite millions of knowledgeable and creative Web 3 participants to collaborate with us in this endeavor.

TAGGER’s platform not only transforms how data collection, annotation, and trading could be done, but how it should be done. By addressing the traditional challenges in collecting and annotating AI datasets, we provide unparalleled solutions that redefine the data annotation industry.

We have developed a comprehensive AI data solutions platform under the TSPACE data authentication protocol, inviting millions of Web 3 participants to join forces in revolutionizing the data industry through real incentives. Our platform addresses the fundamental flaws of traditional Web 2 data industries, creating new data value while accelerating the advancement of AI technology. With seamless onboarding and encrypted payments, TSPACE is the platform where developers and Web 3 participants can collaborate to generate true data value.

TSPACE leverages the general concept of DePIN, incentivizing participants to collect and share data within the framework of the data authentication protocol, overcoming the challenges of data acquisition in the Web 2 landscape. By integrating AI knowledge and innovative AI assistance tools, TSPACE supports human efforts, resolving the shortage of professional data annotators. This ensures that even those without industry-specific expertise can create high-quality, accurate datasets. Additionally, TSPACE has established a decentralized data management and transaction system, transforming data circulation and solving the global data silo issue.

TSPACE has introduced the first data authentication protocol based on cryptography and smart contract technology, marking the creation of a global data rights system under Web 3. This protocol ensures the protection of data owners' rights in a decentralized and trustless environment. By employing deep learning technology, we encrypt data, which is then stored and transmitted through a decentralized network. Advanced encryption algorithms safeguard sensitive data, preventing leaks and misuse. Ownership of data is verified through unique data rights certificates, granting exclusive control to the certificate holder.

Through blockchain technology, TSPACE enables the verification of a dataset’s origin, usage history, and ownership, providing data owners with full traceability and authentication. Our system establishes a unified standard for data transactions and authorized use, pioneering a global certification and trust framework that elevates the protection of digital intellectual property. This drives the rapid development of AI technology by ensuring that data is secure, traceable, and used in a manner that respects the rights of its owners.

Data annotation is a labor-intensive and time-consuming process, heavily reliant on the expertise and precision of annotators. Since the quality of data annotations directly affects the performance of AI models, ensuring high-quality annotations is a critical task. As AI moves toward real-world applications across various vertical domains, professional datasets have become highly sought after to train or fine-tune models for specific industries.

However, the shortage of high-quality professional datasets is largely due to the fact that general annotators lack the specialized knowledge required for accurate annotation in fields such as medicine, biology, mechanics, and industry. Additionally, there is a significant lack of professional annotators in these fields. The path to improving annotation quality lies in providing better training, leveraging intelligent annotation assistance tools, and implementing rigorous quality control measures.

2025 Q1

• Platform Launch

• Data Authentication Protocol Release

• Internal Testing

• Data management system completed

• Data Marketplace Completed

• AI Data Collection System Launch

2025 Q2

• Social Media Engagement

• Data management system release

• Broaden AI Copilot Tool's Supported Industries and Database

• Data Marketplace Release

2025 Q3

• Deploy Decentralized Data Collection Hardware Network

• Wearable Personal Health Data Collection Device Launch

• Build a developer community

• Establish a complete data ecosystem

2025 Q4

• Comprehensive AI data solutions service

• Agent-based data retrieval and usage service

• AGI Smart Contract launch

As AI models become increasingly tailored to specific industries, their training demands vast amounts of high-quality, industry-specific data. However, access to diverse and authentic data is hindered by regulatory, privacy, and commercial sensitivity constraints. This creates a major barrier to unlocking AI’s full potential in transforming industry productivity.

The current state of data collection is further complicated by the absence of unified standards for data gathering and classification, leading to inefficiencies in both the collection process and dataset management. Datasets circulating today often suffer from biases, as samples fail to represent the true diversity of the real world. This results in poor model performance in certain scenarios or with specific groups, sometimes even producing discriminatory outcomes. The solution lies in creating more comprehensive and balanced datasets, while applying debiasing techniques to address data imbalances.

While crowdsourcing has shown promise as a potential solution, the challenge remains in providing sufficient incentives for individuals to contribute and share their data. Without a decentralized structure and proper incentives, the system will continue to face limitations.

TSPACE envisions the construction of a comprehensive information gathering interface, with the goal of enabling the exhaustive acquisition, processing, and analysis of both structured and unstructured data. This initiative aims to establish the largest global data infrastructure, laying the foundation for a new era of data-driven innovation.

To achieve this, TSPACE plans to develop a diverse range of end-user informational devices, including intelligent terminals, Wi-Fi networks, meteorological sensors, and satellites. In addition, TSPACE will foster ongoing collaborations with leading enterprises in the field of data acquisition, unlocking the immense global value embedded within data through collective efforts.

The fragmented nature of data regulations across various countries creates a chaotic environment for data authentication. This presents a major obstacle to the development of the data industry. As a result, the full potential of data value, rights, and privacy remains unrealized, while issues around data security—spanning storage, transmission, and usage—are frequent. In the rapidly expanding AI industry, the need for data is urgent, making the establishment of a cross-border data authentication system critical.

Such a system must integrate encryption, access control, and secure transmission to ensure the integrity and confidentiality of data. It should empower data owners with full control over their assets, including the rights to manage, transact, authorize use, or even destroy their data. However, relying solely on current Web 2 technologies renders the creation of this system nearly impossible.