Presens Network and Pundi AI have announced a partnership to provide real-world data on-chain, paving the way for the advancement of blockchain-based Artificial Intelligence. With this partnership, both companies will combine DePIN data with their own respective verifiable data ecosystems for use in supporting the development of AI. Announced on January 21, 2026, the partnership will connect spatiotemporal data collected from physical environments to the AI infrastructure created using blockchains by allowing builders to access legitimate human activity patterns that will allow them to build the next generation of Intelligent Systems.
Bridging Physical Reality and Blockchain Intelligence
Presens Networks creates a novel of intelligence from presence signals collected from millions of everyday devices and provides insights into human activity patterns based on their location over time without violating user privacy or requiring the collection of personal identifiable information (PII). The Pulse Nodes on Presens Network provide aggregated, anonymized presence signal data to enable many applications, including adaptive traffic management systems and secure robotics placement systems, while preserving user anonymity.
The collaboration established between Pundi AI and the other partner organizations creates an exciting opportunity for both parties to create value by providing real-world contextual data to those developing AI technologies. The integration of this data into the development of AI technologies will give builders that are developing in the Pundi AI Ecosystem the opportunity to train their models with actual data reflecting human activity, rather than just using synthetic/data scraped from the internet.
Decentralized Infrastructure Meets AI Training Data
The partnership fills an urgent void in the current development process of artificial intelligence. Currently, most of the available AI systems are being trained using non-dynamic and static databases that are incapable of embracing real-time changes in human behavior and the environment. The union of Presens Network and its DePIN infrastructure with the Pundi AI and its approach to tokenization and verification provides builders with datasets with time and location indication.
Pundi AI has developed tools for decentralized AI development through the ability for data contributors to tokenize their data into Dataset Tokens (DTOKs) which can be monetized and traded on the Data Pump. The partnership with Presens Network provides an extra dimension of real-world context to this ecosystem beyond that of traditional labelling of data.
Implications for AI Agents and Autonomous Systems
This partnership will have a major impact on the future of AI agents and autonomous systems. Developers who create AI agents using platforms such as HyperGPT and 4AI will now be able to add real-world situational information using Presens Network data. For example, using anonymized crowd density data may enable delivery agent systems to improve route optimization; and for robotics systems to use spatio-temporal information while navigating through public locations, thus enhancing safety for those navigating through areas populated by others.
The partnership illustrates a broader transition to decentralized sensor networks and AI enhanced predictive modelling. In this relationship, both Presens Network and Pundi AI place a strong emphasis on community ownership and transparency. They will ensure that the data associated with these systems can be easily verified, can be publicly managed, and is not subject to any one centralized organization’s authority.
Conclusion
By establishing a collaborative arrangement, the collaborators Pundi AI and Presens Network will deploy a decentralized method of building an AI infrastructure capable of addressing some of the largest issues faced when developing AI technology. The partners have developed a solution that uses tokens to evaluate and establish safe, trustworthy electronic presence for AI systems. Verification with blockchain technology enables the execution of these functions in a single, unified AI solution with minimal problems.
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