OriginTrail (TRAC) is an ecosystem constructing a trusted knowledge infrastructure for artificial intelligence (AI). It tackles misinformation in the AI age by verifying information origins and making critical knowledge and real-world assets (RWAs) discoverable.
The project aims to build a verifiable Internet for decentralized AI by supporting major brands and developers. It employs its proprietary Decentralized Knowledge Graph and the NeuroWeb blockchain to provide AI-driven search and solutions for global enterprises and individuals.
OriginTrail has secured partnerships with prominent entities including British Standards Institution, SCAN, Polkadot, Parity, Walmart, World Federation of Hemophilia, Oracle, the EU Commission's Next Generation Internet, and Microsoft Entrepreneurship for Positive Impact. These collaborations help advance its trusted knowledge base and its use in trillion-dollar sectors, establishing a verifiable Internet of knowledge crucial for RWA economies.
To observe the technology, one can examine ChatDKG, an experimental question-answering bot on X (Twitter) that relies on trusted Knowledge Assets within the OriginTrail Decentralized Knowledge Graph (DKG). ChatDKG delivers responses using OriginTrail’s trusted AI.
The Metcalfe phase of the roadmap targets creating the world’s largest verifiable Internet for AI, comprising 100 billion Knowledge Assets and achieving a 100,000x scalability boost. This phase intends to generate network effects across the OriginTrail DKG and use new methods for autonomous DKG growth, building on the Genesis knowledge foundation established by organizations and individuals. The Genesis component of this phase also launches knowledge mining and signaling features to sustain the growth of the verifiable Internet.
After the Genesis stage and the shift to an AI-native V8, the Convergence stage will introduce further capabilities. By utilizing AI and DKG progress, the project plans to enable autonomous knowledge mining, leading to an autonomous DKG. In this stage, new knowledge enters the DKG with minimal human input. Services like AI agents will conduct knowledge inferencing directly on the DKG to identify and fill “blind spots,” while also seeking new knowledge to add to the DKG via knowledge signaling.