# Overview Brainstems is a pioneering decentralized infrastructure designed to revolutionize the field of artificial intelligence coordination and enterprise-level federated learning. Through Brainstems, businesses are empowered to collaborate seamlessly, harnessing the collective power of their aggregated intelligence to enhance industry-specific AI models. This innovative approach leverages federated learning, ensuring exceptional AI intelligence while maintaining data privacy and eliminating the need for centralized data pooling. ## What does Brainstems do? Brainstems operates as a Decentralized Federated Intelligence Mesh (DeFIM), providing a trustless environment for businesses to coordinate and train AI models on disparate datasets. Enterprises, including direct competitors and supply chain partners, can join forces within the Brainstems ecosystem to train models collaboratively while preserving data privacy. The Brainstems ecosystem consists of key participants, including Federation Partners, Stewards, Nodes, and Validators, each playing crucial roles in ensuring the network's functionality and integrity. In order to ensure the economic security and integrity of the Brainstems network, participants utilize STEMS, the native utility token of the platform. STEMS tokens are ERC-20 tokens used to allocate resources within the network, including computation resources for model training. Active participants, including Federation Partners, Stewards, and Nodes, can contribute to the network and earn rewards proportional to their contributions and STEMS stake. These rewards incentivize active participation and help drive the growth and sustainability of the Brainstems ecosystem. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.intellex.xyz/intellex-ai-protocol-ecosystem/overview.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.