# Neutron **Neutron** is Vanar’s decentralized knowledge ecosystem. It takes scattered information, such as documents, emails, and images, and turns it into a structured network of intelligent data units called **Seeds**. Each Seed is a compact block of knowledge. It can include text, visuals, files, and metadata. Seeds are stored offchain for performance and flexibility, and optionally onchain for verification, ownership, and long-term integrity. This hybrid approach gives users both speed and trust. *** #### What Are Seeds? Seeds are the core building blocks of Neutron. A single Seed can represent: * A document, email, or image * A paragraph of structured content * A visual reference or caption * Linked information that connects across other Seeds * An optional onchain record that verifies authorship and timestamp Each Seed is enriched with AI embeddings, which makes it context-aware and highly searchable. You can search based on meaning, time, file type, or even visual similarity. *** #### How Do You Use Neutron? Most users interact with Neutron through **Kayon AI**, a personal business intelligence assistant that connects to platforms like Gmail and Google Drive. It helps you turn raw data into meaningful insights using natural language. > You can learn more about how Kayon works in the [Kayon AI section](/ai-tech/kayon-ai.md). *** #### Onchain Storage Neutron is designed with privacy and user choice at the center. By default, Seeds are stored offchain for fast access. For users who want additional security or auditability, there is an optional onchain storage layer powered by Vanar Chain. This adds features like: * Immutable metadata and ownership tracking * Encrypted file hashes for integrity verification * Transparent audit trails Only the document owner has the decryption key, ensuring that even onchain data remains fully private. This makes Neutron a strong fit for enterprise needs where compliance and verifiability are essential. --- # 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.vanarchain.com/ai-tech/neutron.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.