> For the complete documentation index, see [llms.txt](https://docs.kernel.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.kernel.ai/concepts/entity-database.md). # Entity database Kernel's entity database pairs traditional structured data with *entity memory:* semi-structured context, timelines, and notes drawn from raw, multi-source unstructured data (websites, PDFs, job posts, company filings, and more). This grounds every entity record in primary sources and lets users go beyond Kernel's core fields to extract the exact data points they care about, at a fraction of the cost of doing it from scratch. #### Sources Kernel draws on a variety of sources to populate coverage and context 1. **Web crawling:** Kernel's web crawler, e.g., crawling Unilever's website for all brands 2. **LinkedIn**: Kernel maintains a database of 70M LinkedIn account profiles, which is updated monthly 3. **Company regsitries:** Company registries and public databases - full list coming soon --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## 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.kernel.ai/concepts/entity-database.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.