> 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/data/foundational-data/headcount.md). # Headcount {% embed url="" %} Headcount looks simple until you define the entity and the scope. Kernel treats it as a researched firmographic field: the number of people directly employed by the company and on its payroll. It is headcount, not FTE. Kernel does not count contractors, consultants, agency workers, franchise employees, gig workers, or everyone connected to a brand. If a subsidiary has its own payroll, those employees belong to that subsidiary. They are not automatically counted as direct employees of the parent legal entity. ## How Kernel determines headcount Kernel starts from the resolved entity: legal name, trading name, website, LinkedIn profile, entity category, parent structure, and regional scope. It then checks available evidence across official filings, annual reports, company websites, investor pages, press releases, credible news, public registry data, LinkedIn company data, and other supporting sources. Each source has tradeoffs. Filings are strong when they report the right scope, but often report a consolidated group. Company websites can be useful but may be rounded or stale. LinkedIn can help, but associated profile counts and size ranges are not treated as truth by default. Kernel weighs the evidence by source quality, recency, entity scope, geography, industry, age, and growth profile. When no reliable direct figure exists, Kernel can estimate the value and marks it as estimated. ## Fields | Field | Description | | ------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `headcount.count` | Entity-level payroll headcount for the specific legal entity. | | `headcount.count_consolidated` | Consolidated headcount for the entity plus its subsidiaries, when available. | | Headcount primary value | The recommended value for CRM and export workflows. It uses consolidated scope for holding companies and group parents, and entity-level scope for operating companies, subsidiaries, and business units. | | `headcount.confidence` | High, Medium, or Low confidence, based on the strength of the supporting evidence. | | Headcount source | Whether the figure was identified directly from evidence or estimated from supporting signals. | | `headcount.reasoning` | Plain-language explanation of the source, date, scope, method, and caveats. | The important point is that Kernel keeps scope visible. Entity-level headcount tells you the precise payroll for the resolved entity. Consolidated headcount shows group scale. The primary value tells you which one to use for the account Kernel identified. --- # 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/data/foundational-data/headcount.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.