> For the complete documentation index, see [llms.txt](https://docs.predicthq.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.predicthq.com/api/beam/overview.md). # Overview Beam is PredictHQ's relevancy engine. It determines which types of real-world events are materially relevant to your business by analyzing your historical demand data - per location. Event impact varies by geography, industry, and demand profile, so Beam quantifies which event signals consistently explain changes in your demand - and your models train on signal instead of noise. The primary output is a set of [Feature Importance](/api/beam/analyses/get-feature-importance.md) results and an `analysis_id`. To apply demand-calibrated filtering automatically, pass the `analysis_id` to the [Features API](/api/features/get-features.md) and [Events API](/api/events/search-events.md) - the relevant event categories, rank thresholds, and location scope, with no manual configuration. Without Beam, feature selection is a manual guess. Run one Analysis per location and refresh it monthly by appending new demand data. For many locations sharing a single model, use [Analysis Groups](/api/beam/analysis-groups/get-an-analysis-group.md) to aggregate Feature Importance into one consistent feature set. ## Guides Read more about Beam: * [What is Beam?](/getting-started/core-concepts/what-is-beam.md) * [Beam guides](/getting-started/guides/beam-guides.md) --- # 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 by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://docs.predicthq.com/api/beam/overview.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. 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.