> For the complete documentation index, see [llms.txt](https://docs.leadbay.app/doc/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.leadbay.app/doc/q-and-a/how-are-scores-calculated.md). # How are scores calculated? Every lead in Leadbay has a **score from 0 to 99**. Higher means more relevant to you. ### What the score measures The score reflects how similar a lead is to the leads that have worked for you — your **won deals** and **liked leads**. It's computed using AI embedding models that capture deep patterns beyond simple keyword matching. ### What influences the score * **Won deals**: the strongest signal. Leads similar to your past wins score highest. * **Liked leads**: likes tell the model what you find relevant. * **Qualification answers**: if the [AI Assistant](/doc/product-guides/ai-assistant.md) is configured, positive answers to your questions boost the score. * **Active lens**: each lens has its own AI model, so the same lead may score differently in different lenses. ### How to improve your scores * **Like and dislike** leads regularly in Discover — this trains the model * **Update lead statuses** (won/lost) — this is the best signal * **Configure qualification questions** — adds another scoring dimension * **Use focused lenses** — a lens targeting one market segment gives more precise scores than a broad one The score recalculates periodically as the model learns from your actions. --- # 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.leadbay.app/doc/q-and-a/how-are-scores-calculated.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.