> For the complete documentation index, see [llms.txt](https://channelroute-ai.gitbook.io/channelroute-ai-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://channelroute-ai.gitbook.io/channelroute-ai-docs/live-human-detection/live-human-detection.md). # Live Human Detection Why recognizing a live person is one of the first decisions in an intelligent outbound communication workflow. ## Live Human Detection Live-human detection is one of the first decision points in the ChannelRoute AI workflow. In outbound communication, a connected call does not necessarily mean meaningful contact. Calls may reach voicemail, an IVR system, a busy line, a fax tone, background audio, or another automated endpoint. Detecting a live person early allows the system to make better routing decisions. Rather than treating every answered call the same, ChannelRoute AI explores methods for estimating whether a real conversation is possible before additional time is invested. Areas of interest include: * Live-human detection * Voicemail identification * Interactive voice response (IVR) detection * Early call classification * Communication workflow optimization * Human-in-the-loop decision support The goal is not simply to automate outbound communication. The objective is to help people spend more time speaking with the right person and less time navigating calls that cannot lead to a productive conversation. --- # 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://channelroute-ai.gitbook.io/channelroute-ai-docs/live-human-detection/live-human-detection.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.