Document-trained knowledge base

GPT-trainer turns uploaded files, pasted text, and website pages into the material a chatbot draws its answers from. A Beam agent watches for a new visitor question, checks it against that trained knowledge base, and replies only with content the account owner approved for the bot to use. The agent can note each exchange in a connected help desk or spreadsheet so the team keeps a record of what visitors asked. When a question falls outside the trained documents, or the match is weak, the agent stops and passes the conversation, along with the question, to a person instead of guessing.

Multi-source data ingestion

A GPT-trainer bot can pull its training material from more than one place: uploaded documents, plain text entries, and pages crawled from a website. Where account permissions allow it, a Beam agent refreshes the bot's sources on a schedule, re-reading the connected pages or files and folding any changes into what the bot can answer from. This keeps the chatbot's answers aligned with the latest version of a policy page or product doc instead of an outdated copy. If a source fails to load, or a page has changed in a way that looks incomplete, the agent notifies a person rather than leaving the bot to answer from stale or partial material.

Escalation on low-confidence answers

Every question a GPT-trainer chatbot receives carries some level of match against its trained data, and a Beam agent reads that signal before replying. Where the match is strong, the agent lets the bot answer directly. Where it is weak, ambiguous, or touches a topic the account owner flagged as sensitive, the agent holds the reply back and routes the conversation to a person instead, along with the visitor's question and the closest passages the bot found. The handoff can post into a shared inbox or chat channel so a human sees the context immediately rather than starting the conversation over from nothing.