How does a RAG chatbot use company documents?
A RAG chatbot searches an approved corpus, such as policies, product sheets, or ticket history, and shows or cites the passages it used. The model writes the reply. The corpus, access rules, and review step decide whether the answer is usable in the business.
Leapfrog AI treats the corpus boundary as part of the scope. A pilot builds one agreed use case. An AI Audit (€3,000, one week) is the step when the document set, owners, and access rules are still open.
When is a RAG system the right offer?
RAG fits when people already answer the same questions from documents and the cost is the search, not the absence of a policy. A general assistant with no retrieval step answers from the model alone. That is a different product from a chatbot tied to your files.
The public comparison for financial-services teams is on the RAG chatbot versus ChatGPT Enterprise page. The build, when you want one workflow in production, is an AI Pilot from €20,000.
Common questions
What is a RAG chatbot?
A RAG chatbot retrieves passages from a defined set of documents and drafts the answer from those passages. Leapfrog AI scopes that corpus in an AI Audit (€3,000) or builds one agreed chatbot in an AI Pilot from €20,000.
Who owns the document set in a RAG chatbot?
A person owns which documents are in bounds, who may see them, and whether a wrong answer is acceptable. The system retrieves and drafts. The owner stays accountable for the source set.