The problem it solves
Every company has a version of this: the answer exists somewhere, in a SharePoint site, a shared drive, a decade of PDFs, but finding it takes an email to the one person who remembers where, or a search that returns forty results and none of them are it. Multiply that by every employee, every week, and it is a real cost that never shows up on an invoice.
An assistant grounded in your own documents fixes the finding problem, not by being smarter in general, but by actually reading what you already have.
How it works
The system retrieves the specific passages relevant to a question from your documents, then answers using only those passages, with a citation to the source. It works over SharePoint, ERP exports, shared drives and scanned PDFs, and it never answers from outside your data, so if the information genuinely is not in there, it says so rather than inventing something plausible.
Typical starting points: an internal knowledge base for support or HR questions, a document search layer over years of contracts or technical manuals, or an assistant embedded directly in a CRM so an agent gets the answer without leaving the tool they already use.
For technical teams
This is retrieval-augmented generation done properly: retrieval is permission-aware, so a user only ever sees passages they already had access to, and every answer carries a citation back to the source document rather than a bare confidence score. Ingestion covers SharePoint, ERP exports, shared drives and scanned documents via OCR. Access is role-based, every query and answer is logged for audit, and both data and processing stay in the EU, not just storage. The model choice matters less than the grounding and permission model around it, which is where most RAG deployments actually fail.
Where this is going
For the mechanics in more depth, read RAG over your company data: an AI assistant that answers from your own documents.