Secure End-to-End Encrypted Company Chatbot

The problem

Company knowledge was spread across shared drives, email threads, wikis, chat history and file servers, in every format from scanned PDFs to recorded training videos. Staff lost hours searching or asking colleagues, and new hires took months to learn where things lived.

Public AI assistants were ruled out. Sending confidential files to an outside model broke data policy, and none of them could respect who is allowed to see what: an assistant that answers a junior analyst with content from a board paper is worse than no assistant at all.

Approach

We built one encrypted knowledge store that every file goes into, whatever shape it arrives in. Scanned paperwork is read with OCR, recordings are transcribed, and images are captioned, so a question can be answered from a photographed form or a training video as easily as from a wiki page.

Access is checked before anything is retrieved, not after an answer is written. Each employee sits in an access tier, and a question only ever reaches the files that tier is cleared for, so an analyst and a director asking the same question get different answers drawn from different sources. Conversations are encrypted end to end between the employee's device and the store, every answer cites the files it came from, and every query is written to an audit log.

Outcome

Staff ask the company's own knowledge a question in plain language instead of hunting through drives or interrupting a colleague, and they get an answer with its sources attached. New joiners stop depending on whoever has been there longest to find out where things live.

Because the checks sit in front of retrieval and every query is logged, the platform gives the same answer quality to a director and to an analyst without either of them seeing something they should not.