Has anyone successfully trained an AI on their company documents without it giving false information?
We have been trying to build an internal chatbot for our team to search through our own brand guidelines, internal SOPs, and past client briefs. The main goal was to give everyone a single source of truth so nobody has to spend hours digging through chaotic Google Drive folders.
Instead, it has been an absolute disaster. It honestly feels like the smarter the model is, the more confidently it lies about our own private data.
We started out with a basic RAG setup using a standard vector database. We chunked up our company PDFs, turned them into embeddings, and hooked them up to a popular open source model. It worked fine for super basic questions like asking what our hex codes are for a specific brand color palette. However, the moment a team member asked something complex, like comparing an older client strategy brief with a newer update, the AI completely broke down. It started blending old and new data together, creating a weird hybrid strategy that we never actually created.
After that, we tried tweaking the chunk sizes and overlap settings to see if it would capture context better. That just made it worse. Now, instead of missing information, it pulls random paragraphs from completely unrelated client folders just because they happen to use similar marketing words. We even looked into fine tuning, but the cost and data requirements seem way too high for a small team just trying to read their own files.
It keeps hallucinating facts, making up fake project deadlines, and pulling outdated metrics with absolute confidence.
Has anyone actually gotten this to work reliably in a real work environment? Did you have to abandon the basic plug and play AI tools entirely, or is there a specific trick to cleaning up the files before the AI reads them?