Imagine you could ask a question — in plain language — and get an answer pulled from every contract, invoice, meeting recording, or product sheet your company has ever produced. Not a Google search. More like asking a very well-read assistant who has actually read everything.
OpenRAG is a tool that makes that possible, and it runs entirely on your own infrastructure. Nothing goes to Google, OpenAI, or anyone else.
What caught our attention is how many types of files it can actually understand. Send it a scanned PDF, a voice memo, a slide deck, or a photo of a whiteboard — it reads all of them, converts them into a consistent format, and makes them searchable and answerable.
Most AI document tools — the ones you pay a monthly fee for — work beautifully, but your files live on someone else's server. For legal contracts, client data, or anything sensitive, that's a real concern.
OpenRAG was built with what they call a "sovereign-by-design" philosophy. Your data stays where you put it. And because it speaks the same language as the most popular AI tools out there, you can connect it to things your team might already be using, without starting from scratch.
It's early days, but this is exactly the kind of infrastructure piece that quietly becomes load-bearing for a business.
RAG — Retrieval-Augmented Generation. The technique that lets an AI answer questions using your documents instead of just what it learned during training. Think of it as giving the AI a filing cabinet to look through before it answers.
OCR — Optical Character Recognition. The ability to read text from scanned images or photos — like turning a photographed receipt into actual searchable words.
Self-hosted — The software runs on a computer you control, not on a company's cloud. More responsibility, but also more privacy.
API-compatible — Means this tool can talk to other tools without needing a custom translator. Plug it in and it just works with what you already use.
If you have a growing pile of documents that nobody can find anything in, this is worth keeping an eye on. Worth bookmarking: https://github.com/linagora/openrag