Your own knowledge collections
Materials are split into fragments, turned into vectors and stored in a database. Each subject has its own collection, so answers from different areas do not mix.
RAG Platform on-premise
Search and answers over your own documents
The model answers questions from your materials rather than from the internet. Everything runs on your own hardware, so documents never leave the company network.
The problem
Company knowledge lives in procedures, correspondence, documentation and people's heads. Search by file name does not help here: people ask in words, not in document titles.
Cloud services solve the task, but they require uploading all of it outside. For part of the material that option is simply off the table.
How it works
Everything on your hardware
The vector database, the embedding model and the language model run on your server. Neither a document nor a question goes outside.
Access from your own services
Search is available through an API, so bots, internal services and staff tools all use it.
Ingestion discipline
Materials enter the database through a single pipeline with format checks. It is the boring part, and it is where most systems of this kind break.
What it gives
- Answers from internal materials instead of search by file name
- Confidential documents stay inside the company
- No cloud model spend for this task
- Search is built into existing services rather than living on a separate site
Who it is for
Companies with a large body of internal material and confidentiality requirements: legal documents, procedures, accumulated expertise.
Questions
Does the data really stay inside?
When installed on your hardware, yes. The vector database and the models run locally, and this design needs no external calls.
Which document formats are supported?
The usual working ones: plain text, markup, PDF, office documents, e-books. Everything goes through the same processing pipeline.
Why not just upload the documents to a chatbot?
You can, if there is little material and none of it is confidential. At volume it stops working: the model cannot hold everything in context, and the cost per request grows several times over.
What breaks most often in systems like this?
Ingestion. If materials enter the database in the wrong format, search formally works but returns nothing. We have been through that, which is why we keep a single pipeline with checks.
Other projects
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