RAG: make your AI answer from your own documents

A general language model like Llama or GPT has read the public internet, but it has never seen your invoices, contracts, or internal wiki. Retrieval-augmented generation (RAG) fixes this: at query time it feeds the model the right passages from your own documents, so the answer is grounded in your company knowledge and can cite the source.

The problem: a general model does not know your business

Ask a stock model who signed a specific contract, what your refund policy says, or which supplier shipped an order, and it will either refuse or invent an answer. The knowledge simply is not in its weights. Retraining the whole model on your files is slow and expensive, and it goes stale the moment a document changes.

Why RAG beats fine-tuning for company knowledge

Fine-tuning bakes information into the model itself. RAG keeps your documents in a separate index the model reads from on demand. For internal knowledge this wins on three counts:

• Cheaper. No GPU training runs; you only build an index and query it.
• Always current. Add or edit a document and it is searchable at once, with no retraining.
• Traceable. Every answer can point back to the exact file and passage it used, so staff can verify it.

How RAG works in four steps

• 1. Chunk. Each document is split into small passages of a few hundred words.
• 2. Embed. An embedding model turns each chunk into a vector, a list of numbers that captures its meaning.
• 3. Store. The vectors go into a vector database such as Qdrant or pgvector.
• 4. Retrieve and answer. At query time the system finds the chunks closest in meaning to the question, hands them to the LLM as context, and the model writes an answer with source references.

Your documents never leave your server

This matters most for a business under GDPR and NIS2. In a private setup the LLM, the embedding model, and the vector index all run on your own hardware in our Riga data centre. Your contracts and invoices are never sent to a public API, never used to train someone else's model, and never leave EU jurisdiction. You keep full control of who can query which documents.

What it can index and how to keep it current

RAG can point at the systems you already use: SharePoint, network file shares, and internal wikis. A nightly re-index picks up new and changed files, so the assistant answers from today's documents, not last month's. We tune chunk size, retrieval settings, and access rules to fit your content.

Set up RAG on your own AI server

Running RAG well means matching the LLM, the embeddings, and the vector store to your documents and your privacy rules. We build and manage the whole pipeline on a private AI server in our Riga data centre: GPU hardware, the retrieval stack, and a nightly re-index, so your team gets grounded, source-cited answers without a single document leaving your control. Contact us for a setup scoped to your data.