RAG: AI-powered search over your company knowledge base

Companies accumulate a vast number of internal documents: procedures, instructions, scripts, contracts, support replies. Finding the right piece of information in that volume is sometimes harder than answering the question itself. RAG is the approach that solves exactly this problem.

What RAG is in plain language

RAG stands for Retrieval-Augmented Generation. The idea is straightforward: when a user asks a question, the system first searches for relevant passages in your document base, and then a language model composes an answer based on what it found. The model doesn't invent — it relies on real sources.

How RAG differs from an ordinary chatbot

A standard chatbot works from scenarios: it answers what it was trained on and knows nothing specific about your company. A RAG system, by contrast, reads your documents and answers from them. If a procedure says one thing and practice says another, the system will state what is written and point to the source.

Where it works well

RAG performs well in situations where accuracy and a citation matter:

• Search over internal documents — an employee asks a question and the system finds the right section of a procedure or contract
• Customer support — the bot answers from an up-to-date knowledge base, not from pre-written scripts
• Onboarding new employees — a new hire gets answers about processes without pulling colleagues away from their work
• Regulatory queries — legal, accounting or technical regulations become accessible through a conversational interface

Why answers are accurate and cited

A language model on its own knows nothing about your documents. In a RAG system the model only sees the passages retrieved from your base by the search step. This constrains the model's tendency to improvise and makes it possible to show the source of every statement. The user sees not just the answer but where it came from.

What you need to get started

To launch a RAG system you need:

• Documents in any readable format: PDF, Word, Google Docs, Notion, HTML
• A clear picture of who will use the system and what questions it should answer
• Access controls if some documents are confidential

We take your existing materials, index them, configure the search and the language model, and integrate the interface — whether that's a web page, a Telegram bot or a website widget. The system runs on your own infrastructure or in the cloud.

If you have a large body of documents that employees or clients struggle to find quickly, RAG addresses exactly that problem.