RAG in plain words: how our AI knows your specific business

The main complaint about AI assistants is 'it speaks in generic terms'. Ali-E answers differently: about your clients, your deals, your stock. Behind this is a technology called RAG. Let's break it down simply.

What's wrong with ordinary AI

A language model knows 'the world on average' but nothing about your company. Ask it 'which invoices should I collect?' and it fundamentally can't answer, because it doesn't see your data.

What RAG is

RAG (retrieval-augmented generation) is an approach where, before answering, the system finds the relevant pieces of your data and feeds them to the model as context. The model then answers from those facts, not 'in general'.

How it works for us

Your company's data (clients, leads, deals, products, invoices) is turned into 'embeddings' — numeric representations of meaning — and stored in a database. When you ask a question, the system instantly finds the most relevant fragments and passes them to the assistant with your question.

Why it's reliable

The assistant relies on facts from your base instead of inventing them. And the RAG layer is built gently: if for some reason no data is found, the system simply answers from the modules' live data — no errors, no empty replies.

What it means for you

You get an assistant that's genuinely aware of your company: it advises on a specific client, finds 'quiet' clients, answers questions about your numbers. That's the difference between a toy and a working tool.

All of this already works in beta. Ask the assistant a question about your data — and feel the difference.