RAG / knowledge bases
A chatbot that answers from your documents — with sources.
RAG (retrieval-augmented generation) connects an AI model to your knowledge base: answers come from your documents and cite the source, instead of the model guessing. We build it on AWS — your data stays with you.
The problem
A model alone guesses. RAG answers from sources.
A public chatbot answers from what it "remembers" from training — it can be confident and wrong at the same time (a hallucination). For a company that is a risk: a wrong answer about a product, a procedure or a contract costs trust, sometimes money.
RAG reverses the order: it first retrieves the right passages from your knowledge base, then has the model answer strictly on that basis — with a link to the source. Your staff and customers can see where the answer comes from and verify it.
How we do it
From documents to a sourced answer.
The same pipeline we built for our own product — we transfer it into your organisation.
1. Order in the data (ETL)
We gather and clean the sources — documents, databases, pages, files — and version them in a single, controlled knowledge base. The hardest work is before the model, not inside it.
2. Knowledge base on AWS
We index the content in Amazon Bedrock Knowledge Bases — the native RAG mechanism on AWS. The data stays in your cloud account; it does not travel to public models.
3. Strict RAG with citations
The model is instructed to answer only from the retrieved passages and to show the source. When the base has no answer, it returns "I don't know" instead of making one up.
4. Evaluations and monitoring
We measure answer quality and cost per query automatically. The base and the prompts change over time — we keep the quality up, not "set and forget".
Proof
We built this ourselves — in production.
100%
AI extraction accuracy on packaging data (validation set, n=200)
$0.0006
cost per AI scan in production
302,516
drug-interaction records in the production knowledge base
mojApteczka production data, June 2026. In healthcare an "almost right" answer is dangerous — which is why strict RAG, source citations and automated evaluations were a requirement, not a decoration.
See the full case studyQuestions
Before you ask
No. We build the knowledge base in your own AWS account (Amazon Bedrock Knowledge Bases). The documents stay with you, under your control — which also matters for GDPR and trade secrets.
Have documents your AI should answer from?
Let's start with an audit: we'll check whether your sources are RAG-ready and how fast we can ship a working system — without experimenting on your budget.