Internal knowledge search
Over procedures, policies and internal documentation.
02 · RAG systems
RAG systems for your company’s knowledge.
RAG (retrieval-augmented generation) lets an AI assistant answer questions from your own documents and data, and show the sources it used. We design, build and hand over RAG systems for growing companies, starting with the work most projects skip: getting the content ready.
In one paragraph
What is RAG, and when does a company need one?
The system first finds the relevant parts of your documents, then an AI model writes the answer from those parts and shows where they came from.
It fits when your teams spend time searching procedures, policies, tickets or product documentation. Read our RAG readiness checklist
Key takeawaysAnswers come with sourcesContent is checked before buildingTested against known answers
What we build
What can a RAG system do for your team?
Assistants for support and CS
Based on product documentation.
Question answering over long documents
Such as contracts or specifications.
Answers in your tools
Integrations that bring answers into the tools your team already uses.
Who it is for
Do you need a RAG system?
Good fit
- Teams search procedures and policies by hand.
- The same questions go to the same few experts.
- Onboarding depends on documents nobody can find.
- An AI pilot gave confident answers that turned out to be wrong.
Not sure yet?
The Technical Diagnostic maps your systems, data and workflows in 2–3 weeks and tells you what to build first.
See the Technical Diagnostic →How we work
How do we build a RAG system?
Four steps, from readiness to a system your team owns.
- 01
Readiness first
We check AI maturity, data ownership, maintenance, trust and compliance, and whether to build or buy.
- 02
Build
Document ingestion, retrieval, answers with sources and access control, built with LlamaIndex and Python.
- 03
Test
We check answers against a set of questions with known answers before anyone relies on the system.
- 04
Hand over and maintain
Documentation, named owners for the content, and a process to keep it current.
Tools
Built with
- LlamaIndex
- Python
- n8n
- APIs
Proof · from the blog
We document a real RAG project in public, step by step: the series “Hey Dad, tell me how to RAG!”.
Related services
Often combined with
Most projects touch more than one area.
FAQ
Frequently asked questions
Not sure where to start? The Technical Diagnostic is the first step for every build.
RAG or fine-tuning?
RAG fits when answers must come from documents that change and need sources. Fine-tuning changes how a model writes or behaves; it is not a good way to keep it up to date with your knowledge.
How long does it take?
We start with the 2–3 week Technical Diagnostic. The build runs in sprints of 4–6 weeks; the scope depends on how ready your content is.
How do you keep answers accurate?
Each content area has an owner, documents are updated through a defined process, answers show their sources, and we test against known questions after changes.
Where does our data stay?
We are based in the EU and work with GDPR in mind. Where your data is stored and processed is agreed with you during the diagnostic.
What does it cost?
The build is scoped after the diagnostic. See the Technical Diagnostic
Proof
Trusted by high-growth teams
“Mateusz has a unique ability to bridge the gap between marketing, sales, and tech. He brings a rare mix of technical acumen and strategic thinking that consistently pushed our projects forward.”
Tell us what slows your team down.
A 30-minute call. We’ll tell you what we’d build first.
What happens next
- 130-min callWe look at your situation and tell you what we would build first.
- 2Technical Diagnostic2–3 weeks, fixed scope: audit report and roadmap.
- 3Build in sprints4–6 weeks each, documented and handed over.