Ask in plain language
Employees write the question the way they would ask a colleague. The assistant handles the search.
Olzhas
A large company always has more knowledge than it can serve. Magnum has internal regulations, onboarding and day-to-day work instructions, materials for every role — and a constant stream of people who ask their manager, a colleague, or whoever is nearby instead of opening the document. Olzhas is the layer that sits between the employee and the existing knowledge base. The employee asks a question. The assistant finds the right piece of content and answers from it.
Problem
Employees always asked.
The knowledge base existed and was kept up to date. It was just hard to use in the moment: too many documents, no quick way to land on the right paragraph, no time to read everything when you have a shift starting.
So employees did the natural thing — they asked a manager, asked a colleague, asked the person next to them. The team ended up answering the same questions week after week, and onboarding always took longer than it should.
- A large library nobody had time to search through.
- Managers and colleagues spent hours a week on the same repeat questions.
- New employees waited on people to explain what was already in the docs.
Solution
A single chat over everything the company has already written down.
Olzhas is a RAG assistant on top of Magnum's internal knowledge. The team uploads materials in the admin panel. The system indexes them. Employees ask questions and get answers grounded in the company's own content.
The goal was not to be clever. The goal was to take the most-repeated questions off managers and colleagues and give new employees an answer in the moment, not in a meeting two days later.
Answer from the company's own content
Answers are grounded in indexed internal materials, so the assistant stays inside what the team has actually approved.
Stay current
The team updates the knowledge base in one place. The assistant picks up new and changed material as it is added.
Run from one panel
An admin panel for the editors to upload, replace and remove materials without an engineering ticket.
Technical work
RAG built around the way the company actually keeps content.
The model is the boring part. The interesting part is how the source material is structured, indexed and refreshed when the team drops in a new policy.
We built a RAG pipeline that ingests internal documents, splits them in a way that matches how Magnum writes them, and keeps the index in sync with the admin panel. The chat layer is multilingual, because the company runs in more than one language.
RAG pipeline tuned to the structure of internal regulations and operations content.
Admin-driven re-indexing — the team uploads, the system absorbs.
Multilingual answers grounded in the source material.
Operator-friendly admin panel for content lifecycle.
Python backendRAG architectureMultilingual LLMAdmin panel
What changed
Managers stop being a search engine
The recurring questions move from people to the assistant. The team keeps the harder work where it belongs.
New employees ramp faster
The answer to "how do we do X" arrives in the moment of the question, not in a follow-up message the next day.
The knowledge base finally gets used
Material the company already produced now has a usable surface. Updating a document immediately reaches everyone.
Tell us what you're building.
Start with a few detailsWe reply within one business day. Then Azamat joins every first call personally, so you get an honest scope, budget, and fit from the person responsible for delivery.