RAG System & AI Knowledge Base that answers from your own documents — with sources
A-LUX builds retrieval-augmented generation (RAG) systems that read your contracts, regulations, 1C records and support history, then answer staff questions in seconds — every answer backed by a link to the exact source paragraph. No hallucinations, no data leaks.
- Answers cite the exact document and page — verifiable, not invented
- Runs on a private LLM: your data never leaves your infrastructure
- Connects to 1C, Kaspi, CRM, WhatsApp Business and file storage
A RAG (retrieval-augmented generation) system is an AI assistant that searches your company's own documents and answers questions with links to the exact source. A-LUX in Almaty builds these on a private LLM so your data stays in-house, with integrations to 1C, Kaspi and WhatsApp Business, starting from ₸1,200,000.
What a RAG system actually does
Instead of forcing staff to dig through folders, a RAG assistant retrieves the right passages from your knowledge base and writes a grounded answer with citations.
Search across everything
Contracts, internal policies, technical manuals, 1C exports, email threads and PDFs are indexed into one semantic search layer. Ask in plain language — Russian, Kazakh or English — and get the relevant paragraphs back instantly.
Answers with sources
Every response shows the document name, page and a link to the original. Your team verifies in one click, so the assistant is trusted for legal, finance and compliance questions, not just small talk.
Grounded, not guessed
The model only answers from retrieved content. If the knowledge base has no answer, it says so instead of inventing one — the difference between a usable corporate tool and a liability.
Always current
When a regulation or price list changes, you update the source document and the assistant reflects it on the next query. No retraining, no stale answers baked into a model.
Built on a private LLM — your data stays yours
For Kazakhstani companies handling customer, financial and contractual data, sending documents to a public chatbot is not an option. We deploy the model where you control it.
On-premise or private cloud
We run open-weight models (Llama, Qwen, Mistral) on your servers or a dedicated VPS. Documents, embeddings and queries never touch a third-party API. Full audit log of who asked what.
Role-based access
HR sees HR documents, finance sees finance. Permissions mirror your existing structure, so the assistant never surfaces a salary table to the wrong department.
Hybrid when it makes sense
Need top-tier reasoning for complex tasks? We can route non-sensitive queries to Claude or GPT while keeping confidential retrieval local — you decide the boundary.
Compliance-ready
Data residency inside Kazakhstan, encrypted storage, and a clear data-flow diagram for your security team. Built to satisfy internal infosec review from day one.
Integrations with the tools you already run
A knowledge base is only useful when it speaks to your live systems. We connect the assistant to the platforms Kazakhstani businesses depend on.
1C as a data source
Pull product specs, inventory, pricing and order history from 1C so staff can ask "what's the stock and price for SKU X" and get a live, sourced answer — not a screenshot from last month.
WhatsApp Business assistant
Expose the knowledge base to your support team or customers through WhatsApp Business. The bot answers FAQs from your real documents and escalates to a human when needed.
Kaspi & payments context
Connect order and payment data so the assistant can answer status and reconciliation questions for your sales and finance teams, with links back to the transaction.
CRM, email & storage
amoCRM, Bitrix24, Google Drive, SharePoint, Confluence and shared folders all feed the index. One assistant, every knowledge silo, no copy-paste.
How A-LUX delivers — and why us
19 years in business, 400+ delivered projects, and a team that ships production AI rather than demos. Here is how a RAG project runs with us.
Discovery & data audit
We map your document sources, access rules and the real questions staff ask daily. You get a clear scope, integration list and fixed price before a line of code is written.
Pilot in 4 weeks
We index a first knowledge domain and deliver a working assistant your team can test on real questions. You measure accuracy and source quality before scaling to the whole company.
Evaluation & tuning
We build a test set of your toughest questions and measure answer accuracy, citation correctness and refusal behaviour — then tune retrieval until the numbers are right.
Support & ownership
You own the code, the model and the data. We provide SLA support, monitoring and a simple admin panel so non-technical staff can add documents themselves.
FAQ
Answers to common questions before ordering from A-LUX.
What is a RAG system in simple terms?
RAG stands for retrieval-augmented generation. Instead of relying on what an AI model "remembers", the system first retrieves the most relevant passages from your own documents, then asks the language model to write an answer grounded in those passages — and shows you the source. This makes answers accurate, verifiable and specific to your company.
How is this different from just using ChatGPT?
Public ChatGPT doesn't know your contracts, your 1C data or your internal policies, and it can invent plausible-sounding but wrong answers. A RAG knowledge base answers only from your documents, cites the exact source, and runs on a private LLM so confidential data never leaves your infrastructure.
Will our documents stay confidential?
Yes. We deploy an open-weight model on your servers or a dedicated VPS inside Kazakhstan. Documents, embeddings and queries are processed locally with encrypted storage and full access logging. Nothing is sent to a third-party API unless you explicitly approve it for non-sensitive tasks.
Which systems can the knowledge base connect to?
Commonly 1C, Kaspi order and payment data, amoCRM and Bitrix24, WhatsApp Business, Google Drive, SharePoint, Confluence and shared file storage. If your data lives in a database, a file or an API, we can index it.
How much does a RAG system cost?
Projects start from ₸1,200,000. The final price depends on the number of data sources, integrations (1C, Kaspi, WhatsApp), the volume of documents and whether you need on-premise hosting. After a short discovery call we give a fixed quote.
How long does it take to launch?
A working pilot covering your first knowledge domain is typically ready in about 4 weeks. Full rollout across all departments and integrations usually runs 2–3 months depending on data complexity.
Does it work in Kazakh and Russian?
Yes. The assistant handles questions and documents in Kazakh, Russian and English, and can answer in the language the user writes in — useful for mixed teams and customer-facing WhatsApp support.
How do you prevent the AI from making things up?
The model is constrained to answer only from retrieved content, every answer carries a source link, and we build an evaluation set of your hardest questions to measure citation accuracy. If the knowledge base has no answer, the assistant says so rather than guessing.
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