Consulting & AI Assessment
We assess your current digital and AI capabilities, identify high-value opportunities, and build a clear roadmap for responsible and effective AI adoption.
Learning Platforms
From UX and architecture to scalable backend and QA, we deliver robust platforms that improve your business results
AI & Data Products
We design and build deep-tech AI products — including intelligent tutors, learning analytics engines, recommendation systems, content-generation pipelines, and many more.
Quick AI Automation
Projects run in weeks, not months, helping your company to reduce manual work and boost efficiency without large budgets or complex IT transformations.
E-commerce
We help e-commerce businesses scale sales and operations using AI, data, and custom digital platforms.
Education & Training
We help education institutions modernize learning and operations using AI, data, and digital platforms.
Industrial Manufacturing
We deliver digital tools that optimize production, and reduce operational waste. From MES enhancements to automation and real-time analytics
Consulting & AI Assessment
We assess your current digital and AI capabilities, identify high-value opportunities, and build a clear roadmap for responsible and effective AI adoption.
Learning Platforms
From UX and architecture to scalable backend and QA, we deliver robust platforms that improve your business results
AI & Data Products
We design and build deep-tech AI products — including intelligent tutors, learning analytics engines, recommendation systems, content-generation pipelines, and many more.
Quick AI Automation
Projects run in weeks, not months, helping your company to reduce manual work and boost efficiency without large budgets or complex IT transformations.
E-commerce
We help e-commerce businesses scale sales and operations using AI, data, and custom digital platforms.
EdTech projects development
Through in-depth research and analysis, we identify opportunities for growth, target audience insights, and the most effective channels to reach them.
Industrial Manufacturing
We deliver digital tools that optimize production, and reduce operational waste. From MES enhancements to automation and real-time analytics
RAG System for Intelligent Knowledge
Unified intelligent tool through which any employee (engineer, operator, manager) could pose a question in natural language.
Introduction
Kazakhmys Corporation, a large mining and metallurgical holding company in Kazakhstan, faced the challenge that employees across divisions could not quickly and reliably get answers from their extensive internal knowledge sources—regulations, protocols, reports, internal databases, instructions. The client wanted to build a unified intelligent tool through which any employee (engineer, operator, manager) could pose a question in natural language and receive an accurate, sourced answer drawn exclusively from up-to-date internal knowledge. The project involved creating an MVP prototype, with target for future piloting and further deployment and scaling.
Business Challenges
Heterogeneous and fragmented data formats
Ensuring response accuracy and trustworthiness
Latency, scalability, and concurrency
Architecture & Concept
Architecture & Concept We designed a RAG approach (Retrieval + Augmented Generation) comprising:
• Retriever / Search component — finds relevant knowledge fragments from the internalknowledge base (semantic/vector search).
• Generator / Summarizer — given the user query plus retrieved fragments, generates a relevant answer in the form of query-oriented extractive multidocument summary.
• Filtering / Verification module — ensures the generated answer stays within permittedinternal sources and rejects speculative content.
• Generator / Summarizer — given the user query plus retrieved fragments, generates a relevant answer in the form of query-oriented extractive multidocument summary.
• Filtering / Verification module — ensures the generated answer stays within permittedinternal sources and rejects speculative content.
Development Process
1. Data consolidation & preprocessing o Audit and inventory knowledge sources (what documents, databases, systems)
2. Embedding & indexing o Select embedding model (e.g. Sentence Transformers, OpenAI embeddings, or domain-adapted model)
3. Quality control / validation
4. Pilot, feedback loop, iteration & scaling o Launch pilot version in a single division o Collect user feedback, refine retrieval, improve prompts
- Normalize formats, chunk documents, attach metadata (tags, date, author, division)
- Deduplicate, cleanse, and review for sensitive content
2. Embedding & indexing o Select embedding model (e.g. Sentence Transformers, OpenAI embeddings, or domain-adapted model)
- Index embeddings in a vector database (Milvus, Weaviate, Pinecone, FAISS, etc.)
- Tweak search semantics and ranking algorithms
3. Quality control / validation
- Check thresholds: refuse answers when evidence is insufficient
- Fallback logic (e.g. "I'm not confident—please consult an expert")
- Monitor metrics: accuracy, answer rejection rate, user feedback
4. Pilot, feedback loop, iteration & scaling o Launch pilot version in a single division o Collect user feedback, refine retrieval, improve prompts
How it Works?
1) A user types a question in the chat or portal
2) The backend converts the query to an embedding, performs vector search → retrieves top N fragments
3) These fragments + the original query are passed to the LLM, which generates a response
4) The answer is returned to the user, along with citations / source links
5) Admin dashboard tracks usage, flags weak answers, shows statistics
2) The backend converts the query to an embedding, performs vector search → retrieves top N fragments
3) These fragments + the original query are passed to the LLM, which generates a response
4) The answer is returned to the user, along with citations / source links
5) Admin dashboard tracks usage, flags weak answers, shows statistics
Results
Dramatic reduction in time to get internal answers
Employees now receive relevant information in seconds.
Reduced load on subject-matter experts
Internal ad hoc questions to experts dropped by 30–50 %.
High user satisfaction
Percentage of accepted / accurate answers (per user feedback)
Strengthened organizational knowledge culture and use of data