RAG is an approach where AI retrieves relevant information from your data and uses it to generate accurate, context-aware responses. We help companies apply RAG in a practical way, connecting AI models with internal knowledge to support real business decisions.
We help companies design and build AI systems that work with real businessdata and deliver answers teams can rely on in daily operations. Our focus is onsolutions that are stable, explainable, and ready for long-term use.
Our team supportsclients from early assessment to delivery, always keeping business context andrisk in mind.
Retrieval-Augmented Generation connects large language models with selecteddata sources such as internal documents, databases, or APIs. Instead of relyingonly on pre-trained knowledge, the system retrieves relevant information firstand then generates responses based on that context.
For businesses, this approach improves accuracy, keeps answers aligned withcurrent data, and makes AI outputs easier to control and trust. This makes RAGsuitable for products and internal tools where accuracy and context arecritical.
We usually see the strongest impact in scenarios where AI needs to work withstructured knowledge and real workflows, including:
Retrieval-Augmented Generation helps AI work with the right information at the right time. By retrieving relevant data first, it produces more accurate and context-aware responses that better match user questions. This makes AI outputs more useful across different business needs, from customer support and content generation to data analysis and personalized recommendations. Because RAG builds on existing information instead of generating everything from scratch, it is also more efficient and easier to adapt to specific domains, workflows, and requirements. This is where RAG services help reduce noise, improve relevance, and support consistent decision-making.
We have been working with data-driven systems, search technologies, andAI-powered products for many years. RAG is a part of a complete solution thatincludes data structure, access control, performance, and maintenance.
Clients choose us when they need customRAG development services that fit their data, industry, andproduct roadmap. Our role often goes beyond delivery. Also, as RAG consultants, we help teamsunderstand trade-offs, review existing setups, and decide how far RAG should betaken in their specific context.
For teams that need continuity after release, we offer “RAG as a service” -a managed engagement to maintain and improve RAG quality as data andrequirements change.
If you are considering it for a product or internal system, we can help youevaluate feasibility, design the right architecture, and move forward withconfidence.
We offer consultations and delivery support focused on real business use,data quality, and long-term sustainability.
This is the process of designing andbuilding AI systems that retrieve relevant information from selected datasources and use it to generate accurate, context-aware responses. It focuses ondata structure, retrieval logic, and integration with real business systems.
This is anongoing engagement where a team supports, maintains, and improves a RAGsolution over time. This typically includes data updates, quality monitoring,and adjustments as business needs and content change.
This is an approach where a languagemodel retrieves relevant information from external data sources beforegenerating a response, ensuring outputs are grounded in current and trustedinformation rather than only in training data.
Jakub Bílý
Head of Business Development
.webp){kind=icon}
