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Scaling a business brings natural risks. One of them is increasing workload across teams whose daily duties often include time-consuming tasks like reviewing contracts, searching through documents, or answering customer questions. These actions drain time, energy, and focus that could otherwise be spent on growth, decision-making, or innovation.
Retrieval-Augmented Generation (RAG) offers a smarter path forward. This approach combines advanced large language models (LLMs) with your business data to deliver precise and contextually relevant answers. It pulls knowledge from internal documents, structured data, and public content to deliver instant value.
We will create an intelligent chatbot assistant that helps you respond faster, make better use of your internal knowledge, and align seamlessly with your business processes as they evolve.
Let us know you are interested by filling out the form. We will contact you to learn more about your challenges and explore how RAG-based AI could work in your context. Together, we will plan a free Proof of Concept that lets you test the solution with your own data and workflows.
Better use of internal knowledge reduces time spent searching for information or preparing reports. Your team focuses on value-generating tasks while repetitive actions become automated.
A data-driven AI system supports employees daily. It helps locate key information, draft summaries, and provide real-time responses. As a result, decisions are made faster and collaboration improves.
We combine a selected large language model with your company data to create a solution designed for your specific industry, teams, and processes. It could be a chatbot, quoting tool, or financial analysis assistant.
By introducing RAG-based solutions that draw from your company knowledge, you show clients and partners that your business uses modern tools in practical ways. It improves process maturity and your public image.
Your documents stay in your environment. The system accesses only what you allow and never shares anything externally. Encryption, permissions, and secure architecture help you stay compliant with GDPR and other industry standards.
Employees and customers quickly find answers from your internal content without digging through folders. This makes interactions smoother and increases satisfaction across the board.
We have delivered more than 50 GenAI projects, including Retrieval-Augmented Generation (RAG), helping companies organize their internal knowledge, reduce time spent searching for information, and improve customer service quality. As a result of these implementations, our clients have observed:
Every day, you spend time searching through hundreds of files, emails, and notes just to find the right procedure or piece of information. Knowledge is often scattered across different systems, which means even simple tasks can take hours and involve multiple people. A custom AI system based on your internal knowledge base can search documents in real time and respond in seconds as if it knew everything that matters. This not only saves time but also ensures consistent and up-to-date information is always available when you need it.
Customer service and HR departments answer the same questions every day, reply to emails, and explain procedures. These are time-consuming tasks that take attention away from more demanding responsibilities. At the same time, both clients and employees expect clear and consistent answers, even outside working hours. An intelligent chatbot powered by your company’s data can automatically deliver accurate responses 24/7, reducing the team’s workload and improving access to information.
The process of creating offers often involves searching through contracts, price lists, and notes stored in different places. This slows down the sales department and increases the risk of mistakes. An AI system automatically searches company documents and generates personalized offers based on their content, speeding up the process and improving its quality. As a result, the sales team can focus on building client relationships and closing deals more effectively.
Lack of quick access to analyses, reports, and summaries delays decision-making. An AI system automates the preparation of overviews, compares data, and highlights possible scenarios. It becomes a real support tool for managers who can respond faster and make better-informed decisions.
Many companies store important information in various locations on drives, in emails, or in private employee folders. It is difficult to find the most recent version of a document, which leads to errors and delays. An AI solution creates an organized, searchable, and always up-to-date central knowledge base. Employees gain a single source of truth available at any moment.
New hires often need time to navigate company structures, understand procedures, and get familiar with essential materials. They are usually supported by experienced employees, who have limited time resources.
By implementing an AI solution, you bring in a virtual assistant that answers questions, points to the right documents, and guides new employees through the onboarding process step by step. The result is faster and less burdensome onboarding for everyone involved.
Fill in the necessary data and send your application, and we will contact you immediately to learn the details and, based on them, prepare a quote for you.
Below you will find case studies of our clients who decided to entrust us with the implementation of their projects. Check what challenges we faced and what we achieved using RAG solutions.
Efektywny onboarding dzięki GenAI | Czat Bot
Automating Quality Control with GenAI in the FMCG Industry.
A GenAI and AWS-powered virtual dietitian.
Imagine that instead of asking AI anything and receiving a made-up response, you have a system that first looks into your documents, procedures, and knowledge base.
That is exactly what Retrieval-Augmented Generation (RAG) does—before answering, the system looks for information in the same places you would, inside your company materials.
There is no guessing involved, only accurate answers based on what you actually have.
In practice, it is like talking to a colleague who knows every company file and always replies according to the rules.
Simply put, it works like a search engine for your company’s drive—the more organized and up-to-date your documents are, the easier they are to find and use effectively.
The key is to have a clean and current knowledge base with no ten versions of the same file.
RAG systems perform best when they have access to fresh, well-structured data.
Then instead of chaotic searching, you get precise answers like “this is the correct entry, here is an excerpt from the procedure”.
There is no one-size-fits-all price for such projects. The cost of implementation depends on several factors, such as the scope of the solution, the level of integration with your systems, and the number of knowledge sources to be included. Every company has different needs and data structure, so our approach is always customized.
That is why most of our clients choose to start with a Proof of Concept (PoC). It is a well-thought-out, limited-scope pilot project that lets you evaluate the solution in practice before a full rollout.
This way, you can safely test integration with your data and processes, assess the quality of generated responses, and identify the areas with the most potential for improvement.
It makes the investment more informed and grounded in real results, and many companies begin seeing value within just a few months.
Data security is one of the most important aspects of any AI project. RAG systems can be designed to run locally within your infrastructure or in a trusted cloud environment, with full access control and in accordance with your internal security policies.
You decide which documents and knowledge bases the system can access—you do not have to share everything. All data is encrypted and processed only by authorized users.
This guarantees GDPR compliance and safeguards the confidentiality of your information at every step.
Integration does not mean turning your systems upside down. RAG systems are designed to work with your existing databases and document repositories, whether you operate in the cloud, on local servers, or in a hybrid model.
First, we determine where the system should source information, how to synchronize it, and how to make the results accessible to users.
This makes the new tool a natural extension of your infrastructure—it improves daily work rather than replacing your existing setup, and it helps you make better use of the resources you already have.
The most important thing is to approach it openly and transparently. People often fear new technologies because they do not know how to use them or worry they might be replaced.
That is why we focus heavily on education during RAG implementation. We run workshops, show live demos of how the internal knowledge system works, and answer questions.
We want everyone on your team to understand that this tool is here to make their job easier—to find information faster, prepare answers, or generate summaries.
This solution does not replace people or their roles. It supports them by handling the most repetitive and time-consuming tasks so they can focus on what truly matters.
Imagine that instead of digging through reports and spreadsheets yourself, you ask a question and get a clear response based on your own data.
RAG systems search your knowledge bases, combine insights from different departments, and generate summaries or recommendations in a readable format.
This provides valuable support for managers—it helps them understand key figures, trends, and relationships faster, enabling more timely and informed decisions.
AI does not make decisions for you, but it gives you the tools to make better ones.
