At Dataslab, we deliberately combine both approaches. 
We develop our own range of AI products, including the AI Platform, Knowledge Base, Meeting Notes, CallClarity AI and Gathering System. At the same time, we provide data engineering and AI adoption services for enterprise clients. 
These two areas reinforce each other: our products are rooted in the practical experience we have gained from working on projects, and our service team implements the products they have developed and have in-depth knowledge of.

We treat team development as an operational necessity, allocating approximately 20% of employees' time to learning and certification. 
This includes systematic study of new technologies and frameworks, continuous professional training, internal knowledge-sharing sessions and maintenance of up-to-date certifications in key technology areas. 
Tools and approaches in enterprise data, generative AI, and the LLM ecosystem evolve very quickly. Therefore, continuous learning is essential for the high-quality development, implementation and support of modern solutions. 

The technical direction is developed by a team with experience in data engineering, AI/ML, enterprise data platform architecture and the implementation of solutions in production environments. 
The AI/ML direction is led by a technical lead who has a PhD in Mathematical Analysis. This gives Dataslab expertise in not only developing AI solutions, but also selecting the right models, assessing the quality of results, working with algorithmic limitations and preparing solutions for practical business use. 
The team works on complex data engineering projects, covering the entire enterprise data lifecycle: from data collection and processing to integration, scaling, security and ongoing support.

Dataslab is primarily a vendor and developer of its own software products for working with enterprise data, analytics and AI/ML scenarios. 
Unlike traditional system integrators, Dataslab does more than implement off-the-shelf solutions. The company creates and develops its own products, gaining a deep understanding of their business purpose, architecture, and further development logic in the process. 
Thanks to its expertise in infrastructure, IT security, and integration processes, Dataslab can also effectively implement its own products, as well as solutions from other vendors, into customers' business processes. 
This enables the development of solutions adapted to the company’s IT landscape that can grow alongside the business. 

Dataslab follows a structured, five-step approach. 
1. Understanding business processes — the business analysis phase, which focuses on understanding the client’s business context and identifying their needs. 
2. Architecture design — developing a technical solution that takes into account the existing infrastructure, scalability, IT security and other project requirements. 
3. Development and implementation — building and deploying the solution with regular results demonstrations. 
4. Technical monitoring after launch — monitoring the system after implementation, identifying any issues and providing training to the customer’s team. 
5. Support: ongoing maintenance, support and further development of the solution. 
The key principle is that Dataslab continues to support the solution after implementation, helping to ensure stability and develop its functionality in line with new business needs.

Dataslab primarily works with medium-sized and enterprise businesses that accumulate significant volumes of data and require effective technological solutions. 
These are often companies with multiple data sources and analytical needs that require data or AI solutions to be implemented. 
However, some of Dataslab's products, such as Meeting Notes, the Gathering System and the Knowledge Base, are available as Software as a Service (SaaS) or Data as a Service (DaaS) solutions and may also be useful for smaller teams.

The company has practical experience in sectors where businesses rely on stable IT infrastructure and need to implement analytics and AI/ML solutions. These sectors also have significant cybersecurity and digital service continuity requirements. 
The team has expertise in finance and banking, telecommunications, media and OTT platforms, insurance, retail and e-commerce, manufacturing, energy, education, the public sector and customer-oriented companies with intensive communication processes. 
Across these areas, we work on building enterprise data platforms, including DataHubs, Data Lakes and Data Warehouses, to process large volumes of data and perform real-time and batch analytics, event streaming and BI analytics. 
The team also has expertise in event forecasting, operational process optimisation, call recording analysis, natural language processing, computer vision and the development of AI assistants. 

To put it briefly, yes. Dataslab can work as a technology partner with an internal data team, providing additional expertise and practical support when implementing data or AI projects. 
This cooperation can take various forms, including consulting support, architecture review, implementation assistance, and support at specific stages of the project. 
This approach helps companies to move faster from concept to working solution, reduce technical risks and develop implemented data or AI systems more effectively.

For Dataslab, it means building projects around the client’s specific business challenges. 
First, we consider the business context, data, processes and expected outcomes, and then we define the technological approach. At the start of the project, we agree on the criteria for determining the project's success, and we evaluate the result based on the practical value that the solution creates for the business. 
This approach also involves transparent communication and knowledge transfer to the client’s team. 

It usually begins with a discussion of the business challenge, the expected outcome and the current context, identifying which processes, communications or workflows need to be improved, automated or made more accessible for analysis. This helps to shape a practical digitalisation roadmap. 
Based on this information, the Dataslab team will help you to define the most suitable format of cooperation. This may involve implementing a ready-to-use product such as Knowledge Base, Meeting Notes, Call Clarity AI or Gathering System, delivering a Data Engineering, Advanced Analytics, AI Adoption, Software Development or IT Consulting service project, or combining a product with service expertise. 

A company should consider Data Engineering if its data is stored across different systems with different structures, is processed manually and is not ready for analytics tools or AI scenarios. 
Dataslab specialises in building enterprise data platforms, including the Dataslab Enterprise DataHub, which combines Data Warehouse, Data Lake and Data Hub approaches to centralise, process and utilise corporate data. 
This foundation is required for Business Intelligence (BI), advanced analytics, real-time analytics, and the implementation of AI/Machine Learning (ML) solutions.

Advanced analytics is required when standard reporting is insufficient and a business needs to identify trends, work with operational data, create forecasts and analyse events in real time. 
Dataslab implements BI systems, analytics dashboards, real-time dashboards, event streaming and predictive analytics, supporting deeper data analysis and informed decision-making in business. 
These solutions can be used to monitor operational metrics, analyse customer activity, forecast demand and detect changes in business processes. 

Predictive analytics helps companies to forecast trends, demand, workload, business activity and other key indicators by using historical and current data. 
It is a way for businesses to analyse past results and plan future actions more effectively, including procurement, resource management, sales, operational processes and production workload. 
These models are particularly useful for companies that already have a sufficient volume of data and want to use it for planning, optimisation or decision automation.

At Dataslab, AI adoption involves implementing AI/ML solutions in business processes where AI can deliver practical value, such as automating information processing, improving analytics, supporting decision-making, and optimising operational processes. 
This may include natural language processing (NLP), text classification, conversational agents, sentiment analysis, computer vision, forecasting, optimisation, logistics scenarios and customer experience improvement. 
The main goal of AI adoption is to integrate AI solutions into real business processes to solve specific company challenges.

Yes, by analysing the company’s business processes, available data and potential AI value. 
Dataslab can help identify areas where the use of AI technologies would be practical, such as document processing, communications, forecasting, analytics, process automation or decision support.

In most cases, data preparation accounts for up to 80% of project work. 
If the data is stored in different systems with different structures and requires additional processing, such as calculations, denormalisation, encryption or other transformations, the first stage should include source analysis, architectural design, data integration and setting up data processing workflows. 

Yes. Dataslab offers DevOps competencies as part of its Software Development and IT Consulting services. 
The team can work with CI/CD, containerisation, orchestration, environment automation and Kubernetes, as well as AI/ML Ops services for managing the lifecycle of artificial intelligence models. 
This means that Dataslab can take responsibility for developing, deploying and updating the solution, as well as scaling it and providing ongoing support. 

Yes. The team can use open-source technologies for data and analytics projects that match the client’s technical requirements and business objectives. 
The team uses a variety of tools and languages, including PostgreSQL, Hadoop, Spark, Apache NiFi, Apache Airflow, Python, Java, Scala and SQL, to build data platforms, process data and develop analytics solutions. 
Whether to use open-source, commercial, or hybrid technologies depends on factors such as the architecture, support requirements, security needs, and total cost of ownership.

Yes. As part of its IT consulting services, Dataslab can assist with business analysis, solution architecture and creating a roadmap for implementing data and AI/ML solutions, from piloting a use case to deploying it in production. 
This enables projects to begin by formalising business tasks, assessing the current state of data and selecting a realistic implementation path, rather than moving directly into development. 

If the challenge relates to fragmented enterprise data, the company should consider Data Engineering or the Dataslab Enterprise DataHub. 
If the business requires reports, dashboards and predictive analytics, then Advanced Analytics could be the appropriate solution. 
If the company wants to implement AI in its business processes, it should consider the AI Adoption or AI Platform solutions. For tasks related to corporate knowledge, Knowledge Base may be suitable. 
For processing meetings and calls, Meeting Notes and CallClarity AI could be useful. The Gathering System can be used to collect data from open digital sources. 
If a company needs to centrally manage product information, catalogues, product attributes, images, SEO data and the transfer of product data to different sales channels, Dataslab PIM may be the appropriate solution. 
If the task involves automating interactions with suppliers and updating product information, prices, stock levels, requests, deliveries or orders, the Supplier Portal should be considered. 
These two products can work separately or together. PIM is responsible for the structure and quality of product data, while the Supplier Portal enables convenient interaction with partners and allows them to update information.

Business intelligence (BI) and reporting help companies analyse data and visualise key metrics. Data engineering creates the foundation for this by providing the architecture for collecting, integrating, storing, processing and preparing data. 
If data is unorganised or distributed across different systems, a company will usually need data engineering first, followed by BI, advanced analytics or AI/ML.