Data Science Services

Software Mind's experience with Data Science dates back to 2005 when our experts developed technology for the first web-scale Semantic Web startup. This innovative project consisted of building a clustered solution for scalable Natural Language Processing (NLP), the first commercial use of Hadoop (early 2006) in Europe, and one of the first such implementations worldwide. In several R&D projects throughout the years, we provided Big Data architecture and Big Data Analytics, successfully delivering Data Science and AI solutions to scale-ups, financial institutions and high-tech companies. Our specialty is working with structured, semi-structured, and unstructured data at any scale.

Maintaining a competitive edge requires being up-to-date with the latest R&D. That's why Software Mind established strong partnerships with leading universities and research institutions to collaborate on the latest breakthroughs and integrate them into data solutions. We carried out R&D projects in various disciplines and areas like Data Science, web science, semantic technologies, Big Data, collective intelligence, water management, citizen science, smart cities, transportation, mobility and hybrid cloud by closely cooperating with leading academic institutions (Southampton University, Sheffield University, TU Berlin, EPFL, TU Delft, University of Koblenz and Landau, Open University, University of Cantabria) as well as research and innovation centers (CERN, Cyfronet, JRC, PSNC, Esade, AARNet, CESNET, SURF, SWITCH, European Open Science Cloud, European Grid Infrastructure). Keep pace with of ever-changing business landscapes by leveraging research results delivered by our solutions.

Bespoke Data Science solutions developed by our specialists create and integrate various models (predictive, social, physical, water, and air quality) and AI components. Top-tier Data Science experts deliver ML and AI solutions for chatbots, smart cities, transportation, document management, optical character recognition (OCR), collective intelligence, crowdsourcing, social media data mining, sentiment analysis, investment, well-being, water management, search, and privacy protection. With Data Science environments and MLOps organizations are empowered with the tools they need to speed up and streamline their machine learning workflows. Thanks to data collection, model training and deployment, integrated platforms for data scientists enable the development and deployment of machine learning models quickly and efficiently.

Since using a bare-metal cluster for scalable data analytics in 2005, we have continually elevated skills and experience in data management and Data Science in private and public clouds, gradually moving to cloud-native technologies. Benefit from Data Science solutions delivered in the cloud, on-premise and in the hybrid cloud, which provide distributed Data Science environments and technolgies in federated clouds.

An experienced team understands how to leverage the local processing capabilities of mobile devices to reduce the reliance on cloud services, thus lowering overall cloud costs. A proven track record of overcoming challenges connected to supporting data-driven decisions in a distributed cloud of resource-limited mobile devices and developing products processing sensitive data on them can be used to accelerate your growth. Software Mind creates efficient algorithms to optimize data processing, communication and storage in a resource-limited environment. Our teams address privacy concerns connected to designing and implementing secure systems for processing sensitive data by developing techniques for methods for anonymizing data, encrypting data, secure data transmission and storage. Taking advantage of federated learning allows us to craft distributed and secure analytics methods for mobile devices to generate insights and drive decisions, without compromising user privacy.

Data Science solutions, combined with highly skilled, autonomous, and agile development teams, enables Software Mind to create revolutionary products, allowing companies to innovate faster and speed-up time to market. Our Data Science services are often part of digital transformation projects that introduce new architectures, break monolith products into microservices, migrate systems to the cloud and build real-time data pipelines. Conducting data discoveryusiness Intelligence, means our teams can help you become a more data-driven enterprise.

By selecting the right tools, proven solutions and latest technologies where appropriate, your company will gain business value. Whether it's the ability to find, evaluate, and use cutting-edge technologies (NLP, NoSQL, ML/AI, hybrid cloud, cloud-native), design new approaches, use data to understand customer needs or develop innovative solutions that address them when designing distributed and decentralized systems, we leverage real-world experience in a variety of Big Data and Data Science projects, identifying the resources available for data processing, as well as the best way to utilize them. Understanding the different types of data that can be used for decision-making and how to access and process it effectively remains crucial, as our expertise with integration tools helps to properly configure and utilize the different components of the distributed cloud, such as storage, databases, and service layers.

Improve efficiency and productivity by receiving a comprehensive suite of data-driven solutions. Implement data architectures (Data Lake, Data Lakehouse) by building a secure and compliant environment, establishing a data catalog to enable data governance and lineage, creating automated data pipelines to streamline ETL/ELT processes and integrating it with existing data sources. We help introduce self-service Business Intelligence by presenting a comprehensive suite of tools, strategies and insights to enable users to access and analyze data easily, allowing businesses to make more informed and accurate decisions, improve operational intelligence and maximize data value. Our solutions allow companies to save time, money and resources by automating data processing, making it easier to access and analyze data and quickly deploying machine learning models.