Implementing machine learning into corporate processes requires a comprehensive approach and skills. This is a field that is constantly evolving, and it is virtually impossible for those who don't work with it on a daily basis to have the latest information and know the latest methods.
We help companies implement machine learning within a broader context that has a direct impact on the quality of results. We serve as a professional partner to company management, providing insight into current development, trends, and capabilities of the entire field. We go over selection of the ideal solution for the given company, ideal staff capacities, and configuration of individual process components within the context of corporate strategy. Working with data definitely brings better performance and profitability, but this result is contingent on its functional configuration.
As a team of mathematicians and engineers, we closely supervise every detail of our highly efficient and functional solutions, and can thus provide companies support for highly advanced and demanding issues and problems in the area of data science.
When working on a data strategy, we base ourselves on our technical excellence and understanding of the business aspects of the problem. Though working with Big Data is something companies cannot avoid today, at the same time for many of them it is an excessively complicated challenge, which has an effect on the quality of results. For us, the main criterion when defining a data strategy is a quick and quantifiable impact on the company's business and the use of modern and effective technologies. Thanks to our expertise, we deliver solutions that give companies an edge.
Our advantage is that when building architecture, we supervise the entire process within which it is embedded. In contrast to an in-house solution, we can offer knowledge and understanding of the entire field and the latest application practices and trends. Our solutions deliver benefits whenever a company needs to process big amount of data, when big data processing is part of some relatively complex process that must be automated, and when new processes need to be linked to existing ones. When designing data architecture, we concentrate on several factors: the elimination of human error, speed, cost, flexibility, use of open source technology, and ease of use for the client. As a result, we design solutions that process terabytes of data on a daily basis and work with information that has a direct impact on companies' performance.
Reap the benefits of statistics when working with your customer database. Companies use our solution when they don't know their customers' needs or their motivation to buy, when they don't base their product on data, and other situations where proper use of user data is absent. We built the TaranDM product to be able to perform client segmentation, propensity modelling, market basket analysis, survival analysis, and other analytics in real time. You can thus quickly react to situational changes and successfully manage not only your basic business strategy, but also cross-selling and up-selling activities.
We're capable of developing a unique solution thanks to our formal education and jobs and projects we've implemented in various fields across various continents.
Absence of reporting is akin to blindness - you don't know what's truly going on in your portfolio and you don't have the ability to make decisions based on data. Our tools are designed for situations when you need to have a regular and easily accessible overview of pre-defined metrics for a portfolio, or to depict predictions or the process itself. For cases where you need to go into detail, we create interactive reports that provide you an overview on various levels, from the highest (summaries) to the most detailed (individual cases). We help define what needs to be seen and then assemble an informational hierarchy. Our work isn't over when the model is done - following implementation, we continue to monitor and evaluate its business value.
We provide corporate education and training. Effective use of data requires constant education and continuous monitoring of new developments in various fields, which in turn places demands on time and resources that companies often cannot meet. We build on the fact that several our experts have been working as university lecturers. Training is always customized to the client's requirements. Most frequently, we help the team that will be using our solution get the most from it. Another situation is managing a team of analysts or developers when developing a new model or solution. In these cases, we serve as professional sponsors to guide the team as it works, allowing know-how to remain in-house. A third case is training in a specific subject in the area of data science requested by a company.
This is a tool whose quality has a direct impact on the profitability of financial services. When banks or other institutions whose business is based on financial services use obsolete methods, their costs increase and their portfolios will include loans that will never be paid off.
Modern methods, on the other hand, result in statistically better performance, are more flexible, and are capable of detecting characteristics such as data non-linearity. Nevertheless, switching to this method is a complicated process for companies, and one that is accompanied by a number of risks when developing a solution in-house. This is why we developed TaranDM, which is based on our extensive experience with credit risk management and leads to simplification of the entire process, faster and more flexible development of models, lower costs, and improvement thanks to error-proofing.
A scalable and modular solution with end-to-end reliability for enterprise decision management built with high-performance big data technologies and no vendor lock-in. It is fully configurable and easy to deploy in any enterprise architecture.Learn more
The TaRank algorithm is fully customizable, state-of-the-art recommendation engine, developed in Python and PySpark technology in order to be able to regularly process and rank up to hundreds of millions of items daily.Learn more
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