Portfolio
Since being founded in 2004, we have developed various complex products,
mainly in the fields of Semantic Web (Linked Data) and text analytics.
Many of our customers are high-tech companies from Europe and North America .
We have been working with some of them for many years now.
The products we have been developing are used by organisations such as HP and NASA,
as well as by pharmaceutical companies currently active in COVID-19 vaccine research.
Please find below descriptions of selected projects.
Domain management system
Client:
Operator of a managed registry model for national TLD of one of the EU countries
Project description:
We are developing a set of applications that support the daily operations of one of the European TLD registrars.
It supports outside processes (such as individual and wholesale domain selling, and integration with external registrars) as well as internal processes (such as domain purchase approval). There are multiple web UIs and two separate web service interfaces to the system. Automated tests were created to test both logic and user interfaces.
Technologies:
Java, Spring, Python, PHP, yii, Perl, Selenium
Project size:
This is an on-going work that started a few years ago and involves several software developers and testers.
Automated Brand Monitoring on the Web
Client:
Heureka: A Polish digital agency serving many large companies
Project description:
We implemented a system that monitors what people are saying about brands in web pages, forums and social media.
The system includes a crawler and an analytical module.
Technologies:
C++, wxWidgets
Project size:
This was several weeks worth of work for two developers.
Procurement Optimisation System
Client:
McKinsey: A leading international consultancy that asked us for help in their cost-cutting project conducted for a big airline company
Project description:
A big airline company needs to purchase many different services in many cities around the globe. For each city and service, a supplier has to be chosen in a way that all the necessary services are ensured and the global cost is as low as possible. The suppliers offer global volume discounts and this makes the problem computationally difficult ("NP-hard") because a solution locally optimal is not necessarily part of the global solution.
We have created a genetic optimiser that finds the best (or near-best) solutions for various scenarios and constraints selected by the user.
Technologies:
C++, Python
Project size:
This was several weeks worth of work for two developers.