WHAT WE DO
QBT develops algorithms and software in the fintech area, backed by a proof experience in the direct provision of services.
The mentioned experience has increased our know-how, consolidating our ability to develop tailored and customized management softwares in order to provide the best technological solution according to the specific customers’ needs.
Moreover, through a deep research and development activity we explored new areas of business turning into a point of reference in the field of Artificial Intelligence, and, in particular, in the Natural Language Processing and machine learning.
We develop all of our research activities thanks to a close cooperation with universities and research institutes, which represent the natural competence network at QBT.
Natural Language Processing e Machine Learning
«The Artificial Intelligence (known with the English acronym AI) is a discipline belonging to the computer science which studies theories, methodologies and techniques allowing the design of hardware systems and software programs to supply the electronic computer with services that, to an ordinary observer, would seem to be exclusively human».
Specific definitions can be given by focusing on internal reasoning processes or on the external behavior of the intelligent system and using the similarity with human behavior or with an ideal behavior, called rational, as a measure of effectiveness.
The AI can simulate human intelligence in different ways, according to the processes activated:
- acting humanly – the result of the operation performed by the intelligent system cannot be distinguished by the one belonging to the humankind;
- thinking humanly – the process leading the intelligent system to solve a problem is very similar to the human one. This approach is linked to cognitive science;
- thinking rationally – the process leading the intelligent system to solve a problem is a formal procedure referring to logic;
- acting rationally - the process leading the intelligent system to solve a problem is the one that allows getting the best-expected result given the information available.
Semantic research or Natural Language Processing:
- does not rely on the simple search of Keywords;
- solves the problems linked to morphology (singular/plural, infinitive verbs, etc.);
- understands the meaning of the “context” thought, through the disambiguation of texts;
- provides results that refer to the context even if the Keywords are not specifically present in the content.
An example is shown below:
The search “Penguins” will provide all the information sources reporting contents related to the funny “grey animals of the Southern Pole”, without such contents having to include strictly the word “Penguins”.
MOrSe (Semantic Search Engine) is a QBT platform, based on proprietary semantic technology, which supports customers in managing the information available in the most effective way, to get fundamental and strategic notions.
The platform can manage large data flows (text documents, multimedia, audio streams, web pages and social network) in 27 different languages.
The strength of the MOrSe platform are the specific thesauri defined for each specific application sector to conceptualize, classify and search for topics in the best way. This allows going beyond the limits of traditional technologies, which are based on keyword.
Designed and realized by experts of the sector together with CNR in Rome, MOrSe is adopted by qualified customers in Italy and Switzerland and it is used in different and multiple environments.
Personnel Selection through semantic research
Companies receive curricula from different sources, from personal applications to specialized websites, on a daily basis. But interesting professional profiles are not always relevant with the ongoing selection.
How to select the best candidates?
It is not necessary that the file is in a predefined format or in Italian; the software analyzes the semantic of the content to understand the most important information so as not to miss an interesting candidate and it is able to do it in 27 different languages.
The research is expanded by semantic areas related to the work environment, to identify similar profiles even if the searched words are not directly included in the content of the file.
The software does not replace the job of the selector but it helps to identify more quickly the required profiles, saving up to 90% of time in archiving files.
The software is available for both company network and Software as a Service.