Eugeniuses

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Artificial intelligence

AI is an emerging technology that has only recently been widely disseminated, and yet, it still remains a complete mystery in terms of its full potential. With an overwhelming array of possibilities, it becomes challenging to stay abreast of all the innovations and discern which ones can truly propel your business forward. Here at Eugeniuses, we have in our hands the most advanced technology available in the industry, and we are more than eager to continue utilizing our expertise in Artificial Intelligence for future projects.

These are our specialities:

Over the past four years, our team has been heavily involved in several development initiatives of the Pinecone Vector Database. As a collection of multiple layers or feature classes, this vector database is capable of efficiently storing, searching, and manipulating high-dimensional data.

Natural Language Processing is dedicated to the interaction between computers and human language. As part of this, we have developed a sophisticated Message Sorting System, our newest product. This sentiment analysis involves the use of statistics, Machine Learning, and NLP to extract and identify subjective information from the source material and interpret the sentiment of the information as either positive, negative, or neutral.

As the demand for qualified engineers and developers to optimize business operations increased, a gap emerged, prompting our agency to start working on an in-house Machine Learning system. Machine Learning can handle large amounts of data and enables computers to train and learn, allowing for autonomous predictions and decisions based on pre-established models defined by our indicators.

We develop and incorporate automated testing based on Computer Vision solutions that makes it possible to scan and analyze all interface applications visually. It recognizes and understands images, symbols, buttons, layouts, colors, and more, anticipating user experience needs. Any discrepancies are flagged, allowing developers to identify potential UX issues or visual regressions early in the testing process.