The power of cross-data analysis!
With eXenGine, you bring the unique power of a document analysis solution that unifies text (semantic) processing, graph analysis (inter document links, social connections), and behavior analysis.
eXenGine uses a unique machine learning algorithm with extreme robustness, high relevance and the unique ability to cross-feed data from different sources. eXenGine can be used as a foundation for recommender systems (behavior or content-based), document exploration, classification, customer segmentation,...
Based on Apache Spark, and more importantly, using a super-fast, super-scalable analysis method, eXenGine can deal with several hundreds of millions of documents, and provides a real-time query engine for the exploitation of the analysis. There is simply no limit to the amount of data you can process
News at eXenSa
- eXenGine , eXenSa , WikipediaUniversity Paris-Saclay organizes the first Business / Academics event in France with the Big Data Business Convention 24-25 October at the HEC campus. Meet you at the eXenSa's booth to talk about our latest works and our future : we're working on a online version of our multi data anaylsis solution. Currently we process the whole English Wikipedia from scratch Continue Reading...
- eXenSaAs a Tech'Mentor of the Epitech Innovation Hub, Guillaume Pitel co-organizes DataTechs with Nicolas Kamennoff, a new meetup about Data, Big Data, Data Science and Machine Learning. The goal of the meetup is to bring students and beginners together to help them discover Data with 4 differents points of view : Certes, vous pouvez pas lire les versions de jeux de Continue Reading...
Customizing and Licensing our technology is our primary activity.
We provide advices, development and report to help you build your own data analysis solutions : Big Data algorithms, Data Science, Machine Learning and Big Data systems (Apache Spark) are our domain of expertise.
We help companies doing research to write their annual activity reports for tax credits
We give training session on GPGPU, Spark, High performance computing.