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
Your use case
We provide extreme customization services to adapt eXenGine to your problem and help you beat the competition in your area : search, e-marketing, law, patents, scientific edition, e-commerce.
News at eXenSa
- On June, 6th and 7th, we'll be at AI Paris to showcase our next version of eXenGine, combining exclusive technologies to push data mining to the next level : Super efficient counting (10 times less memory than current state-of-the art competition) Ultra-fast Machine Learning (we can create a model synthetizing half a billion documents in 30 minutes on a cluster of Continue Reading
- Guillaume Pitel gave a talk at iSwag 2016 about our newest breakthrough algorithm for fast approximate counting. Our Count-Min-Tree sketch structure outperforms the state of the art methods by serveral orders of magnitude. See the presentation and the paper.
- University 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