Retail
Data analysis

Nowadays data proves to be a powerful pushing force of the industry. Big companies representing diverse trade spheres seek to make use of the beneficial value of the data. Nowadays data science is a key point for the retail industry. The main tasks in the retail industry that require high-performance data analytics are Recommendation engines, Market basket analysis, Warranty Analytics, Price optimization, Inventory management, Location of new stores, Customer sentiment analysis, Merchandising, Lifetime value prediction and Fraud detection. In all these tasks having supper efficient text processing units are necessary because the main data type that is being generated in the retail industry is unstructured text data. Grovf offers it's text processor engines which is the fastest in the market allowing retail companies to achieve dozens of times faster speed when analyzing unstructured text data while saving the TCO of datacenter 3 times less.

 


User Sentiment Analytics

User Sentiment Analytics

The promise of artificial intelligence (AI) is to make work and life more productive. But to do so, AI needs to better understand humans, which are the most complex organisms on Earth. A significant element of AI’s limitations, to date, is understanding humans and, more specifically, human emotion. In the past few years, however, accelerated access to data (primarily social media feeds and digital video), cheaper compute power, and evolving deep learning combined with natural language processing (NLP) and computer vision are enabling technologists to watch and listen to humans with the intention of analyzing their sentiments and emotions. A better understanding of emotion will help AI technology create more empathetic customer and healthcare experiences, drive our cars, enhance teaching methods, and figure out ways to build better products that meet our need


MongoDB Acceleration using Grovf's MonetX Platform

Databases provide a wealth of functionality to a wide range of applications. Yet, there are tasks for which they are less than optimal, for instance when processing becomes more complex or the data is less structured. As data is exploding exponentially only CPU based systems no longer provide real-time insights to businesses in a cost-effective way. At Grovf we designed a Monet – A FPGA based smart memory extension for near memory data processing. Monet implemented on top of Xilinx’s Alveo U50 acceleration card and once plugged into server’s PCIe bus acts as a standard RAM memory for the Linux operating system with in-memory compute API capability.

MongoDB Acceleration using Grovf's MonetX Platform