GROVF
Big Data Computing - Accelerated

Industries

Cyber Security

Machine generated log data is growing exponentially making it extremely hard for cybersecurity software to provide real-time defense for the infrastructure. The software needs to analyze Terabytes of data every day generated by servers, applications, and firewalls. With Grovf massively parallel FPGA cores companies analyze and prevent the cyber threat 15x faster, saving critical time for organizations to detect a penetration and counteract almost in real-time. 

 

Financials

In an industry where milliseconds matter and where insight directly equates to money, faster analytics offer a distinct competitive advantage. Grovf makes it possible for financial organizations to derive insights and make predictions from vast volumes of complex and streaming data in milliseconds. Use Grovf truly real-time analytics demands including fraud analysis, risk management, algorithmic trading, and high-frequency trading.

Retail

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 retail industry. The main tasks in 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 task having supper efficient text processing units are necessary because the main data type that is being generated in retail industry are 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 analizing unstructured text data while saving the TCO of datacenter 3 times less.

10x

Faster Insights

Real Time Infrastructure

3x

Save on TCO

2x-4x

Power Saving

In 2018, there was generated more data than in the entire previous human history. Current processors have reached their physical limit and are not capable to provide the desired business value.

Partners