FPGA Acceleration for Big Data & AI Computing
Turning challenges into opportunities

Grovf offers products that accelerate big-data analytics and enable organizations to search and analyze the most critical data at speeds approaching real-time.

The Grovf platform takes advantage of the versatile FPGA HWs to accelerate the processing of computationally intensive big-data. We do this by accelerating middleware, so applications running on top of middleware get an automatic boost without any changes on the user application.

Read more about the product on Xilinx® Alveo™ Data Center accelerator cards.

10x

Faster Insights

10x

Real-Time Infrastructure

2x

Save on TCO

Grovf's Platform for Big Data Computing

Grovf's Platform for  Big Data Computing
Security Log Analytics

Security Log Analytics

Implementing effective cybersecurity measures is particularly challenging today because there are more devices than people, and attackers are becoming more innovative. Cybersecurity brings dual challenge of low-latency detection and remediation of advanced threats, and batch analysis of log data from servers, firewalls, applications and security systems. Considering how fast new threats and attacks emerge, Big Data performance and the use of new types of software and hardware accelerators is becoming more critical.


Financial Fraud/Risk Analytics

A fraud risk assessment is an essential element of an organization’s fight against fraud. The topic of risk continues to be of critical importance across financial services segments. While there are many forms of risk, the most common form of risk across all financial segments is surrounding cybercrime and fraud. There is also a post-financial crisis regulatory aspect of risk management that forces lenders to know precisely how much capital they need in reserve. Keep too much and you tie up capital unnecessarily, lowering profit. Keep too little and you run afoul of Basel III regulations.

Financial Fraud/Risk Analytics

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