Accelerate your Business Intelligence
applications up to

30x faster

Bigstream NS/XS





User Behavior Analysis Business Intelligence Risk Management

Healthcare Analytics

Security Analytics HFT RTB Genome Sequencing Fraud Detection Messaging
logos In-house high-performance / low latency stacks
Hyper-acceleration Layer
  • fpgaFPGA
  • gpuGPU
  • cpuCPU


  • User Behavior Analytics
  • Fraud Detection
  • Risk Management
  • Business Intelligence
  • Healthcare Analytics
  • Security Analytics

User Behavior Analytics

Bigstream's Big Data/Machine Learning platform translates user analysis into actionable insights in real-time, allowing enterprises to accurately predict future usage trends and user preferences.

Analyzing the behavior of online users can help predict the success and future trends of a product/service. As data is collected from both past and current users' activity, the volume of data associated with the analysis in e-commerce, AdTech, and FinTech industries can be extremely high. Enterprises need high computing power to build accurate machine learning models in order to analyze purchasing decisions, marketing responsiveness, and overall improve customer conversion rates.

Fraud Detection

Using Bigstream's Big Data/Machine Learning algorithms can accelerate the detection of fraud transactions to identify unusual financial activity, improve security, and protect customers/partners.

Fraudulent transactions pose a serious threat to all enterprises. As fraudsters have come up with more complex attack scenarios, fraud detection systems need to become more agile, adaptive, and sophisticated. To detect and prevent fraud transactions with precision, and in real-time, enterprises need more advanced algorithms, which in turn requires higher computing power and deeper analysis of data.

Risk Management

Bigstream's Big Data/Machine Learning solution can not only help enterprises forecast, evaluate, and eliminate potential threats, but also offer proactive risk intelligence to minimize financial risks.

Estimating Value at Risk (VAR) and other financial statistics is an important component for financial services as well as insurance companies. Credit and debit valuation adjustments also require a large amount of computing power to sweep the space of different scenarios, instruments, and time intervals. Risk managers need to be provided with the right resources to identify, assess, and monitor known, unknown, and hidden risks of the organization.


Business Intelligence

Bigstream's Big Data/Machine Learning's technology delivers higher performance and empowers enterprises to make faster, more informed business decisions.

From operational workers, business managers, to corporate decision makers, business intelligence allows organizations to visualize and query various business metrics under different scenarios. Crunching numbers for various scenarios, creating dashboards, and generating data visualizations in real-time all require a high amount of computing resources.

Healthcare Analytics

Bigstream's Big Data/Machine Learning platform enables healthcare providers to transform unprecedented amounts of patient data to create true individualized care and advance personalized medicine.

Due to the advancements in genome sequencing, the healthcare industry is undergoing a major breakthrough to proliferate precision medicine. Customizing patient treatments and integrating precision medicine into mainstream delivery requires high performance computing to recognize the conditions and complex patterns of each, individual patient.

Security Analytics

Organizations can utilize Bigstream's Big Data/Machine Learning solutions to detect anomalies in the data collected from endpoint network equipment and servers, enabling them to defend against cyber security threats, and significantly mitigate damage from security breaches.

Through real-time collection and historical analysis of security events, SIEMs provide instantaneous threat detection and security alerts generated by network hardware and applications. The effectiveness of SIEMs depend on the volume of data the systems can process, as well as the response latency when attacks occur.