Preparing for the exponential growth of multi-sourced data through GPU acceleration.
Faster than ever
Crunch at magnitudes faster through GPU accelerated processes for massive scale tabular and spatial analytics, model training and visualization workloads.
Multi-dimensional and massive data
Unifying various data objects and layers streamed through multiple data sources via data fusion enables the GPU accelerated infrastructure to compute massive data analysis through-out multi-dimensions.
Deep learning enabler
Deep learning enables multi-sourced computations such as uncovering patterns of life, behavioral correlations and spatial clusters through deep learning and AI algorithms seamlessly interacting with BI dashboards. The scalability and compute efficiency of the GPU accelerated infrastructure in combination to crystallized data attributes enables us to train and monetize attributes and algorithms.
Data attributes fused from various sources provide cross-sectoral synergies.
Characteristics of consumers and audiences are mapped like genetic sequences.
Present day customer requirements for data analytics, require multiple data source fusing systems and high speed and performance data crunching, while seamlessly providing training models, visualizing through BI interfaces and data management endpoints. Therefore, conventional data analytics is soon coming to an end.
85% cost reduction
State-of-the-art processor architectures allow for time and computational efficieny
Batch and real time data streams efficiently parallelized and computed at 100x faster.
See how we differentiate our products and services through the infrastructure.