SWIM provides powerful edge-based software solutions that applies powerful machine learning analytics to high volume edge data feeds from data-rich environments to automatically discover powerful insights that can transform efficiency and performance, and maximize user benefits. SWIM software is designed to “plug and play” in brown-field manufacturing, enterprise, government, city and utility environments to deliver revolutionary insights from the data sources you've got already.
SWIM consumes floods of data from devices at the network edge , intelligently transforming low-level events into a manageable stream of automatically learned, real-time, context-relevant insights. SWIM software edge nodes automatically learn their deployment context with minimal configuration, learn powerful insights and deliver predictions that help operators and applications quickly respond.
Real-time, distributed and fault tolerant edge processing on commodity hardware
Automatically learns powerful domain specific insights
Plug & Play in brown- and green-field deployments
Secure by design
SWIM uses powerful, distributed edge-based machine learning algorithms to automatically figure out the deployment context and configuration of each node, and to dynamically identify and track insights. SWIM transforms masses of low-value data into high-value insights. This enables operators to achieve previously impossible performance by responding quickly to observed or predicted resource availability at a fraction of the cost – filtering out noise, saving bandwidth, and avoiding unnecessary storage and cloud processing.
Edge learning delivers new insights fast, in context, enabling the infrastructure to dynamically adapt to changing conditions. SWIM embeds intelligence into the edge fabric, replacing centralized data storage and delayed cloud processing with embedded, local learning and real-time contextually relevant responses.
Create intelligent, responsive, business-optimized resources. Transform data-rich enterprise environments with SWIM software at the edge.