Devices, sensors, IT assets, machines, vehicles and buildings generate large volumes of streaming data. SWIM software finds critical events, discovers hidden patterns and predicts future behavior using self-training machine learning to enable decisions in real-time. SWIM delivers views of the analyzed data, insights and predictions to any device in real-time, or to streaming APIs for enterprise applications.
Here's how SWIM software turns edge data into real-time insights and predictions, entirely at the edge.
SWIM EDX processes edge streaming data as it’s generated in real-time at the edge, without big-data. SWIM finds critical events, anomalies and identifies correlations - reducing data volumes.
SWIM EDX creates a resilient "edge computing" and data processing fabric, spanning any combination of existing edge devices. SWIM’s stateful edge architecture efficiently reduces, analyzes and learns large volumes of real-time data across multiple types of hardware – all at the edge.
SWIM builds and self-trains active “digital twin” models of real-world systems from “gray” edge data. Each digital twin analyzes real-time data from its sibling, trains a deep-learning model to and predicts future behavior.
SWIM EDX self-trains from real-world data – Each "digital twin" precisely matches the actual system streaming data and captures unique local behaviors. SWIM EDX software is also self-managing and requires minimal set-up or configuration.
SWIM delivers real-time insights and predictions from digital twins to both APIs and GUIs. SWIM serves business, OT and IT stakeholders with simple and intuitive visualizations, detailed insights and predictions. SWIM APIs enable simple integration with ERP, CRM, MES or other enterprise applications.
See how SWIM works for different industries