• Flexible and Scalable Platform

    Litmus Edge collects, normalizes, analyzes and integrates real-time data from any source to provide a complete data picture across the organization with one single solution.

    • Deploy securely and locally at the edge with no internet connection required
    • Install as an OS on any gateway, VM or server at the edge
    • Deploy quickly on a pre-installed device or via USB
    • Access an intuitive web interface via web browser or terminal user interface (TUI)
    • Securely manage any number of assets at any number of sites
    • Access Litmus Edge anywhere in the world and diagnose issues remotely
  • Device Connectivity

    Quickly connect to any industrial asset – PLC, DCS, SCADA, Historian, sensor or ERP – with pre-loaded drivers and no programming required.

    • Scan the network to seamlessly add industrial assets without coding
    • Access 250+ legacy industrial systems and protocols out-of-the-box
    • Use the DeviceHub interface to add, modify, refresh, start, stop or remove a device
    • Define how to connect and collect data from any device on the network
    • Use a drag-and-drop flows editor to test device connectivity and customize workflows
  • Data Collection

    Collect and normalize hundreds of custom data points from any number of assets into one standard format for consumption by any application.

    • Store normalized data in a scalable and secure time series database
    • Index data to be utilized for terabytes of storage
    • Use an optimized version of influxdb for all data storage
    • Publish data to a local message broker for immediate consumption
    • Access native data by SDKs and non-native REST API
    • Integrate with any enterprise grade cold storage
    • Every data point has pre-analyzed cubes for device management, alerts and analytics
  • Real-time Analytics

    Monitor real-time asset data, set up alerts and utilize ready analytics based on common KPIs such as uptime, downtime, anomaly detection and more.

    • Use ready analytics to dramatically reduce manual setup and configuration time
    • Configure KPIs including OEE, uptime, downtime and more with no coding
    • Configure time series data analytics by average, maximum and minimum
    • Perform statistical and analytical queries on the live data
    • Define workflows with a drag-and-drop editor for simple data manipulation and visualization
    • Utilize Grafana open source-based dashboards
    • Create visualizations, BI dashboards and custom SQL-scripted analytics in a few clicks
  • Application Marketplace

    Enable one-click application orchestration and deploy docker container-based applications from a public or private application marketplace.

    • Access the Litmus Edge Marketplace, a local application repository for launching applications on demand to enable edge-level analytics
    • Utilize a default set of 45+ applications in the Public Marketplace
    • Add a Private Marketplace to leverage existing custom applications
    • Add proprietary docker container-based applications to the Marketplace
    • Deploy docker applications to one or many devices with one click
    • Zero-touch provisioning, mass management and application orchestration
    • Perform application orchestration and lifecycle management (in docker) from a central location
  • Data Integration

    Immediately feed valuable and ready-to-use data to any cloud or enterprise application to achieve a complete data picture from OT to IT.

    • Easily integrate to the cloud with pre-built connectors for data visualization and device management
    • Feed collected data into Big Data implementations with native Kafka and database interface
    • MQTT for device-to-Litmus Edge data collection and pre-processing
    • REST API integration for workflows
  • Machine Learning Runtime

    Feed machine learning models with normalized data and complete the feedback loop by running the new models at the asset for continuous optimization.

    • Run machine learning processes inside Litmus Edge ready analytics
    • Utilize available models for prediction, classification and anomaly detection
    • Save models from TensorFlow and upload them to Litmus Edge analytics
    • Access pre-built, easy to configure connectors to Cloudera, Azure, Oden and others for rapid machine learning deployment
    • Feed machine learning models with normalized data
    • Enable edge-side models to ingest data from local devices and act based on training received from cloud-based platforms
    • Run new models at the asset to deliver corrective actions in real-time