Our product is delivering Digital Transformation as a Service

  1. Curation and Mobilization “Getting the Data” – SenseOps identifies, procures and constructs sensor/gateway hardware packages specific to the customers use case and needs. Through a mobilization effort, SenseOps engineers “walk the site” and work with the business/equipment owner to identify the critical components, assets, and processes that need to be monitored as well as any existing or legacy controller/systems and other data sources that may be accessible onsite. With this information, SenseOps will engineer, procure and construct the sensors and I/O with the SenseOps Gateway and bench test the solution at the SenseOps labs. Once tested, the entire solution is shipped to the customer location(s), where SenseOps technicians work with the customer (or its contractors) to do the physical installation of the sensors, the gateway(s), the connectivity and establish the data flow. The last step on-site is to work with the customer to establish data quality and validity, tuning/calibrating sensors, and ensuring registers are being properly read, recorded, and completion of the acceptance test.
  2. SenseOps Edge Suite – SenseOps delivers software as a service that runs directly on the SenseOps Gateway. The first component is the data acquisition and archive. SenseOps Edge-Archive acquires and stores the time-series/sensor data. Compression algorithms are configurable, however the hardware is typically sized to accommodate storage of 1 year (or more) of sensor data at high-resolution. Since SenseOps has curated the sensor package with the gateway, SenseOps also pre-configures the database to accept raw sensor data, contextualize, and perform streaming transformations on the data before making it available to other applications. With the “good” data in the archive, SenseOps Edge-Visualization makes the real-time and historical data available from an on-edge device webserver. SenseOps does this for key reasons; 1) Customers don’t want to ship their data off to a cloud vendor and lose control of where their data goes and who has access to it; 2) we economize on the use of the network – “if you stream all the data all the time – you will give all your money to the network service provider”.
  3. SenseOps Cloud Service – The SenseOps Edge solutions have multiple connectivity options to a variety of Enterprise/Cloud Based Systems. While SenseOps believes in the “power of the edge” there are many applications where Cloud technology makes sense. For example, benchmarking the performance of dozens or thousands of distributed assets, training predictive analytics or machine learning models, integration with enterprise applications such as OSIsoft PI System, ESRI ArcGIS, CMMS/EAM or SAP Hana. SenseOps supports integration with all of these (and more) Enterprise/Cloud based systems. However, SenseOps also recognizes that many customers do not have access to these Enterprise/Cloud solutions…This is where SenseOps Cloud Service comes in. The SenseOps Cloud Service provides small to mid-size businesses with access to enterprise-grade solutions for fleetwide situational awareness, deeper analytics, reporting, visualization and “cloud to end-customer” secure data exchanges.

Connecting the Unconnected


Data Acquisition

  • Many assets are unconnected
  • Acquisition devices are purpose built
  • Data transmission requires significant bandwidth and security
  • Acquisition technology doesn’t support local user interaction

Storage & Analysis

  • Large capital expense for centralized infrastructure
  • Remote data has little value until sent back to a centralized system
  • Analysis applications are expensive and not applicable to all assets

Using Data

  • Users who could benefit from the value of the data most are often remote
  • Data has to be sent back to remote assets to support desired outcome
  • Users often require company issued IT to leverage data
  • Users sometimes lack all the data necessary to make decisions



  • M2M & Sensor Layer
  • Standard APIs to Legacy Systems
  • External Data Sets


  • Buffer
  • Compress-wireless optimized
  • Archive


  • Data Quality
  • Sensor Drift
  • Deviation from Model
  • Events


  • Alerts, Events, and Escalation
  • Event and Data Push


  • Queries
  • Transform
  • Machine Learning
  • Interact with Cloud Models


  • Local Data
  • BYOD


  • Bi-Directional with Cloud Analytics
  • Benchmark
  • Continuous Model Improvement