Enabling sustainable utilization through connected sensing
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How SwitchOn works?

Connect

Wireless Non-Intrusive nodes stream high-frequency data in real-time.


Analytics and Machine Learning on the Sensors allows real-time decision-making


End-to-End Enterprise grade security enforced by secure hardware-design and firmware architecture.


Compute

Store and analyze and take decisions based on Big-Data in real-time.


Better equipment design through Digital-Twins based on Multi-sensor input.


Machine Learning to predict equipment failure and reduce down-time.


Conserve

Data-driven decisions with actionable intelligence on web and mobile Dasboard.


Reduce Operational Expenditure with energy management and predictive maintenance.


Upto 15% energy savings with energy modelling and usage pattern analytics.


we serve

Buildings and Facilities

Energy Savings by Usage Model Analytics

15%

Industries

Savings from Predictive Maintenance

40%

OEMs

Product Innovation Success from Digital Twins

125%
35
Active
Devices
5
Equipment
Optimized
10 mn
Events
Processed
9%
Energy
Saved

Technology we build on





Hardware

Energy Meter:
3-Phase class1 wireless energy meters with advanced harmonics and signature capture capabilities
Vibration Sensor:
IP65-grade vibration sensors with a bandwidth of 1.6KHz. The vibration sensors are completely wireless and non-intrusive.
Ambient Temperature and Humidity Sensor:
Wireless Non-Intrusive battery-powered or, mains-powered sensors cotinuously stream real-feel temperature and humidity analytics to the cloud.

Cloud

Big Data Capability:
Complete Industrial IoT cloud architecture capable of handling more than 100 million events.
Security:
End-to-End Enterprise-grade security implemented through HTTPS/SSL/HSTS/PKC.
Open API:
Scalability and interoperability acheived through open APIs that other customers and vendors can directly interact with.

Analytics

Usage Pattern Analytics:
Analytics platform capable of using ambient weather and multiple device characteristics for data visualization
Energy Model:
Deep Neural Network based Energy Model to predict and optimize the energy consumption of industrial equipment.
Building Thermal Model:
Second-accurate thermal model of the entire building that allows us to drive Building Information Model Usecases

Machine Learning

Predictive Maintenance:
Deep Neural Network based predictive maintenance models to predict equipment failure at early-stages.
Machine Learning at the Edge:
Proprietary hardware and algorithms allow us to run machine learning applications in our hardware and reduce end-to-end latency.
Equipment/Energy Model:
Proprietary Industrial Equipment model trained on millions of lines of industrial data to give accurate digital twins of critical equipment.

Dashboard

Real-time Visualization Platform:
The real-time visualization platform allows the customers to see the state of their building/Industry in real-time and take data-driven decisions.
Real-time Alerts:
Capable of alerting our customers through user-configurable SMS/Email in case of a malfunction within 1 second of the incident.
Accessibility:
Web and Mobile Platform capable of global access in an end-to-end secure manner.
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