Easily and securely connect devices to the cloud. Reliably scale to billions of devices and trillions of messages.

PAASMER Cloud allows you to easily connect devices to the cloud and to other devices. AWS IoT supports HTTP, Web Sockets, and MQTT, a lightweight communication protocol specifically designed to tolerate intermittent connections, minimize the code footprint on devices, and reduce network bandwidth requirements. PAASMER Cloud also supports other industry-standard and custom protocols, and devices can communicate with each other even if they are using different protocols.

PAASMER Cloud provides authentication and end-to-end encryption throughout all points of connection, so that data is never exchanged between devices and PAASMER Cloud without proven identity. In addition, you can secure access to your devices and applications by applying policies with granular permissions.

With PAASMER Cloud, you can filter, transform, and act upon device data on the fly, based on business rules you define. You can update your rules to implement new device and application features at any time. PAASMER Cloud makes it easy to use other cloud services.


PAASMER BI platform is designed to analyze various aspects of IoT data from an ingestion, modeling, storage and analysis perspective. PAASMER BI focuses on delivering business outcomes (ROI) that answer key business questions.

Product manufacturers are embracing analytics as the cornerstone of their connected product experience and aftermarket service offering. When applied to the IoT, analytics focus on providing competitive differentiation and strategic insights to IoT product manufacturers and IoT service providers on the usage and behaviour of connected products.


IoT analytics can be grouped into 5 key benefits:

  1. Product & service feedback – manufacturers use product usage feedback to assess product quality and monitor behaviour thereby focusing on their R&D spend
  2. Usage behaviour tracking – understand how customers are interacting with the connected product and enhancing the experience to match the customer’s behaviour
  3. Operational analysis – optimize service offerings based on usage segmentation analysis and reduce the costs associated with providing that service
  4. Contextual analysis –enrich the sensor data with external data (weather, geolocation, etc) to provide greater context on how the physical objects are behaving in relation to their surroundings
  5. Predictive analysis & maintenance – use previous patterns and the knowledge of the current usage to predict future trends and behaviour


  • Objects unlimited
  • Quota
  • Dashboard
  • Managed
  • Data storage
  • Data analytics
  • Data Model
  • Pre-Built Model
  • Automated Model Selection
  • Machine Learning