// PROJECT SUMMARY
BeeConn is a connected beekeeping ecosystem developed by Slovenian company Strip's, combining smart hive scales, sensors, gateways and other monitoring devices.
As the number of devices, users and measurements grew, the software platform behind the hardware needed to evolve as well.
JPM Systems performed a complete redesign of the BeeConn IoT platform, including its time-series backend, frontend, device management, data access, subscription logic and scalable cloud architecture.
The result is the central digital platform through which BeeConn's connected hardware becomes a complete service.
// THE PROBLEM
Connected hardware generates value through data.
BeeConn's flagship product is its connected beehive scale. By continuously measuring hive weight, beekeepers can monitor changes in the colony and track how much mass is being brought into the hive — providing valuable information about honey production and hive activity.
But monitoring many hives generates a significant amount of data.
A single device can produce measurements throughout the day. Multiply that by many devices, locations and customers, and the platform must efficiently store, process and retrieve large volumes of time-series data.
At the same time, BeeConn's business model was evolving. Different subscription packages needed to provide different levels of data resolution and functionality. Users needed better ways to organize devices, manage multiple apiaries and selectively share information.
The existing software architecture had reached the point where incremental changes were no longer enough. The platform needed to be redesigned.
// THE SOLUTION
We redesigned the BeeConn platform as a scalable IoT backend and web application capable of becoming the central software layer for the complete BeeConn hardware ecosystem.
The backend architecture was redesigned around efficient storage and retrieval of time-series sensor data, while the frontend was rebuilt to make large numbers of devices and measurements easier to manage and understand.
Users can organize devices by physical location or create their own custom groups, making the platform suitable for both individual beekeepers and users managing larger numbers of hives.
Subscription and payment logic was also integrated into the platform. Different service plans can provide different data resolutions — for example, hourly information on one package and more detailed measurements on another.
The result connects hardware, data and the business model in a single platform.
// OUR WORK
JPM Systems was responsible for:
- – IoT platform architecture
- – Complete software platform redesign
- – Custom software development
- – Backend development
- – Frontend development
- – Time-series database architecture
- – High-volume sensor data processing
- – API development
- – Device management
- – User and account management
- – Device grouping
- – Location-based device organization
- – Custom device groups
- – Data visualization
- – Historical data access
- – Subscription management
- – Payment integration
- – Feature and data access by subscription plan
- – Public data sharing
- – Cloud infrastructure
// KEY CHALLENGES
Storing and Serving High-Volume Time-Series Data
A single device can produce measurements throughout the day, and the platform must handle many devices across many locations and customers.
The backend was redesigned around efficient storage, processing and retrieval of large volumes of time-series data so that historical measurements remain fast to access as the ecosystem grows.
Connecting Hardware, Data and the Business Model
Different subscription packages needed to provide different levels of data resolution and functionality.
Subscription and payment logic was integrated directly into the platform so that service plans can control data resolution and features — connecting the connected hardware, the data it produces and the business model in a single system.
// THE RESULT
The redesigned BeeConn platform became the central digital layer that turns BeeConn's connected hardware into a complete service — a scalable IoT backend and web application built around high-volume time-series data, flexible device organization and subscription-based access.