We built Force-V to solve a very real problem managing and monitoring solar plants at scale.
This project is a real-time IoT-based solar monitoring platform that processes over 100 million data records and supports 130+ industrial solar plants from a single system.
It reflects the kind of work we do at Data Function: building custom software, real-time data platforms, and AI-ready systems that can operate reliably at scale.
When solar infrastructure grows, operations become harder to manage.
The client needed a way to:
Existing tools were fragmented and slow. There was no centralized system that could handle real-time data at this scale.
We built Force-V, a custom IoT platform that connects solar hardware devices to a centralized system and processes data in real time.
The platform uses the MQTT protocol for reliable data transmission and integrates with an EMQX broker to handle continuous data streams from field devices.
For enterprise-grade messaging systems, similar architectures are also used with platforms like HiveMQ .
Incoming data is processed through a backend built with Django REST Framework, stored efficiently in a structured database, and delivered to the frontend with minimal latency.
The result is a system that gives complete visibility across all solar operations.
We designed the platform to process and visualize data in real time for instant decision-making.
Users can continuously monitor:
The system supports large-scale operations with 140+ plants managed from a single interface.
A centralized map-based view helps users:
We built an intelligent alerting system that continuously monitors performance and detects anomalies.
The system provides:
The platform provides detailed analytics to help users understand operational and financial performance.
Users can analyze:
We implemented a secure role-based access system for enterprise environments.
Administrators can:
The system is accessible across multiple platforms for maximum flexibility.
We developed:
We designed the system for performance, reliability, and scalability.
Core stack:
The system processes large volumes of data efficiently and delivers responses in near real time.

Although the platform is currently focused on monitoring and analytics, we built it with AI and machine learning integration in mind.
The data architecture supports:
This is the direction many industrial platforms are moving toward, and Force-V is already structured to support it.
We focused on making the system easy to use.
Users can:
The goal was to simplify complex data into a usable interface.

The mobile app allows users to:
This is especially useful for field teams and managers.
Force-V has been in production for over two years.
It has helped:
It continues to scale as more plants are added.
We build custom software systems for businesses that need reliability, scale, and real-time performance.
Our work includes:
Force-V is one example of what we build.
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