What We
Do

01
Smart
Design

Strategic planning

1- Innovative Strategies

We design it for uses advanced metallurgical and computational strategies to optimize every heat in EAF and LF operations. Through AI-driven decision support and dynamic data integration, the system continuously analyzes process behavior, predicts thermal trends, and minimizes energy consumption. These strategies transform raw process data into real-time insights that support operators and engineers in achieving stable, efficient, and high-quality steel production.

2- Layer Architecture

We designed on a modular multi-layer architecture including Data Acquisition, Processing, Modeling, and Optimization layers. Each layer has a clear functional boundary and communicates through secure OPC and REST interfaces to ensure reliability and scalability. This architecture enables redundancy, load balancing, and seamless integration with Level-1 PLCs and plant SCADA systems while maintaining full data consistency across servers and clients.

3- Native Coding

‏We Developed using modern multi-platform technologies (C#, .NET MAUI, and MVVM pattern), OPPROCS™ delivers a native coding environment with high performance and secure execution. Every module—from real-time data handlers to AI models—runs natively on the server and client applications without third-party dependencies. This ensures low latency, stability, and long-term maintainability of the industrial software stack.
02
Digital Experience

Platform integration

1- Level-1 & PLC Connectivity

Seamless integration with industrial control systems such as Siemens, ABB, and Rockwell PLCs through OPC UA/DA, Modbus TCP, and direct protocol interfaces. Real-time data from furnaces, ladles, and auxiliary systems are unified into a secure Level-2 data layer, ensuring synchronized and traceable production information across all automation levels.

2- MES / ERP / Laboratory

We provides bi-directional data exchange with Manufacturing Execution Systems (MES), Enterprise Resource Planning (ERP) tools, and laboratory databases. Material tracking, chemical analysis, and production KPIs are automatically consolidated, eliminating manual data entry and improving consistency between operational and business systems.

3- Cloud & AI Analytics

We Through secure APIs and encrypted channels, process data can be mirrored to cloud environments for advanced analytics, AI model training, and predictive optimization. This hybrid integration approach allows on-premise reliability while enabling cloud-based intelligence for continuous process improvement and energy efficiency.
03
Data Science

Data platforms

1- Process Data Infrastructure

Create a dedicated industrial data layer that continuously collects, filters, and synchronizes data from Level-1 PLCs, sensors, and SCADA systems. It ensures timestamp accuracy, redundancy, and integrity for every signal transmitted during EAF and LF operations. Designed for high-volume metallurgical data, it forms the foundation for modeling, forecasting, and

2-Analytical Intelligence & Modeling

Designed At the heart of the platform lies an intelligent analytics engine capable of interpreting complex process patterns. It combines machine learning with metallurgical modeling to deliver accurate predictions for temperature, chemistry, and energy consumption. This analytical layer bridges raw industrial data with decision support, enabling real-time process

3- Data Strategy & Business Integration

We are Beyond process monitoring, the platform defines a comprehensive data strategy that links operational data with management systems. Seamless integration with MES, ERP, and laboratory databases ensures unified information flow from the shop floor to enterprise level. This connection empowers managers to make informed decisions based on live production indicators and