Welcome to Projects & Tools

This section showcases a collection of custom-developed tools and projects designed to enhance processes, improve efficiency, and solve real-world challenges across various industries. Whether it's automation, system integration, or complex problem-solving, these solutions reflect a commitment to innovation and quality.

Custom Tool Development

Overview: Specializing in the development of tailored software tools and solutions, the focus is on optimizing workflows, integrating seamlessly with existing systems, and addressing specific business needs. Each project is built with a strong emphasis on efficiency, scalability, and user-friendly design.

Key Features:

  • Customized solutions for automation, data processing, and integration
  • User-friendly interfaces designed for ease of use
  • Real-time communication with industrial PLCs
  • Automated testing, validation, and data extraction
  • Alerting systems and real-time monitoring of critical metrics
  • Customizable reports and templates for production environments
  • Migration of legacy systems and seamless data synchronization
  • Service monitoring and automated task management for Windows environments

Technologies & Tools: Python, C#, .NET, VBA, Excel, MS Project, JavaScript, SQL, PROFINET, Modbus, OPC UA, Siemens WinCC, Wonderware InTouch

Highlighted Projects:

  • PLC I/O Simulation and Monitoring Tool: Developed a Python application to simulate and monitor PLC I/O, enabling real-time data read/write operations. The tool leveraged PLC communication protocols to improve control and automation processes.
  • Scheduling and Task Tracking System: Built a VBA tool for Excel and MS Project to automate scheduling and task tracking. It streamlined project management by syncing data, improving task assignment, and reducing errors.
  • CSV Database Export Sorting Tool: Developed tool to automate sorting of CSV data export by column within sections. The tool segmented, sorted, and recombined data, significantly reducing processing time for large datasets.