My focus is where engineering, CPQ systems and digital transformation come together — turning technical knowledge and business processes into structured, scalable digital workflows.
My path began with a curiosity for how things work. It grew through engineering, new languages, and life in different countries—then found a focus where technology meets the way industrial companies work.
I was born and raised in Istanbul in a family whose roots trace back to Montenegro. Growing up in one of the world’s most dynamic cities, I developed an interest in engineering and in understanding how things work—a curiosity that would eventually take me well beyond Istanbul.
I moved to Eskişehir to study Mechanical Engineering at Eskişehir Osmangazi University, beginning with a one-year English preparatory program followed by four years of engineering study.
For my bachelor’s thesis, I explored solar energy systems—my first opportunity to investigate an engineering subject independently and in depth.
For three summers, I joined the Work and Travel USA program—once in New Jersey and twice in Maine. Living and working abroad made me comfortable communicating in English, introduced me to different people and cultures, and taught me a great deal about independence.
☕ And, perhaps just as importantly, I learned how to make a pretty good coffee.After my bachelor’s degree, I moved to Germany, spent a year learning German, and then pursued a master’s degree in Mechanical Engineering at the University of Wuppertal. My interests increasingly moved beyond traditional engineering toward digital transformation, process optimization, and the intersection of engineering and software.
During the early part of my master’s studies, I worked in quality management at Scheer Druckgusstechnik. Later, I joined LOESCHE in Düsseldorf alongside my studies.
At LOESCHE, I encountered Configure, Price, Quote and found an area connecting many of my interests: engineering, complex products, business processes, data, automation, and software. My master’s thesis focused on optimizing the quotation creation process through CPQ, turning a subject of study into the field in which I wanted to continue developing.
Today, my work sits at the intersection of engineering, CPQ systems, process digitalization, and business process optimization. This site is a place to share the technologies, problems, and ideas that interest me—and to explore how digitalization and AI can improve the way industrial companies work.
I combine technical depth with hands-on experience developing CPQ systems and improving quotation processes.
In my CPQ work, I bring technical product knowledge, commercial requirements and data structures together to create consistent calculations and reliable quotation outputs.
The areas of expertise shared here are general in nature; they do not represent employer or customer internal systems, data or intellectual property.
A structured approach to configuring products, determining the right price, and generating consistent quotations.
Structured models, dependencies, characteristics, validation rules, and reusable product logic for configurable industrial equipment.
Cost allocation, weight-based logic, commercial factors, packing, freight, and transparent calculation structures.
Dynamic quotation templates, conditional content, multilingual text, engineering scope, and reliable output formatting.
Clear user flows and inputs that help proposal teams configure correctly without carrying the complexity themselves.
Test scenarios, regression checks, issue analysis, release coordination, and translation between users and developers.
Mechanical-engineering judgment applied to equipment, scope boundaries, technical data, weights, and proposal content.
Digital transformation starts with understanding how work gets done. My interest is in turning fragmented information, repetitive tasks and unclear handoffs into processes that are easier to follow, more reliable and ready to grow.
Putting an existing form on a screen is only a first step. The larger opportunity is to rethink how people, information and systems work together — across requests, planning, purchasing, approvals and operational work.
I would start by following a task from its initial request to completion: who contributes, what information they need and where work waits. Conversations with the people doing the work reveal details that a process diagram alone can miss.
Repeated data entry, unnecessary checks and unclear responsibilities should be questioned before they become part of a new system. A shorter, clearer process is a stronger foundation than a digital copy of every existing manual step.
Digital workflows depend on clear definitions and usable data. What does each field mean? Which source is authoritative? Who keeps it current? Consistent master data and shared rules help prevent departments from working with different versions of the same information.
Work often slows down at the boundaries between tasks and teams. Information should reach the right person at the right time, without getting lost in email chains or separate files. Clear handoffs make it easier to see what has been done, what is still missing and who takes the next step.
Routine checks, notifications, data transfers and repeatable calculations are useful starting points. Exceptions and decisions that need judgment should remain visible, with a clear way for people to intervene. Automation should reduce effort without hiding how the process works.
I would test a manageable first version with real users, including the situations that do not follow the ideal path. Feedback, clear guidance and gradual improvement matter as much as the technical setup. A workflow only creates value when people can use it confidently.
An approval workflow is useful when people can see what is missing and who needs to act. A shared data source is useful when it replaces conflicting spreadsheets. I would assess progress through practical signals: shorter waiting times, fewer corrections, less duplicate entry and better visibility of ongoing work.
I see potential in AI for finding knowledge, classifying incoming information and helping people prepare decisions. Its role should follow a concrete need, with attention to data quality, confidentiality and human review.
The starting question is not which technology is newest, but which problem is worth solving.
This website is where I document what I am learning and thinking about CPQ, process digitalization, and practical AI in industry. If these subjects interest you too, let’s exchange ideas.