Same method, applied to different systems, in order. This is how the path connects — and why it doesn't stop at one industry.
“I got into industrial engineering to answer one question: how do you get something to its true potential — the difference between working and optimized?”
My path started with a B.Sc. in Industrial Engineering at Ferdowsi University of Mashhad, followed by production planning intern, industrial engineer, and — within a few years — lead industrial engineering manager, running planning, production, quality, and inventory for a full manufacturing unit.
At the same time, I went back for an M.Sc. in Industrial Engineering — Systems Optimization — at Tarbiat Modares University, where my thesis applied reinforcement learning to a real revenue-decision problem. That's the base under the AI and optimization work I do now — not a recent interest, but where it started.
That range of responsibility inside one factory, reached that fast, is where I learned that most systems break for the same handful of reasons, no matter what the org chart says.
From there, I moved into software development as a QA manager — the same discipline of process control and defect tracking, applied to a completely different kind of production line: code instead of a factory floor. It was my first real foothold in tech, and proof the method held up outside industrial systems entirely.
Then I moved into luxury real estate business development — a different industry, different buyers, different pressure, and on paper nothing to do with the factory floor or software. But the same broken pattern showed up again: static pricing, manual client handling, no system behind the decisions being made. I rebuilt the pricing process and brought generative AI into the sales workflow. That's the actual pattern behind every move I've made — walk into an industry that isn't mine yet, find the same handful of failure modes wearing different clothes, and fix them with the same method.
Today I'm a product manager in tech, building AI-driven software from the ground up. Factory floor, software QA, real estate, now product — four industries that don't share a playbook, run by the same underlying logic. That's what makes an external optimizer useful: not expertise in your industry specifically, but a method that's already been proven to transfer.
That's also why Max Optimization Lab and the YouTube channel exist — to share the method as it keeps evolving, instead of keeping it in one industry.
Bring the process, decision, or workflow that's underperforming — industrial, digital, or somewhere in between. I'll tell you what's actually going on.
