Built by people who have made garments, and people who build software.

Arslan’s foundation is in the physical realities of garment manufacturing: running production floors, managing chemical treatment and finishing processes, and holding quality control to account through laboratory testing.

That means pH analysis, material strength verification, colourfastness and shrinkage behaviour, measured under real production conditions rather than ideal ones. It is this grounding in how fabric and construction actually perform that shapes the way MakeFit assesses failure risk: a prediction should hold up against the stresses a garment meets on a factory floor, not only against a model of how it ought to behave.

His focus at MakeFit is protecting that discipline as the platform grows, and keeping the distance between a technical specification and a finished, wearable garment small enough that a physical sample is no longer the only way to find out something is wrong.

Pankaj leads MakeFit’s product direction and AI architecture. He brings a product design background together with the ability to build the AI systems behind the interface himself, rather than handing that work on.

He designed and built the guided interview that replaces a spec sheet, the AI reasoning that turns it into a graded garment report, and the interface a non-technical founder uses day to day.

His focus is closing the gap between enterprise-grade manufacturing intelligence and the people who need it most: independent brands and small suppliers with no data team, no technical co-founder and no time for a platform that takes weeks to learn.