It all started with the word “Algorithm.”
I was a 10-year-old kid in a small town in Iran when I realized that the word algorithm, one I struggled to pronounce, originated from the name of a Persian mathematician “Kharazmi,” who lived 1,200 years ago.
The first time I saw a computer, it was love at first sight. To my eye, a computer wasn’t merely a magical black box; I recognized immediately the endless possibilities computers provided. At the time, I was helping my dad, a civil engineer, with land surveying calculations all using a basic calculator. As you can imagine, this manual work took several painful weeks to finish. But instead of suffering through the tedium, I wrote a program to automate the process instead. It’s crazy to think about now, but at just thirteen, I not only wrote this software program, but I managed to sell it, since the software replaced any need for me to do the manual work. This experience laid the foundation for me to learn several programming languages, teach computer programming in university, and eventually design, implement, and lead teams that delivered software applications.
I began my career in New York by designing and implementing software for Building Information Modeling (BIM) to help construction managers gaining better insights into their projects. I enjoyed this work because it involved researching various technologies to find the best ones and implementing them in the construction industry, which desperately needs technological advancement.
It was clear from the start that most construction problems were rooted in a lack of access to the right data at the right time. That’s why I designed and developed several tools to enable advanced data analytics in project scheduling, cost estimation, and BIM.
As an example, construction project delays are major problems, so we developed software to use Monte Carlo simulations and machine learning to more accurately predict construction delays in a 4D simulation for the $4B Laguardia Airport Redevelopment Program in New York City.
These tools were useful, but the data was in silos and it was not easy to access it programmatically. The other challenge was that lots of important information was trapped in documents.
I spent the next several years trying to solve the interoperability challenges facing the construction industry. I shared our lessons learned with the construction community through presentations and workshops, whitepapers, and guest lectures in universities. I also participated in many AEC hackathons and tried to solve these challenges with like-minded individuals.
Finally I came to the conclusion that in order to build the most impactful solutions for the construction industry, I could learn lessons from other industries that had already adopted new tech. So I began consulting in other industries in order to explore the best avenues to solve fundamental problems in construction.
I started working with General Motors as a data scientist, where I was introduced to Robotic Process Automation and its potential in filling the gap when APIs are not available. I worked on autonomous driving data for Cadillac Supercruise and used big data analytics to uncover insights. I also built machine learning models to predict brand opinion.
Later, working on AI-Triage and Dynamic Scheduling projects, I designed and implemented Machine Learning models and AI systems for the Mayo Clinic. These models were designed to help automate and optimize the patient triage experience in their web application.
And I worked with Unispace (global leaders in business interior design and commercial interior design) to design AI-driven generative design processes for automated test-fit.
These projects led to nights and weekends of brainstorming with my cofounder, Dareen who I worked closely with for 5 years on various projects from Laguardia Redevelopment to NYCEDC.
We knew that the inefficiencies and challenges in the $10 trillion construction industry could be solved by leveraging cutting edge technologies from other industries.
I could not be more excited to co-found Gryps with Dareen.
Construction is fundamental to everyone and everything in society.
Our Gryps software platform uses robotic process automation (RPA), computer vision, and machine learning to connect to the various siloed systems in construction, ingest and understand the documents, and open up access to the data and insights produced by every single construction project.
We are excited to be modernizing the construction industry that we’ve devoted our careers to by using state-of-the-art machine learning and deep learning techniques.
We look forward to growing our team with more phenomenal people. Reach out if you are passionate about the world we live in, building technology that solves huge problems and creating an extremely valuable business together.
I think back to the first time I saw my very first computer and I remember that anything is possible.