The FT Technologies Team on February 6, 2015
It was almost two years ago—in a now shutdown restaurant on Cal Ave in Palo Alto—that we started discussing how we could reduce the operational inefficiency in our day-to-day lives. Leveraging our backgrounds in machine learning, mobile platforms, and sensor technologies, we set out to optimize pricing and inventory management for apparel retailers. In the process, we grew partnerships with fantastic companies and developed a suite of software and hardware technologies to help make the shopping experience more streamlined, user-friendly, and enjoyable.
The decision to join Palantir was not made lightly, but ultimately it was clear that our goal of solving important problems in a data-driven way is deeply aligned with theirs. We are confident that we will be able to drive more value—in the retail industry and beyond—working in concert with the awesome team at Palantir.
We would like to extend our gratitude to Pejman Nozad and Mar Hershenson for being our earliest believers and connecting us with our first clients. We also owe our families and friends quite a few shopping trips for nagging them about what seemed like every nuanced UI decision we made. Thanks for believing in us. We fancy that.
Seriously, now back to work.
CEO & Co-Founder
Amrit studied Computer Science with a concentration in Artificial Intelligence and MS&E with a concentration in OR and Optimization at Stanford. A lifelong shopaholic, nothing drives Amrit more than making things efficient.
CTO & Co-Founder
Ayush graduated from Stanford with degrees in Computer Science and Electrical Engineering with a focus in Mechatronics. He loves making and breaking things and hopes to enhance the shopping experience.
Catherine graduated with a B.S. in Computer Science from Stanford, and she'll be graduating with her master's with a focus in AI. She's enthusiastic about working on impactful projects at a large scale—and shopping.
Karanveer pursued a B.S. and M.S. in Computer Science at Stanford with a focus in AI and Convex Optimization. He is excited to improve his currently unfashionable wardrobe through cheaper prices and better recommendations.