My professional career beyond graduate studies is really in three parts, each roughly a decade long and none of those planned. The first decade exposed me to the Engineering R&D culture at the Departments of Space and Atomic Energy of Govt of India with a couple of years at the academia (IITB) in between. I still cherish that decade as the one which pushed me to build a strong foundation for a technology and R&D career. I worked in the inter-related areas of design of space launch vehicles, structural dynamics of helicopter blades, and high-speed rotors and rotor-dynamic systems with a focus on both metal and FRP composites structure.The second decade at the General Electric (JFWTC, Bangalore) was again an R&D experience but with a difference which GE had on offer. Significant learnings for me were on the business and leadership aspects of the entire R&D journey. Leading a technology team, a quality focused lean six-sigma MBB role and then a business operations role acted as a training ground for imbibing the do’s and don'ts of a leadership journey. Working knowledge of such business processes and the domain of ‘Strategic IP management’ were the two critical takeaways which propelled me into the next phase. The third decade, as I indicated, was spent in building an organization ground-up which I simply called TekIP, since it was at the junction of the two industries. Over the decade we have engaged several Fortune 500 companies and leading national universities and partnered with them in the development of their patent portfolios. We also set up a crack IT team and developed several patent-based workflows in the form of customized software products for our key clients. And it is really the productivity issues within the IP services which have pointed us to the next generation of disruption needed for our industry. And now the idea is to participate and help shape the disruption rather than wait to get disrupted.The entire knowledge management industry, of which patent analytics domain is a part of, is plagued with data and/or information overload leading to huge productivity issues. Cost of taking informed decisions has gone up tremendously. With the advent of development of NLP tools and some significant progress in the general AI/ML field, it is now seeming possible to find some practical solutions. However, it is up to the domain experts to adopt the science and engineer them into specific solutions. That is really the next set of problems I would want to solve working at the Graphemelabs and we have put together a good team to do just that.