Question: Tell us a little bit about your background and education.
Rishi Khan: I’ve been starting companies since I was in middle school. My first company was a lemonade stand with my brother and a friend. My dad financed the initial outlay but made us keep Excel spreadsheets of inventory, sales, P&L, and time spent. I think we broke even, but I learned a lot in the process. In high school I started a tutoring service and I also started two companies while in college.
I have a BS in Computer Engineering from the University of Delaware and a PhD in Computational Biology from a joint program between the University of Delaware and Thomas Jefferson University. After my PhD, I started a company with one of my PhD advisors, Dr. James Schwaber, and spent one year as a post doctoral fellow building a proof of concept for a DNA sequencing device that would reduce the then technology cost 1000X. After my post-doc I joined ET International as the VP of Research and Development and led projects that brought $8M dollars to the firm over 5 years. I was a Principle Investigator on a number of Dept. of Energy (DOE) and Dept. of Defense (DOD) high performance computing projects and built a strong network in that field.
Q: How did the idea of Extreme Scale Solutions arise and develop?
RK: When I struck out on my own in 2014, the original focus of Extreme Scale Solutions was on the marriage of High Performance Computing and Big Data, a fusion predicted by Gartner and heavily funded by VCs and the US Government. We originally intended to focus on DOE and DOD Research but started on Enterprise Solutions following a contract from a large Fortune 100 bank. We standardized database configurations and automated for database provisioning reducing a 30 day process with 9 teams to a fully automated 30 minute process.
In 2015, leveraging that initial success, we were contracted by another Fortune 100 bank to build out “Database As a Service”. This included all steps to bring siloed processes from multiple lines of business together into a unified self-service portal for planning, provisioning, and operations. After this contract, we built a platform, Nubrado, which shortens the journey for large enterprises to move to public cloud, or a private cloud-like environment from years to months.
Additionally, in 2017 we began working with Defense Advanced Research Projects Agency (DARPA) and Qualcomm on building next-generation computer architectures to speed up graph analytics by 1000X within the next five years. We believe these two tracks will converge as operational analytics becomes increasingly graph-oriented.
We often refer to our 3 pillars as R&D, software-as-a-service (SaaS) platform, and advisory services. Our R&D keeps us on the forefront of analytics and automation. Our SaaS platform provides planning, automation, and analytics support for large enterprise clouds. Our advisory services supplement our platform by helping companies define process and procedures to align their people with the platform.
Q: So what services, in a nutshell, do you offer companies today?
RK: Today, we provide a platform that enables large enterprises to migrate from legacy bespoke silos to a public or private unified cloud environment. This involves planning (What do I need to buy? Where will all of the databases go? How will they be isolated? How will the share resources? What databases should I migrate first to minimize cost or risk?), automated migration, automated lifecycle management actions, and operational analytics.
Q: What advice would you give to startups seeking to start a business in the world of IT/big data?
RK: Work at a startup to gain experience, credibility, network, and a cash hoard on somebody else’s dime. Give yourself a one-year runway (either through bootstrap funding, grants, or VC funding) to see if you can start to make money. Exist in a network of other entrepreneurs such as the Emerging Enterprise Center, Small Business Development Center, and CEO Thinktank®. Fail fast and often, and stick with what sells.
Q: What’s next for Extreme Scale?
Our major effort is to bring our platform, Nubrado, to alpha customers. We are currently engaging with Oracle on a number of potential customers in banking, insurance, telecom, and other fields. Our goal is to make it easy for large companies to manage massive database landscapes through standardization, automation, manage-many-as-one, management through measured metrics.
In addition, our research arm is focusing on extending work on graph analytic processors to machine learning and other problems that can benefit from software-defined reconfigurable hardware.