Knowledge Graphs now power many applications across diverse industries such as FinTech, Pharma and Manufacturing. Data volumes are growing at a staggering rate, and graphs with hundreds of billions edges are not uncommon. Computations on such data sets include querying, analytics, and pattern mining, and there is growing interest in using machine learning to perform inference on large graphs. In many applications, it is necessary to combine these operations seamlessly to extract actionable intelligence as quickly as possible. Katana Graph is a start-up based in Austin and the Bay Area that is building a scale-out platform for seamless, high-performance computing on such graph data sets. I will describe the key features of the Katana Graph Engine that enable high performance, some important use cases for this technology from Katana's customers, and the main lessons I have learned from doing a startup after a career in academia.