Jason has built search and recommendation systems for the past 6 years. He has consulted and advised a dozens startups in the last year to improve their RAG systems. He is the creator of the Instructor Python library. 

Scott Clark is Co-Founder and CEO of Distributional, the first enterprise platform that analyzes hidden behavioral signals from production log data to help you continuously improve your AI products.

The platform uses unsupervised learning to surface subsets of log data corresponding to shifts, clusters, or outliers in AI behavior that a user can investigate and track through custom filters, metrics, and alerts to catch issues and adapt this analysis over time. This empowers AI teams to better understand the behavior of their users and AI applications so that they can fix and improve those applications with confidence.

Scott was previously the Co-Founder and CEO of SigOpt, an enterprise AI optimization platform. After selling SigOpt to Intel in 2020, Scott was VP of AI and HPC engineering within the Supercomputing organization. Prior to SigOpt, Scott worked and performed research at various technology companies, universities, and national labs around the world. Scott holds a PhD in Applied Math and MS in Computer Science from Cornell University and BS degrees in Mathematics, Physics, and Computational Physics from Oregon State University. Scott was named to the Forbes 30 under 30 for enterprise tech in 2016.