Harrison Marick

This project involved designing an artificial intelligence engine to analyze publicly traded companies' SEC filings and assess their regulatory risk. I built an engine that ingests SEC documents and uses a custom-built artificial intelligence algorithm that leverages NLP and sentiment analysis to evaluate publicly traded companies based on regulatory risk. In addition to the algorithm, I developed the entirety of the back-end infrastructure in AWS (Docker, ECS, Batch) that ran the updated results regularly.

Built a data-driven sports analytics platform that uses advanced modeling and machine learning to generate predictions, probabilities, and insights across multiple sports and betting markets. The platform is designed to translate complex statistical outputs into clear, actionable recommendations, helping users evaluate risk, identify value, and make more informed decisions in real time.

Built a secure, enterprise-grade AI platform designed to transform complex and fragmented data into clear, actionable intelligence. The platform combines governed retrieval, advanced language models, and customizable AI agents to help organizations search, analyze, and synthesize information across internal and external data sources. Built for scale and reliability, it enables AI-powered workflows that support decision-making, automation, and insight generation while maintaining strict controls around privacy, security, and data governance.