Prechelon uses ai-enabled software to transform unstructured data like documents, reports, email, and social media into high-value representations so machines have smarter data to reason about real-world events. Our real-world event data improves the odds of surfacing the most explanatory context so clients have differentiated decision support when confronted with unfolding stories critical to their workflows.
The force of any campaign starts with events. But which one? Our context machines tip the odds in favor of locating the richest explanatory context inside the torrent of text, image, and other unstructured data generation. Our event data ensures smarter connections between the physical world and the digital domain.
Our machines help clients speak a more analytical dialect. Macro linguistics™ helps clients more efficiently transform unstructured data into superior natural language and real-world event understanding computes. We designed it to generate near real-time situational understanding from elections to coups, pandemics to peace talks, terror attacks to natural disasters, and cryptocurrencies to Eurodollars.
Experts index differently. But how? We know how invaluable expert domain knowledge (EDK) is to designing real-world human and machine reasoning infrastructure because we have it. Our principles spent decades in the real-world testbed analyzing global markets and macro global information space developments to decipher signal from noise, local from foreign, and tactic from strategy.
Shawn Feely is the founder of Prechelon. He has two decades of global cross product investment research experience leading both a buy-side L/S macro thematic research effort and a Institutional Investor (II) recognized sell-side media equity research franchise. He started his career in fixed income derivatives. Shawn holds an M.B.A. from Columbia Business School, a B.S. from NYU’s Stern School of Business and he has earned the right to use the Chartered Financial Analyst (CFA) designation. He also completed graduate coursework in security studies and Eurasia at Columbia University’s SIPA. He is self-taught in NLP and applied machine learning.
We are a group of theoreticians, mathematicians, engineers, and data scientists solving contextual real-world event understanding problems.