Petabyte Economics Corp.: First Generation, Cloud-Scalable Economic Intelligence

First-generation cloud-scalable economic intelligence.

Petabyte Economics Corp. is a private R&D lab based in Stowe, Vermont. We create new science and innovative technologies that unleash the power of big data, economic models for clients in industry and government.

Expert-led R&D

We are the researchers pioneering the science and technology behind cloud-scalable economic intelligence. We spent our academic careers designing and building economic, statistic, and AI models; so we know what solutions will match our clients' capabilities and needs.

Economic intelligence

Neural networks give black-box answers. Economic intelligence provides meaningful white-box answers grounded in coherent, structured understanding. EI can also leverage contextual data and expert opinion to increase accuracy and calibration beyond what is possible from training samples alone.

Cloud-scalable

Applications like forecasting, investment and operations optimization, bottom-to-top coherent decision making, and portfolio selection require fine grained, high dimensional data. We build models that use multi-GPU and cluster accelerated computing, and integrate with your data lakes, on premises or in the cloud.

Projects & Products

Economics-based Machine Learning for Private Capital Investment and Oversight.

We build scalable decision support systems, grounded in economics-based machine learning, for investment, divestment, and oversight in venture capital, private equity, M&A, and small business lending. Our data- and theory-driven approach delivers reliable, well-calibrated, and transparent models that quantify and attribute uncertainty and value for investment committees, regulatory compliance, and litigation.

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Meaningful demand forecasts at any grain.

CatFish is a fully customizable, multidimensional Bayesian simulation system for count data outcomes. It provides location-specific, consistently aggregatable and disaggregable, full joint distribution inference and forecasting with human-interpretable factors. CatFish delivers the massive, multidimensional demand forecasts needed to support modern supply chains. Its framework supports a wide variety of complex, industrial-scale applications.

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Minimize High-Dimensional Risk Functions Using Adaptive Bayesian Learning.

Newsvendor minimizes high-dimensional risk functions that encode complex, real-world determinants of profit and loss. It solves modern generalizations of its eponymous 1888 mathematical problem at scale. Newsvendor can optimally place millions of inventory items into thousands of locations or maximize returns while minimizing risk across billion-dollar investment portfolios.

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Lead Economists

Dr. Ed Egan

Dr. Egan is a serial entrepreneur, venture capitalist, and academic. He is a leading expert in venture finance, particularly VC, PE, M&A, and IPOs, and scalable economics-based machine learning. Dr. Egan received his Ph.D. in Business Administration from the Haas School, U.C. Berkeley, in 2012. He subsequently held positions at the NBER, Imperial College London, Rice, and Georgetown universities.

Prof. John Geweke

Prof. Emeritus Geweke is an internationally renowned Bayesian theorist and big data econometrician. He received his Ph.D. in economics from the University of Minnesota in 1975. Prof. Geweke is the author of over 100 papers in international refereed journals; the former editor of the top five journals in econometrics and applied statistics; and a past president of the International Society for Bayesian Analysis.

Dr. Garland Durham

Dr. Durham is an expert in Bayesian modeling, forecasting, and simulation using GPUs for massively parallel computation. He recieved his Ph.D. in economics from the University of North Carolina in 2001 and was a professor of finance at California Polytechnic State University. His research has been published in leading journals, including the Journals of Financial Economics and Econometrics.

Recent Federal Proposals

We made an invited submission for Phase I SBIR funding to the National Science Foundation in early 2025. Our project tests whether Bayesian machine learning can scale to solve real-world supply chain optimization problems. It combines full joint distribution demand forecasts from a spatiotemporal economic model with high-dimensional loss functions to optimize inventory placement. This approach could begin a new paradigm of industrial economic decision-making.

We applied to the Defense Logistics Agency's Emergent IV BAA in late 2024. Our proposal develops supply chain optimization tools to meet DLA's need for scalable, interpretable systems that support combat readiness. We model uncertainty and optimize materiel placement under posture, precision, and logistical constraints. The goal is to improve responsiveness, reduce cost, and empower DLA personnel to assess risk and make mission-aligned decisions across tactical, operational, and strategic levels.

The Small Business Administration's Office of Advocacy solicited research proposals from economists in late 2024. Our proposal creates a Bayesian simulation of the U.S. small business economy to evaluate the effects of federal policies and natural disasters at the county level. The project focuses on HUBZones, Opportunity Zones, and rural trends, and supports data-driven, evidence-based policymaking through causal inference and risk assessment.

Connect with us

info@petabyteeconomics.com
Website URL

www.petabyteeconomics.com
Longitude-Latitude

44.48N, 72.69W
Network Address

69.53.109.149