Our approach is inspired by the work of L. Jimmie Savage, a pioneering statistician who helped shape modern decision theory. Savage viewed statistics as the science of making decisions under uncertainty—a principle we apply when helping clients harness data and AI to make confident, evidence-based choices.

Classical statistics—methods like ANOVA, linear regression, and time-series analysis—are mostly parametric. They assume specific mathematical forms, can be fitted by hand (or on weak computers), and have only a few dials you can turn. Their strength lies in explainability: you can clearly interpret the relationships between variables and understand why a result appears.

Modern statistics, by contrast, relies on computing power to open the door to millions of possible model configurations. Techniques such as ensemble learning, deep neural networks, and non-parametric methods allow far greater flexibility and predictive power. The trade-off is that they’re often less transparent—excellent for prediction, but not always for declaring which model is “best” or for explaining underlying causal effects.

A simple way to think about it:

AI — words go in, words come out.
ML — numbers go in, numbers come out.

AI is the broader field focused on building systems that simulate human reasoning, language, and decision-making. Machine learning is the mathematical core of AI—the algorithms that learn from numerical data to recognize patterns, make predictions, or automate decisions. In practice, ML is how AI learns.

We work with any organization or professional who needs to better understand, measure, and manage risk. Our clients include asset managers, insurers, sports betting firms, sports analytics organizations, financial institutions, and non-financial enterprises facing complex uncertainty. Whether you’re optimizing portfolios, modeling game outcomes, evaluating exposure, or integrating AI into decision processes, we help turn data into clarity.

We work across fixed-income and alternative/non-traditional asset classes, bringing real-time innovation to asset managers in those markets.

Examples include building in-house risk systems and applying statistical/ML techniques to accelerate analysis, enhance decision-making, and reduce costs.

We start with discovery and scoping, then design and build a tailored ML/AI solution, and support integration into your workflows.

Yes—our work is custom to your environment, designed to plug into the systems and data sources you already use.

We’re headquartered in New York City; our team consists of experienced financial analysts and ML/AI practitioners who build bespoke solutions for data-driven organizations.

We scope engagements based on project goals and complexity and provide a free quotation before kickoff.

 Contact us to discuss your use case and request a free quote; we’ll review your objectives and outline a proposed approach and timeline.