Our models correctly forecasted revenues and TV viewership, at very granular levels, for films and TV shows by analyzing large scale time-series data sets and optimizing many qualitative and quantitative aspects of financing, marketing, and distribution. The model also serves as a multi purpose (end users and/or asset generator) large scale recommendation system.

Our machine learning models were able to price extreme catastrophic risk (extreme weather events, earthquakes, etc.) for an insurance company, setting up the basis for a market based risk transfer mechanism and trading platform for alternative risk investors.

An A.I. designed by us and pre-dating Google's experiments, was able to learn thousands of musical styles, and able to create music in any style and/or any influence we define. Music is currently playing at Spotify, Apple Music, Google Play, etc. generating revenues for securitization. Click image for more info.

Our machine learning models successfully reduced the default risk associated with loan origination for different types of products, increasing risk-adjusted returns for a financial services firm in both, structuring and trading.

We created an A.I. that was able to generate new rules for insurance underwriting, by analyzing thousands of actual polices. Our A.I. helped create an expanded knowledge base, which allows greater risk-adjusted profitability when data containing time-series of claims is used jointly with a probabilistic graphical model.

Our A.I. allocates computing resources to different crypto currency mining & trading operations, based on market prices and other inputs. We use proven methodologies employed by top-tier investment banks, coupled with proprietary forecasting and optimization models that leverage computational and behavioral economics concepts.