Ayasdi is a machine intelligence software company that offers a software platform and applications to organizations looking to analyze and build predictive models using big data and / or highly dimensional data sets. Organizations and governments have deployed Ayasdis software across a variety of use cases including the development of clinical pathways for hospitals, anti-money laundering, fraud detection, trading strategies, customer segmentation, oil & gas well development, drug development, disease research, information security, anomaly detection and national security applications. Ayasdi focuses on hypothesis-free, automated analytics at scale. In effect the Ayasdi system consumes the target data set, runs many different unsupervised and supervised machine learning algorithms on the data, automatically finds and ranks best fits, and then applies topological data analysis to find similar groups within the resultant data. It presents the end analysis in the form of a network similarity map, which is useful for an analyst to use to further explore the groupings and correlations that the system has uncovered. This reduces the risk of bias since the system surfaces "what the data says" in an unbiased fashion, rather than relying on analysts or data scientists manually running algorithms in support of pre-existing hypotheses. Ayasdi then generates mathematical models which are deployed in predictive and operational systems and applications. Organizations using Ayasdi have found Ayasdis automated, platform-based approach to machine intelligence to be two to five orders of magnitude more efficient than existing approaches to big data analytics, as measured in the amount of time and expense required to complete analysis and build models using large and complex data sets. One widely reported example at a top five global systemically important bank was that to build models required for the annual Comprehensive Capital Analysis and Review (CCAR) process took 1,800 person months with traditional manual big data analytics and machine learning tools, but took 6 person months with Ayasdi. A project at a second global systemically important bank showed Ayasdi reducing the time to build risk models from 3,000 person hours to 10 minutes.