The world’s large technology enterprises are in a race to build AI technologies and applications to translate enormous data sets into informational insights, reduced uncertainty and machine automation.  These technologies and applications serve as the foundational layer for our work. 

We engage industry experts to suggest untapped data sources and to enrich the available data with their domain specific insights and wisdom.  Human insights and creative conjecture complement the raw observed data and provide an antidote to spurious correlations (data mining).  

The ideas and insights developed with the industry experts are then formalized into dynamic machine learning models and static algorithms.  The format and parameterization of the desired outcome (i.e. description, classification, prediction, or prescription) are also defined at this point.

We submit the machine learning models, algorithms and data feeds to the AI engine room and evaluate the outcomes produced.  An iterative process then starts of client feedback, prototype adjustment and retesting with the aim of producing a high value outcome worth further investment.

With a successful proof of concept, we proceed to build a scalable and enterprise-capable version of the AI application, refine the visual interfaces for human decision makers and connect to the system cybernetics for machine automation.