Predict the location and behavior of your data, and draw meaningful conclusions from their motion with Analyze Corporation. We understand which techniques are best applied to a particular problem

Graph Theoretical and Network Techniques

Graph theory is a rich mathematical discipline with established solutions to many common problems such as connectedness, pathfinding, distances, and probabilistic prediction. Interconnectedness in human relationships can also be explored effectively through graph theoretical techniques.

Geometric and Pattern Matching Techniques

Sometimes motion is expressed in regular, predictable, or distinctive mathematical or geometric patterns. Shape and object recognition techniques, series pattern detection and geometric algorithms help us understand, organize, and classify motion. 

Machine Learning Techniques

Machine learning techniques help make sense of complex, unorganized, and random data sets.  With the combination of clustering, correlation, and dimensionality you can predict what an object in motion should do based on past behavior.  It can also detect irregular behavior from an object.


Illegal fishing results in approximately $20 billion in economic losses annually and causes significant environmental challenges for countries around the world.  To solve this problem, Analyze created a complex algorithm with geospatial data to identify illegal fishing behaviors.

To solve this problem, Analyze had to address multiple questions while working with an abundance of geospatial data.  For example, why does one vessel take a seemingly inefficient route between two places in perfectly good weather?  Or, why do two fishing vessels from different countries rendezvous on the open ocean?

This approach promises to be a factor in suppressing illegal fishing in the world, gaining economic value and reducing environmental challenges.