We combine specifically human ablities for finding patterns with high-speed machine processing and analyses of our multi-level semantic data pool.
Today, the network nature of companies is taking shape in the form of digitally stored information that is linked and used in diverse ways. The necessary analysis of this huge bulk of information available requires new tools that highlight relevant patterns and connections for individual users continuously rather than selectively, and in a way that is integrated into the worker's everyday routines.
System One meets this requirement by a bundle of algorithms that not only analyze texts, text fragments, links, and document similarity. Moreover, since System One can present a wide range of further relevant information without causing any additional workload to the user, the quality of machine-aided work changes fundamentally. The key to improving automatic inferences on a given pool of data lies in analyzing social structures and the use of data in time.
By using a broad data pool and new algorithmic strategies for dealing with information excess, we are able to reduce tedious routine tasks, radically simplify the use of electronic systems and make previously hidden patterns apparent.