VŌR uses descriptive, predictive, and prescriptive analytics cooperatively to provide deep, rich, and meaningful insights into your data.

VŌR automates the discovery of unknowns by identifying relationships and behaviors that it finds “interesting” and forming hypotheses around the discovery.

VŌR incorporates multiple methods and mechanisms to automatically generate hypotheses from data sets, and explore them to determine their validity and impact.

Unlike most competitors, VOR leverages historical analytics to enhance and evolve the quality of real-time analytics. This iterative analytics process allows the AI engine to generate more confident hypothesis and recommended courses of action.

VŌR leverages ISSAC’s location representation technology to provide extremely accurate 4-D geo-tagging for surface, air, and space. This technology also reduces 4-D point data sizes by more than 50%, drastically reducing computing power and storage needs.

For data storage and curation ISSAC’s ChewieDB technology organically builds graph structures around data, uncovering naturally-formed relationships between objects. Compatible with multiple DB languages, ChewieDB provides an immediately traversable data structure, without the need for a schema beforehand.