The Ipvive Relational Intelligence Engine (IRIE©) is finely tuned to resonate with complex adaptive systems by leveraging the local precision of low dimensional Spherical geometry rather than inheriting the global bias of high-dimensional Euclidean geometry.

It is a new data and toolset which enables people to objectively understand, predict, and trigger change in complex adaptive systems as a whole. Its purpose is to understand what triggers change in relationships of components *within* the same layer (I.e. intra-relationships) and *between* layers (I.e. inter-relationships) to create emergent micro- and macroscopic patterns. Existing approaches were limited by their geometries,

- Euclidean geometry which has 1 infinite plane reaches limitations at edge cases producing false positives and false negatives
- Spherical geometry which has 1 finite manifold only enables understanding of intra-patterns

IRIE© was created based on the Thurston Geometrization Conjecture which inter-connected 8 different known geometries (including Euclidean and Spherical) which up until this point existed as independent paradigms.

It is not to be confused with artificial intelligence, many of which have evolved from Geoffrey Hinton and similar neural network based approaches. Nor should it be confused with agent-based model approaches to understanding CAS. Both fall into Euclidean, and sometimes into spherical geometry categories.