Methodbrain is pioneering the science behind UI structural engineering. Our methods are based on more than a decade of research that has resulted in theoretical models and major advances in understanding the structural nature of user interfaces.
Methodbrain is testing the industry’s first information modeling software capable of a mapping the structural dynamics of concept relationships to measure and predict structural fitness. The ability to quantify UI structure enables teams to better predict the usability and performance of application interfaces under conditions that have been beyond most teams knowledge. This capability will have a significant impact on the $
A major reason for project failure is that while business assertions are exposed, they are never modeled into a conceptual environment that enable a referenceable conceptual structure. The technical implementation of this structure could be a knowledge graph (KG) or a LLM interface. Knowledge graphs don’t magically appear, they must be designed and engineered and is a reason why many companies have been slow to adopt KGs.
KG and LLM make up the last mile of intelligent information access, but the first mile requires intentional human intervention and curation of corporate knowledge. This happens naturally across an enterprise but never progresses to the where the proper conceptual models transformed into KG, LLMs, ML, etc. Two things have prevented this: lack of accountability within organizations to design information models and an understanding of structure to know what models actually need to be created for a given scenario.
The industry has decent variety of information modelers that practice in the areas of information architecture, taxonomy, and ontology, and the many people that contribute go by wide range of titles. Methodbrain has the capability to help teams to certify the integrity of their structural designs.
Structure has been acknowledged mainly as a point of reference by information architecture practitioners since the late 1990s. It has never but quantifying it has never been achieved.