Modeling the world as one interconnected system

Modeled as one complex system, unifying scales (e.g. molecular, epidemiological) and domains (e.g. health, climate)

Based on empirical quantitative findings extracted from scientific papers and expert-curated databases using SOTA large language models

Extracted findings are linked and normalized in a large-scale graph via a patented compound AI system with causal inference techniques

Connects previously siloed knowledge and meta-analyzes and synthesizes research

Interpretable and transparent with complete knowledge provenance

Continuously ‘trained’; no knowledge cut-off

Primary nodes are variables that are interoperable with established ontologies (e.g. UMLS, Wikidata)

Accessible and queryable via GraphQL, REST APIs, or natural language