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