System

System is a pioneering AI platform designed to address the world's most complex challenges by shifting the paradigm from siloed knowledge to an interconnected, systems-based understanding. At its core is the System Graph, a quantitative model that represents the world as a single, intricate system. This platform is built for researchers, data scientists, and organizations who need to synthesize vast amounts of scientific literature to uncover deep, quantitative relationships between variables in fields like healthcare, climate science, and social sciences.

By leveraging state-of-the-art Large Language Models (LLMs), advanced graph technology, complexity science, and causal inference techniques, System automates the extraction of statistical findings from verified sources such as peer-reviewed studies, the PubMed database, GWAS Catalog, and expert-curated databases. This process generates millions of normalized, quantitative relationships, allowing users to move beyond simple keyword searches to a systemic analysis of causes and effects.

How to use System

System is primarily accessed and utilized through its powerful API, designed for developers, researchers, and data analysts. Users can integrate the System Graph into their own applications and models to perform complex analyses.

1. API Integration: Connect to the System Graph using GraphQL or REST APIs. This allows you to programmatically query the vast network of concepts and relationships.
2. Formulate Queries: Instead of searching for documents, you query for relationships between variables. For example, a researcher could query the system to understand the upstream causes and downstream effects of a specific biomarker or the social and environmental determinants of a disease like stroke.
3. Synthesize Evidence: The platform automatically synthesizes findings from thousands of sources, providing a meta-analytical view of the evidence. It presents statistical associations (e.g., odds ratios, hazard ratios) with complete provenance, linking every piece of data back to its original source.
4. Build Models: Use the extracted quantitative data to build predictive models, develop population health roadmaps, or conduct robust literature reviews for evidence-based decision-making.

Core Features of System

• System Graph: A large-scale, quantitative model of interconnected systems, unifying different scales (e.g., molecular to epidemiological) and domains (e.g., health to climate).
• AI-Powered Data Extraction: Utilizes sophisticated LLMs to extract quantitative findings from a continuously expanding corpus of verified sources like PubMed and other scientific databases.
• Quantitative & Causal Inference: Focuses on statistical relationships (e.g., hazard ratios, correlation coefficients) and uses causal inference techniques to link them, enabling a deeper, systemic understanding.
• Complete Knowledge Provenance: Offers full transparency by tracing every relationship and finding back to its original peer-reviewed source, minimizing the risk of AI 'hallucinations'.
• Continuous Updates: The graph is updated daily with new findings, ensuring there is no knowledge cut-off and the model remains current.
• High Interoperability: Nodes and variables are interoperable with established biomedical ontologies like UMLS, SNOMED CT, LOINC, and MeSH, facilitating integration with existing health data systems.
• Flexible API Access: The entire graph is accessible and queryable via GraphQL, REST APIs, or natural language, providing robust tooling for custom applications.

Use Cases for System

System is trusted by leading organizations for complex research and data synthesis:

• Medical and Health Research: The Novartis Foundation uses System in its AI4HealthyCities initiative to identify key drivers of cardiovascular health, understand relationships between determinants and outcomes, and inform public health strategies.
• Evidence Synthesis and Literature Review: Atropos Health leverages System for accurate and robust literature reviews, evaluating the quality and relevance of citations to synthesize evidence efficiently.
• Understanding Complex Diseases: Researchers can explore the systemic nature of conditions like cognitive dysfunction, mapping out the intricate web of genetic, environmental, and social factors.
• Climate and Health Analysis: The platform can be used to model the changing and complex relationship between climate factors and health outcomes.

Advantages of System

System offers a distinct advantage over traditional research methods and standard LLM-based models. While foundation models are trained on a wide range of unverified sources, System exclusively uses trusted, verified scientific literature. Its architecture is based on normalized entities and quantitative relationships, not just token embeddings, which allows for deterministic, systemic reasoning and eliminates the risk of hallucination. This focus on quantitative, verifiable data from trusted sources makes it an indispensable tool for scientific and clinical decision-making.

Pricing and Plans

System is an enterprise-grade platform primarily serving research institutions, foundations, and corporations. Pricing information is not publicly listed on the website. Interested parties are encouraged to contact the System team directly through their website to learn more about API access, partnerships, and customized plans tailored to their specific needs.