Building the information layer of health through biomolecular causality
Decode mechanisms. Prevent disease.
Why current approaches fall short
Healthcare generates unprecedented data—genomics, transcriptomics, epigenomics, metabolomics, biometrics—yet these remain fragmented and underutilized.
Current AI approaches find correlations in this data. But correlation isn't causation. Without understanding why a molecular pattern leads to disease, recommendations remain guesswork.
Factor graphs meet multi-omics data
We integrate multi-omics data (genomic, epigenetic, metabolomic, biometric) with scientific literature through factor graphs—probabilistic models that map causal relationships at every biological scale.
Think of it as a living map of human biology:
Unlike foundation models that pattern-match, our approach reasons about why molecular states lead to specific health trajectories. Every recommendation traces back to a causal mechanism.
Beyond Foundation Models
We use foundation models as probability distributions—inputs to our factor graphs, not endpoints. This enables true causal reasoning instead of sophisticated correlation.
From molecules to medicine
Disease Prevention
Detect disease-trending processes before clinical symptoms
Traditional diagnostics catch disease after damage occurs. Our causal models identify molecular trajectories toward inflammation, metabolic syndrome, and cardiovascular disease years earlier—when intervention is most effective.
Personalized Interventions
Recommendations rooted in your molecular causality
Rather than generic advice, we trace your specific molecular state through biological pathways to identify personalized interventions—lifestyle changes, supplements, therapies—with clear mechanistic rationale.
Research & Development
Partner to build custom causal models for your data
We create specialized factor graph models tailored to specific data modalities: genomic variants, methylation patterns, metabolomics, clinical biomarkers. Each implementation strengthens the network for all partners.
Build the future of precision health with us
Co-Development Partnerships
Collaborate on novel applications of causal biomolecular modeling. We bring the factor graph infrastructure and causal AI expertise—you bring domain knowledge and data.
Ideal for: Biotech companies, longevity clinics, research institutions exploring new therapeutic targets.
Data Partnerships
Provide specialized data modalities (methylation, proteomics, clinical outcomes) to help build and validate causal models. Gain insights from the resulting models.
Ideal for: Testing companies, health platforms with unique datasets, clinical research organizations.
Research Collaborations
Joint research initiatives to advance the science of biomolecular causality and precision health.
Ideal for: Academic labs, consortium partners, organizations pushing the boundaries of computational biology.
Built on pioneering research in causal AI
- UC Santa Cruz: Bioengineering, Cognitive Science, Neuroscience
- Architected genomic pipelines processing 10,000+ genomes (Gladstone Institutes, AnswerALS consortium)
- Open-source contributor to causal genomics methods
- Specializes in scalable bioinformatics infrastructure
Ready to explore what's possible?
Whether you're developing precision therapeutics, building health platforms, or advancing longevity science—Aeon Bio's causal modeling infrastructure can accelerate your work.
Let's discuss how biomolecular causality can transform your approach to human health.