Pioneering Decentralized Longevity Research
The state of biomedical research has largely remained unchanged over the past decades. Fragmented. Non-representative. And driven by perverse incentives. This is exactly why progress toward understanding and reversing aging has been slow.​
At Rejuve.AI, we believe the tools to change that already exist. By combining advanced AI with decentralized science, we are unlocking the data, insight, and collaboration needed to accelerate human longevity.​
We crowdsource data and build open AI research models to drive the discovery of rejuvenation biomarkers, effective protocols, and personalized longevity strategies. Our mission is to create a robust, transparent, and unbiased scientific ecosystem built on a simple principle:
The more the diverse data → The stronger the research → The closer we are to solving aging
International Longevity Research Database (IRLDB)
The International Longevity Research Database, or IRLDB, plays on “In Real Life” to emphasize its grounding in real-world human data. This global, IRB-exempt longitudinal observational study serves as a live repository for biohacking and self-experimentation data. It provides the scaffolding for a human analogue to the National Institute on Aging’s Interventions Testing Program (ITP), creating a decentralized foundation for studying rejuvenation in everyday contexts. App users contribute directly to the IRLDB, helping to enhance Rejuve.AI’s models and enable truly personalized longevity protocols.
Our Models: Turning Data into Rejuvenation Insights
At the heart of Rejuve.AI lies an expanding ecosystem of research models that translate diverse health data into measurable indicators of biological aging and rejuvenation.
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Our models are trained and validated using data from the International Longevity Research Database (IRLDB), our IRB-approved research protocol and global, longitudinal observational study combining anonymized datasets from Rejuve App users, partner longevity clinics, and public resources such as NHANES and UK Biobank. The IRLDB provides the foundation, while our models transform this data into actionable insight for personalized health optimization and large-scale longevity science.
RejuveAge-Q
(Survey-based Biological Age Model)
RejuveAge-Q estimates biological age using over one hundred validated health, lifestyle, and functional variables from NHANES surveys. These include cardiometabolic, sensory, musculoskeletal, and behavioral factors that collectively capture whole-body resilience. Trained to predict chronological age in reference cohorts, the model computes biological age acceleration as a marker of overall health status and rejuvenation potential. It provides an accessible entry point for global participation where lab testing is limited.
LinAge2
(Blood-Based Biological Age Model)
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LinAge2 is a clinical biological age clock developed by Fong et al. (npj Aging, 2025) using routine blood biomarkers to predict mortality and functional aging. Trained on NHANES data, it outperforms both chronological age and leading epigenetic clocks in forecasting healthspan and survival. Rejuve.AI has operationalized LinAge2 within a Python framework and enhanced it to impute missing values through age- and sex-matched cohorts, enabling robust biological age estimation across diverse populations and integration with rejuvenation tracking models.
Delphi
(Morbidity and Healthspan Prediction Model)
Delphi, developed by Shmatko et al. (Nature, 2025), is a generative transformer trained on large population datasets to model how diseases develop and interact across a lifetime. It predicts multi-morbidity risk and healthspan trajectories from an individual’s medical history. Rejuve.AI applies Delphi to aging-related conditions, using its generative framework to forecast healthspan outcomes and quantify biological resilience across time.
Insights Generator
(LongevityGPT)
The Insights Generator, or LongevityGPT, acts as the user’s intelligent longevity coach. It aggregates results from Rejuve.AI’s analytical models and translates them into personalized, conversational guidance, allowing users to “talk to” their data. Designed to adapt to each individual’s preferences and needs, it combines scientific rigor with empathy, helping users form healthier habits, understand aging biology, and engage with cutting-edge research. By connecting behavior, biomarkers, and outcomes, LongevityGPT turns complex data into actionable paths toward improved healthspan and reduced biological age.
THE PINNACLE OF COMPREHENSIVE AGI DEVELOPMENT
OpenCog Hyperon AGI Engine
SingularityNET’s OpenCog's Hyperon AGI Engine is a next-generation neurosymbolic AI framework designed to support scalable, transparent, and adaptive general intelligence. Hyperon brings together symbolic reasoning, probabilistic learning, and vector-based representations in order to integrate and manipulate complex knowledge structures in ways that complement modern machine learning models.
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A mature Hyperon implementation will strengthen the Rejuve.AI platform by enabling advanced reasoning across biological pathways, biomarkers, and intervention strategies. Rejuve.AI’s domain expertise and datasets will in turn help shape Hyperon’s biological intelligence modules as they evolve.
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Current joint work focuses on improving large language models using structured knowledge graphs that reduce hallucinations and enhance factual reasoning. Looking ahead, Rejuve.AI plans to apply Hyperon’s evolutionary learning capabilities to optimize personalized biohacking and rejuvenation protocols so the system continues to refine its recommendations as it learns from real-world outcomes.
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Hyperon may also support future exploratory research at the intersection of AI, biophysics, and quantum-level biological processes, including phenomena such as proton tunneling or quantum effects in enzymatic reactions that could influence aging.
Connecting Models for a Unified Longevity Framework
Rejuve.AI continues to expand its model suite, with additional systems in development to interpret single-omic and multi-omic data. These upcoming models will complement the existing framework by deepening biological resolution and refining predictive accuracy. The broader goal is to connect all Rejuve.AI models in collaboration with the SingularityNET and ASI ecosystem as well as the decentralized science (DeSci) ecosystem, creating a cohesive, adaptive network that continuously learns from real-world data to enhance precision, personalization, and measurable rejuvenation outcomes. Outputs inform both individual users and the wider scientific community through open, decentralized research collaboration.
