About
Deep Longevity is a longevity technology company that leverages artificial intelligence to quantify biological aging through a suite of specialized aging clocks. Its commercially available products include Blood Age (derived from blood biomarkers), Mind Age (cognitive aging assessment), Biometrics Age (physical biometric data), and Epigenetic Age (DNA methylation analysis), with Microbiome Age and Face Age in active development. These clocks power two enterprise SaaS platforms: SenoClock, designed for healthcare providers and clinical settings, and SenoClock Gold, tailored for employer wellness programs. Both platforms generate detailed Biological Age Prediction Reports and Health Summary Reports, giving practitioners and individuals a nuanced view of organ health, aging trajectories, and actionable lifestyle, nutrition, and supplement recommendations. Deep Longevity also exposes its aging clock capabilities through an API, enabling software developers, life science companies, and diagnostics firms to integrate biological age predictions into their own products. The company serves a wide range of industries including insurance, government and public health, consumer health, and life science, and collaborates with leading universities and hospitals to continuously advance longevity science. Its mission is to redefine aging from a barrier into an optimized, data-driven journey toward enduring vitality.
Key Features
- Multi-Modal Aging Clocks: Assess biological age from blood biomarkers, DNA methylation, cognitive assessments, physical biometrics, facial imaging, and microbiome data for a comprehensive aging picture.
- SenoClock SaaS Platform: An enterprise platform for healthcare providers that generates Biological Age Prediction Reports and Health Summary Reports with organ-level insights and personalized recommendations.
- SenoClock Gold for Employers: A tailored wellness platform that helps organizations monitor employee biological age and promote proactive health interventions to reduce workforce health risks.
- Aging Clocks API: Exposes Deep Longevity's aging clock models via API, enabling life science companies, diagnostics firms, and developers to integrate biological age predictions into their own applications.
- Personalized Longevity Recommendations: Each report delivers individualized guidance on lifestyle, nutrition, and supplements based on the user's unique biological age profile and organ health indicators.
Use Cases
- Healthcare providers use SenoClock to generate biological age reports for patients during preventive care consultations, enabling early identification of accelerated aging and targeted lifestyle interventions.
- Insurance companies integrate aging clock data to improve underwriting accuracy by assessing policyholders' biological rather than chronological age as a proxy for health risk.
- Employers deploy SenoClock Gold to monitor workforce biological age trends and design data-driven wellness programs that reduce absenteeism and long-term healthcare costs.
- Life science and pharmaceutical companies use the API to incorporate biological age endpoints into clinical trial designs and drug efficacy research.
- Consumer health and longevity clinics offer epigenetic and blood age testing as premium services, using Deep Longevity's reports to provide clients with personalized anti-aging protocols.
Pros
- Comprehensive Multi-Omics Approach: Combines blood, DNA, biometric, and cognitive data into distinct aging clocks, offering a far more complete view of biological aging than single-biomarker solutions.
- Flexible Deployment Options: Available as a web-based SaaS platform for providers and employers, plus an API for custom integrations, making it versatile across industries and use cases.
- Research-Backed Science: Developed in collaboration with leading universities and hospitals, grounding the aging clock models in peer-reviewed longevity research.
Cons
- Enterprise-Focused Pricing: The platform is primarily designed for B2B clients such as insurers, hospitals, and employers, making it less accessible to individual consumers or small practices.
- Some Clocks Still Upcoming: Microbiome Age and Face Age are listed as in development, so the full suite of aging clocks is not yet commercially available.
- Requires Lab or Clinical Data: Generating accurate biological age predictions depends on access to blood tests, epigenetic panels, or other clinical measurements, which adds friction for end users.
Frequently Asked Questions
What are aging clocks and how do they work?
Aging clocks are AI models trained on biological data—such as blood biomarkers, DNA methylation patterns, or cognitive test results—to predict an individual's biological age, which may differ significantly from their chronological age. Deep Longevity's clocks analyze these inputs and produce an age estimate that reflects underlying health and aging processes.
What aging clocks does Deep Longevity currently offer?
Deep Longevity currently offers four commercially available aging clocks: Blood Age, Mind Age, Biometrics Age, and Epigenetic Age. Face Age and Microbiome Age are actively in development and coming soon.
Who is Deep Longevity designed for?
Deep Longevity serves a wide range of enterprise clients including healthcare providers, insurance companies, employers, diagnostics companies, government health agencies, and life science organizations. It also provides API access for software developers building longevity or health-tech applications.
Can Deep Longevity be integrated into existing healthcare software?
Yes. Deep Longevity provides an API that allows developers and companies to integrate aging clock predictions directly into their own platforms, apps, or clinical workflows without needing to use the SenoClock interface.
What kind of reports does the SenoClock platform generate?
SenoClock generates detailed Biological Age Prediction Reports and Health Summary Reports that highlight organ-specific aging, identify health risk areas, and provide personalized recommendations for lifestyle, nutrition, and supplements tailored to each individual's data.
