The promise of longevity medicine is often framed in decades, but the practical challenge for any clinic is the next patient visit. How do you synthesize a decade of disparate lab results, a year of wearable data, and a family history into a coherent, actionable plan in the minutes you have? Longevity AI, a New York and Tel Aviv-based startup, is betting the answer lies in a clinical co-pilot named Florence, trained on a dataset few can access: 1.6 million electronic health records spanning 20 years and over 5 million lives in Israel [Longevity AI, 2026].
The company’s platform aims to give physicians a continuously updated, longitudinal view of a patient, integrating EMRs, lab PDFs, and wearable streams to generate a unified biological risk profile. The AI-powered dashboard then translates these cross-domain patterns into structured summaries and personalized longevity plans. For a field that straddles proactive wellness and complex clinical medicine, the bet is that software can provide the evidence-based scaffolding to make longevity care reproducible at scale.
The data moat from Clalit
Longevity AI’s most distinctive asset is not its algorithms, but its foundational dataset. The company has a research partnership with Clalit Health Services, Israel’s largest integrated health network, which provides the anonymized, longitudinal medical records that train its clinical LLM, Florence. This partnership is more than a data license; it’s a validation pathway. Deploying within a massive, real-world health system like Clalit offers a testing ground for clinical utility that is rare for a startup at this stage. The company says its platform is already live with Clalit and within the U.S. [Crunchbase, Unknown].
Florence is designed to be a workhorse for time-pressed clinicians. The co-pilot can read and extract highlights from any health report PDF in under a minute, and its knowledge base is grounded in a continuously updated library of over 10,000 clinically validated studies. The goal is to flag meaningful shifts in a patient’s biomarkers early, enabling proactive guidance rather than reactive care. In a concierge or longevity clinic setting, where patient touchpoints are frequent and data-rich, such a tool could theoretically shift the clinician’s role from data integrator to strategic interpreter.
The team building a bridge
The company’s leadership reflects its dual focus on clinical rigor and commercial execution. While the founder is not named in public records, the executive team includes Chief Technology Officer Dor Daniel and Chief Operating Officer Kayla Raizner, who hold the reins on product and operations. The board includes Amihai Neiderman, founder of the clinical NLP company Nym Health, suggesting an emphasis on healthcare-specific AI.
With a team estimated at 11-50 employees, the company is actively hiring for roles like AI Engineer in Tel Aviv, indicating investment in core model development. The presence of roles like Longevity Researcher and a roster of data scientists points to a team built to navigate the intersection of clinical research and software engineering [1, 11].
| Role | Name | Note |
|---|---|---|
| CEO & Founder | Guy Leitersdorf | Listed as board member |
| Chief Technology Officer | Dor Daniel | [20] |
| Chief Operating Officer | Kayla Raizner | [20] |
| Chief Financial Officer | Omer Regev | [1] |
| Board Member | Amihai Neiderman | Founder of Nym Health |
Where the model meets the medicine
The ambition is clear, but the path to widespread clinical adoption is lined with regulatory and practical hurdles. Longevity AI’s platform, which surfaces risk profiles and supports decision-making, likely falls under the FDA’s category of Clinical Decision Support (CDS) software. The regulatory boundary between a helpful tool and a device requiring clearance is nuanced, hinging on how the information is presented to the clinician. The company has not disclosed any FDA submissions or clearances, which will be a key milestone for U.S. expansion beyond early-adopter clinics.
Furthermore, the “evidence-based” claim central to its marketing must withstand peer-reviewed scrutiny. While training on 1.6 million records is a significant advantage, validating that the AI’s outputs improve patient outcomes,not just organize data,is the true test. The partnership with Clalit provides a powerful venue for that validation research, but published results are not yet part of the public story.
The competitive landscape is also evolving. While no direct competitors are named in sources, the broader longevity and digital health arena is crowded with well-funded players like Fountain Life and NewLimit, which are approaching the same patient population from different angles (preventive care clinics and biotech, respectively) [Perplexity Sonar Pro Brief]. Longevity AI’s wedge is the clinical software layer, but it must demonstrate that its integrated platform delivers unique value beyond a suite of point solutions for data aggregation and patient engagement.
The next twelve months
The company has raised a total of $5 million in disclosed funding across two rounds, the latest noted in May 2025. The next phase will likely focus on proving commercial traction and clinical validation.
- Regulatory steps. Watch for any public filing or announcement regarding FDA qualification for its clinical co-pilot, a move that would significantly de-risk sales to larger U.S. health systems.
- Published research. Peer-reviewed publications stemming from the Clalit partnership would provide tangible evidence for the “evidence-based” claim and build credibility with a skeptical medical audience.
- Customer expansion. Moving beyond the initial deployment with Clalit to name additional, sizable clinic networks in North America would signal that the product is crossing the chasm from pilot to standard workflow.
The patient population here is adults seeking proactive, data-driven care to extend their healthspan, often in concierge or dedicated longevity clinics. The standard of care today is fragmented: a patient might have a cardiologist reviewing their lipid panel, a nutritionist analyzing their food log, and a wearable tracking their sleep, with no single practitioner synthesizing it all. The burden of integration falls on the patient or an exceptionally diligent primary care physician. Longevity AI is attempting to build that unified view into the clinical software itself, offering a single timeline where aging trajectories become visible. The success of that attempt hinges not on the sophistication of the AI, but on its humble utility in a real exam room.
Sources
- [Longevity AI, 2026] About Longevity AI: Mission, Team & Investors | https://www.longevity-ai.com/about
- [Crunchbase, Unknown] Longevity AI - Crunchbase Company Profile & Funding | https://www.crunchbase.com/organization/longevity-ai
- [Longevity AI, 2026] Longevity AI: AI Platform for Medical Practices | https://www.longevity-ai.com/
- [Perplexity Sonar Pro Brief, Unknown] PERPLEXITY SONAR PRO BRIEF
- [Tracxn, 2026] Longevity AI | https://platform.tracxn.com/a/d/company/6a635d8a7142c819dfd2131a/longevity%20ai?utm_source=parallel&utm_medium=ai#a:about
- [LinkedIn, 2026] Various team member profiles
- [DevJobs, 2026] AI Engineer Job Listing | https://devjobs.co.il/job-details/4442664256