A vision sixteen years
in the making.
Kinosis is the continuation of that vision, built for a world that finally is.
[SEC_01] WHERE THIS COMES FROM
In 2010, I joined Daniel Nofal and The Wikilife Foundation with a single conviction: that a global, longitudinal repository of real-world health data — owned by the individuals who generated it — could fundamentally accelerate research into human health and longevity. The infrastructure didn't exist. The sensors were primitive. The AI to make sense of it wasn't there. The idea was right, but the world wasn't ready.
Kinosis is the continuation of that vision, built for a world that finally is.
The sensors now exist. The AI now exists. The audience — millions of people running deliberate health protocols and generating data across wearables, labs, and daily behavior — now exists. What has been missing is the platform that captures all of it in one place, structures it, measures it, and makes it available for science. That is what Kinosis is building.
[SEC_02] THE FOUNDATIONAL PRINCIPLE
Before anything else, one principle that is non-negotiable and shapes every decision Kinosis makes:
Your health data belongs to you.
Not to Kinosis. Not to the physician who prescribed your protocol. Not to the pharmaceutical company whose drug you are taking. Not to the hospital that treated you. To you.
This is not a privacy policy. It is an architectural commitment. Kinosis is built so that the data never leaves user control without explicit, granular, purpose-specific, revocable consent. Full export at any time. Complete deletion on request, with no residual copies anywhere. No individual data is ever sold or licensed — to anyone, on any terms. Individual data moves only at the user's own direction: to a provider they invite in, or as a voluntary donation to science. No financial incentive to distort what gets captured — Kinosis earns from subscriptions and services, not from the data itself.
This principle is core for two reasons. The practical one: users will only share their most sensitive data — their biology, their behavior, their symptoms, their failures — with a platform they trust completely. Trust is earned through control, not through promises. The moral one: a person's health history is among the most intimate things that exist. The fact that it has commercial value does not mean it should be treated as a commodity.
Everything that follows — the platform, the institutional layer, the scientific endgame — is built on this foundation. It only works because the data stays with the people it came from.
[SEC_03] THE PROBLEM WE ARE ACTUALLY SOLVING
Modern medicine is extraordinary at acute intervention and terrible at longitudinal understanding. We can perform heart surgery with robotic precision. We cannot tell you, with any confidence, whether the protocol you started six months ago is actually working — or why.
The reason is not ignorance. It is data. Specifically, the absence of the right kind of data: continuous, multi-dimensional, real-world behavioral and biological data, captured at the individual level, over time, at scale.
Clinical trials run for months. They measure a narrow set of variables. They exclude anyone who doesn't fit a narrow demographic profile. They end. Real life does not end. Real protocols do not end. And the gap between what happens in a trial and what happens when a real person runs a real protocol in the real world is vast — and almost entirely unmeasured.
Kinosis exists to close that gap.
[SEC_04] THE VISION
Kinosis is the protocol management platform for human health.
A protocol is any structured intervention — a supplement stack, a peptide regimen, a training program, a medication regimen, a lifestyle change, a clinical trial arm, a post-surgical recovery plan. Every person running a health protocol, at every scale from individual to institutional, should be able to define it, track it, measure adherence to it, and know whether it is working.
That is the product. But the product is also the means to something larger.
Every protocol tracked on Kinosis generates data. Behavioral data. Biomarker data. Environmental data. Symptom data. Lab data. Wearable data. Longitudinal data — not a snapshot, but a continuous record of a human life in motion. When that data is aggregated across millions of users, across thousands of protocols, across decades, it becomes something that has never existed before: the world's largest, most comprehensive, most longitudinal real-world health dataset.
With explicit user consent, that dataset can be donated to the scientific community. Not sold. Donated. Made available for research on human health in a way that no institution, no government, and no company has been able to do before.
The commercial and the scientific are kept distinct, deliberately. Institutions running commercial studies — pharma, health systems — pay for study infrastructure, cohort access, and analysis (Layer 4 below). The scientific community receives access as a donation, for non-commercial research, under per-study consent. In neither case does the data itself ever change hands.
That is the vision. A platform that starts by helping one person know whether their morning supplements are moving their HRV. And ends by answering questions about human health that science currently cannot ask.
The Four Layers
The vision is built in layers, each one feeding the next.
Layer 1
The Individual
The foundation. People running personal health protocols: longevity optimizers, athletes, chronic disease patients, GLP-1 users, anyone who has made a deliberate decision about their health and wants to know if it is working.
This layer is where trust is built and where the data starts. The product has to be genuinely, obviously useful here — people do not log data for science. They log because it helps them. Kinosis earns the data by earning the user.
Layer 2
The Provider
Physicians, nutritionists, coaches, functional medicine practitioners. They define protocols for their patients and get longitudinal adherence and efficacy data back — always with each patient's explicit, revocable consent; the data remains the patient's, and access ends when the patient says so. For providers this replaces a fundamental blind spot: they currently have no visibility into what happens between appointments. A physician with 500 patients brings 500 consented longitudinal records into the platform. Each one richer, deeper, and more continuous than anything in a clinical chart.
Layer 3
Health Systems & Hospitals
Protocol management at institutional scale. Post-surgical recovery. Chronic disease management programs. Preventive health initiatives. At this layer, Kinosis becomes infrastructure rather than a tool — embedded in care pathways, generating outcomes data that health systems need for value-based care, population health management, and regulatory reporting.
Layer 4
Pharma & Biotech
Drug development and post-market surveillance. This layer deserves careful explanation, because it raises an obvious question: why would a pharmaceutical company run a study on a platform where they do not own the data?
The answer is that they never needed to own the data. They needed the answers.
What pharma pays for in a clinical trial is not a dataset — it is the ability to answer a specific research question with statistical confidence. They spend $50 million on a trial not to own patient records but to know whether their drug works, in whom, and at what dose. A contract research organization running that trial never transfers patient data ownership to the sponsor either. What transfers is the analysis, the findings, and the regulatory evidence package. Kinosis offers the same structure: pharma pays for study infrastructure, cohort access, and the analytical outputs. The data stays with users. The answers go to the sponsor.
But Kinosis offers something that a traditional trial structurally cannot: longitudinal context that extends before and after the intervention. A trial starts at enrollment. Kinosis users have months or years of behavioral and biomarker history before the study begins, and continue generating data after it ends. That pre/post context is enormously valuable — it reveals baseline patterns, identifies confounders, and captures long-term effects that trials never see because they end too soon. A drug that shows efficacy at 12 weeks may behave very differently at 24 months. Kinosis knows, because it kept watching.
Additionally, clinical trials recruit from a narrow population: people healthy enough to enroll, willing to participate, geographically accessible to trial sites, and demographically unrepresentative of the real patient population. Kinosis studies can reach the actual population who will use the drug — diverse, global, real-world — with continuous data at a fraction of the trial cost.
[SEC_05] AGGREGATED EFFICACY
A new kind of evidence.
One of the most immediate and concrete expressions of the Kinosis vision is the publication of aggregated protocol efficacy data — anonymized, structured, and drawn from real users running real protocols in the real world.
Consider peptides. Thousands of people are currently self-experimenting with peptide protocols — BPC-157 for tissue repair, TB-500 for recovery, GHK-Cu for longevity, and dozens of others. They are tracking their own results, sharing anecdotes in forums, and making decisions based on fragmented, unstructured, self-reported information. There is no single place where someone can go to see, across a large population of real users: which peptide protocols are being followed, with what adherence, and with what measurable effect on biomarkers and reported outcomes.
Kinosis can be that place.
Because every protocol on Kinosis is structured — defined targets, tracked adherence, measured biomarkers — the platform can aggregate across users running similar protocols and publish what the data actually shows. Not anecdotes. Not marketing claims. Not small-sample studies run by companies with a commercial interest in the outcome. Anonymized, aggregated, real-world efficacy data from a large and growing population of committed protocol users.
The publications are not medical advice. They are evidence — the kind of structured, population-level signal that has been impossible to generate outside of a formal clinical trial. Kinosis generates it as a natural byproduct of the platform doing its job: tracking protocols and measuring their effects.
This is also a powerful distribution mechanism. Researchers, physicians, and the longevity community will come to Kinosis not just to track their own protocols, but to read the aggregated findings. The platform becomes a publishing venue for real-world evidence — something between a research database and a living clinical registry, updated continuously as more users contribute data.
[SEC_06] THE SCIENTIFIC ENDGAME
The dataset that emerges from all four layers is unprecedented.
Breadth
Hundreds of behavioral, nutritional, environmental, biomarker, symptomatic, and clinical dimensions — captured simultaneously, not in silos.
Depth
Continuous capture, not episodic snapshots — the full texture of a human life over time.
Scale
Millions of users across demographics, geographies, conditions, and protocols.
Structure
Protocol-anchored — every data point is attached to the intervention the person was running, making it analyzable in ways that unstructured health data never could be.
The research questions this unlocks are ones science currently cannot answer:
- —What protocols actually work, for whom, under what conditions, over what timeframe?
- —What are the real-world determinants of biological aging across diverse populations?
- —What does long-term supplement adherence actually predict at the biomarker level?
- —Which lifestyle interventions have the strongest causal effect on chronic disease progression?
- —What are the earliest detectable signals of metabolic decline, neurodegeneration, or cardiovascular disease — years before clinical presentation?
With explicit user consent and rigorous anonymization — structural anonymization, where re-identification is technically infeasible rather than merely contractually prohibited — the Kinosis dataset will be made available to the scientific community. The same spirit that animated the Human Genome Project — that some knowledge belongs to humanity — applied to the most personal and most universal dataset of all: how humans actually live, and what that does to their health over time.
[SEC_07] HOW TECHNOLOGY AMPLIFIES THE VISION
The vision described above is achievable today with current technology. But it will become exponentially more powerful as technology evolves — and the trajectory is clear.
Tracking becomes invisible
Today, logging requires intent. A user has to remember to log a meal, open an app, describe a symptom. This is the single biggest constraint on data completeness. It will not always be this way.
Wearable technology is moving from wrist-worn devices to continuous ambient sensors: smart rings, patches, implantables, and eventually materials woven into clothing and embedded in environments. Within a decade, the majority of physiologically relevant data — heart rate, HRV, temperature, glucose, cortisol, respiratory rate, sleep architecture, movement — will be captured passively, continuously, and without friction.
Environmental sensors will capture air quality, UV exposure, noise, temperature, and social context automatically. Computer vision and audio processing will infer meal composition, social interaction, stress, and emotional state from ambient data. The act of logging will increasingly shift from active reporting to passive confirmation — not "what did you eat?" but "you had what looks like eggs and coffee this morning, correct?"
As tracking becomes invisible, the dataset becomes complete. And a complete longitudinal dataset is qualitatively different from a partial one — it is the difference between a sketch and a photograph.
AI learns the individual
Today, Kinosis asks every user roughly the same questions. Tomorrow, it will ask each user precisely the right question, at precisely the right moment, in precisely the right way.
Contextual AI — trained on each user's behavioral patterns, circadian rhythms, logging habits, life context, and response history — will develop a precise model of who this person is and how they move through their day. It will know that this user logs reliably at 8am but forgets after 6pm. It will know that they are more likely to skip their Zone 2 session when they had a poor night of sleep. It will know that they respond better to data than to encouragement.
This is the JITAI framework — Just-In-Time Adaptive Interventions — applied at the individual level with genuine intelligence behind it. Not a push notification schedule. A model of a person, intervening at the optimal moment with the optimal prompt. The result is capture rates and engagement levels that static apps cannot approach.
The AI layer also becomes the interface layer. As language models mature, the conversation between a user and Kinosis will feel less like logging and more like talking to someone who knows you and your health deeply — who remembers everything, notices patterns you missed, and asks the question you didn't know to ask.
Analysis reaches conclusions not possible before
The analytical ceiling on health data has always been set by the quality and completeness of the data, not by the sophistication of the methods. With a complete longitudinal dataset at population scale, that ceiling disappears.
Causal inference at scale — distinguishing correlation from causation across millions of natural experiments — becomes tractable. If 50,000 people independently started a similar protocol at different times, under different conditions, with different baseline biomarkers, the dataset contains the signal to understand what actually drives outcomes versus what merely correlates with them.
Personalized prediction models — trained on the full depth of an individual's longitudinal record — will be able to forecast biomarker trajectories, flag early warning signals, and identify the specific protocol adjustments most likely to move a particular person's markers in the right direction. Not population averages. Individual predictions, grounded in the person's own history.
Multi-modal AI — processing genomic data, microbiome data, imaging, labs, wearables, behavioral patterns, and environmental context simultaneously — will surface connections between dimensions that no human analyst could find. The interaction effects between sleep quality, microbiome composition, exercise timing, and inflammatory markers, for example, are too complex for traditional statistical methods to untangle. They are exactly the kind of pattern that large-scale AI models are built to find.
Over time, the Kinosis platform becomes not just a place to track protocols, but a genuine intelligence about human health — one that knows more about the relationship between behavior and biology than any research institution in history, because it has seen more of it.
The platform becomes predictive
The final evolution is the most profound. Today, every health tool is retrospective — it tells you what already happened. A sufficiently rich longitudinal dataset, combined with the analytical power that is rapidly becoming available, turns that around: the platform begins to tell you what is likely to happen, and what to do about it now.
After years of continuous data on an individual — behavioral patterns, biomarker trends, environmental correlates, protocol history — the system develops a personal model of that person's health trajectory. Not population averages applied to them. Predictions grounded in their own history, their own patterns, their own responses to interventions. At population scale, these individual models aggregate into something more powerful still: the ability to detect the early signatures of disease years before clinical presentation, across millions of people, continuously — a fundamentally different kind of health intelligence than anything that exists today.
This is the logical endpoint of the data flywheel. The question is not whether it is possible. The question is who builds the platform that makes it happen.
For Kinosis, this is also an architectural commitment, not just a vision statement. The platform is being built from the beginning to handle the data volumes, the longitudinal depth, the multi-modal complexity, and the privacy architecture that this future requires. The decisions being made today — how data is structured, how consent is managed, how the correlation engine is built, how the API is designed — are being made with this endpoint in mind. A platform that is not built for this from the start cannot become it later. Kinosis is.
[SEC_08] WHY NOW
Three things are true simultaneously for the first time.
The sensors exist.
Wearables, home diagnostics, continuous glucose monitors, lab-on-chip technology, and ambient environmental sensors provide data capture at a resolution and a cost that was science fiction ten years ago.
The AI exists.
Large language models make conversational health logging natural and frictionless. Computer vision makes food and activity recognition accurate enough to be useful. Correlation engines can run in real time on individual data at a cost approaching zero.
The audience is ready.
The longevity movement has created a large, growing, and highly motivated population of people who are already tracking, already protocol-obsessed, and already frustrated by the fragmentation of their data across a dozen apps and devices that do not talk to each other. They are waiting for exactly this.
The infrastructure for the world's largest longitudinal health dataset is being built right now, by people who have no idea that is what they are building. Kinosis is the platform that makes it intentional.
Juan Gargiulo
Founder, Kinosis
[SEC_09] GET IN TOUCH
We're looking for the right partner.
Kinosis is at an inflection point. The product is live, the architecture is proven, and three revenue streams are active from day one — Pro subscriptions, lab testing, and physician memberships.
We're looking for a partner who understands the longevity and health data space, can open doors with physicians and health systems, and wants to help build something that genuinely improves how people manage their health over time.
Pre-seed round · Details available on request