And the evidence women's prevention has never had.
Most of what medicine knows about women's health was built on studies of men, with a small adjustment added on for sex. The rest treats a woman as an average, not as someone whose body changes over years. Ashwam is building two things at once — a longitudinal health record for each woman, and the evidence base that finally makes prediction models built on women's actual lives possible.
Ashwam is a longitudinal health intelligence platform for women. It operates at two resolutions on the same substrate. The same data infrastructure serves both — which is why building either alone would not work. The N-of-1 record is the training data women's health has never had. The evidence infrastructure is what turns it into prediction models women's prevention has never had.
Five minutes a day builds an N-of-1 longitudinal health record that compounds across her whole life. Her own baseline. Her own drift. Her patterns, understood on her terms — not against a population average she was never represented in. The daily check-in connects to wearables, diagnostics, and clinical data she already has.
Millions of these individual records, with each woman's consent, become the evidence base women's biology has never had — covering the life stages, populations, and physiology that existing research has missed. From there: prediction models built on real women's lives, for cardiovascular risk, metabolic disease, cognitive decline, and the conditions that shape the post-reproductive years.
Each record is hers. The evidence is what it makes possible for every woman who comes after.
Midlife is where women's biology has been least well studied — and where the questions that matter most have been hardest to ask. Because the data that would answer them has never existed.
Ashwam is built to deepen over time. Each layer adds a new data slot to the same architecture; existing logic runs on richer inputs. No layer assumes data from the next. No layer requires code changes to the one before. A woman on the platform today benefits from every layer that follows, without interruption.
Five-minute daily check-in across eight domains — body, mind, emotion, sleep, food, exercise, social, environment. The foundation the rest of the platform is built on.
Reads the actual signal from your device — sleep, heart rate, activity, skin temperature — measured against your own baseline, not a manufacturer's average score. Apple Watch first, then Oura, Ultrahuman, Garmin, Fitbit, Samsung.
Lab results — when she chooses to add them — recalibrate her personal baseline. Raw values stay where they belong, in clinical and research systems; Ashwam sees only the derived signal.
Genetic risk context for each woman, integrated into the same architecture. Basic first, advanced later. Governed by distinct consent.
Devices that capture or modulate signal in ways the mainstream wearable category does not yet. Ashwam reads their signal into the same longitudinal record.
Diagnostics emerging across new substrates — body fluids, breath, structural imaging — at a pace the medical system has not yet adopted them. Ashwam folds their results into the woman's longitudinal record as they become available.
The Ashwam platform is built for every woman — across every starting point, every population. The evidence programmes that will generate its prediction models are sequenced with intent.
The first programmes address women the founding cohorts left out — South Asian, African and African-diaspora, Latina, Middle Eastern, and Indigenous women — on the disease axes where every major risk model in medicine today systematically under-predicts. There are no large-scale longitudinal cohorts of perimenopausal cardiovascular and metabolic transition for these populations — no SWAN equivalent. Building this dataset is where the evidence sequencing starts, with South Asian women first.
Subsequent programmes extend — new populations, new disease axes, or both — as the platform, the evidence base, and the clinical partnerships mature together.
The evidence base does not have them.
Each bar shows the women whose biology and health trajectories are missing from the longitudinal cohorts that anchor women's health today. The Global North bar aggregates the women within the US, UK, Australia, and Europe who are similarly understudied — ~95M in total.
Most data is 2024 estimates from UN Population Division, World Bank, or national census authorities.
Built around each woman — her biology, her baseline, her life. From a million such records — each held with consent, governed for research — comes the evidence base women's health has never had.
If you're building in women's health and your work belongs in this sensemaking layer,