Aevum doesn't catalogue the genome. It models the living human — every day — and it was trained, not prompted.
Velya Pulse is how the engine sees. Velya Twin is how it speaks. Both exist to feed the same core.
Two people with identical bloodwork and genetics can age at different rates.
The variable is largely psychological — stress load, emotional regulation, recovery. It moves daily, it compounds, and almost nothing in longevity measures it.
Every longevity app reads the body, ignores the mind, then nags you to sleep and walk more. Reading both, continuously, per person, isn't a feature you bolt onto an app.
It needed an architecture nobody had built.
So we built it.
Modelling a rate needs longitudinal, per-person, intervention-linked data — the one thing catalogue-first AI structurally lacks.
Stress archetype, emotional regulation, social contact, sleep architecture, allostatic load. The stream nobody else carries — and the one that moves fastest.
Recovery, strain, cardiovascular and movement signal — read off the watch a member already owns. No proprietary hardware, no new thing to wear.
Bloodwork and clinical biomarkers drawn through partner clinics, anchoring the daily signal to hard biology at a slower, deliberate cadence.
Everything speaks one vocabulary: the hallmarks of aging. Signal enters left, trajectory leaves right.
Each stream learns its own representation before anything is mixed.
Weights each stream per person, per day. Not an average — a learned gate.
A recurrent module carrying trajectory forward. Rate, not snapshot.
Models the effect of an intervention before a member commits to it.
Most health-AI companies place a foundation model at the centre and wrap it. There is no such node here. Aevum was trained on longitudinal structure, and the reasoning stays inside our own weights — which is also why it can be held to a trajectory rather than a plausible sentence.
The consumer layer — hardware-free, on the watch a member already owns. Aria builds the day around modelled energy, and every action taken deepens the profile underneath it.
Where the counterfactual engine becomes visible — members and clinicians see the modelled effect of an intervention before choosing it. Specified and prototyped, not sold.