The industry chases the easy, high-volume middle. Ardia is built for the long tail — the high-stakes, hand-done work that gets written off or left undone — and every answer it gives can be traced, checked, and attested by a human.
It's long-tail, high-stakes, and today it's either done by hand — slowly, expensively — or quietly ignored. Three places we keep seeing it:
A denied molecular-pathology claim is often worth more to appeal than to abandon — yet most are simply written off, because appealing means hours per claim against dense, shifting payer policy. The work is real; the labor to do it isn't there.
Clinical and genomic reports arrive in language built for specialists. The person they're about is left guessing what a variant, a value, or a flag actually means for them — so the information sits there, technically delivered and functionally unread.
Medication timing, movement, a nudge when a pattern changes — the ordinary rhythms that keep someone independent. High-stakes when they slip, and today almost entirely manual, resting on family and stretched care teams.
Not one model that does everything passably. A shared reasoning core, a set of named models that each do a single job exceptionally, and a compliance kernel underneath it all.
Every model reasons through TARA — a single, governed core with shared guardrails, so behaviour is consistent and inspectable rather than reinvented per feature.
Each use case gets its own model, tuned and evaluated for that one task. One does appeals, one explains reports, one supports older adults — none of them pretends to do all three.
Sentinel sits under every model: it strips identifiers before data reaches reasoning, and it writes an audit trail for each step — so what happened, and why, is always reconstructable.
Cadence is a real trained classifier for human-activity recognition, reaching 95.45% on a held-out split of the public UCI HAR dataset — a measured Ardia result on open data, not a projection.
All of it runs on Google Cloud under a Business Associate Agreement. One model is genuinely trained today (Cadence); the others are Ardia specialists built on a frontier model under our guardrails — and nothing here claims a production outcome.
We haven't shipped to a paying customer, and we don't pretend otherwise. No production results are claimed anywhere on this site. Every figure you'll see is one of exactly three things: a measured Ardia result (like Cadence's 95.45% on public UCI HAR data), a cited public number with its source, or a clearly labelled modelled target. If it isn't one of those, it doesn't go on the page.
Not a poster on the wall — constraints wired into the product, the code paths, and what we're willing to publish.
Sourced, measured, or labelled as modelled — every number earns its place. No polished figure without a provenance behind it.
Identifiers are stripped before data reaches reasoning. Protection isn't a setting to enable — it's the default path.
Ardia informs, drafts, and explains. It does not diagnose. A clinician always decides — that line does not move.
Nothing goes out the door — no appeal, no record, no action — until a person has reviewed and attested to it.
Everything above is easier to trust when you can watch it reason. Open the Studio and try a specialist on sample data — de-identified, logged, and never a diagnosis.
