The platform

One engine. Any AI-enabled device. Regulator-ready output.

An independent, multi-site network feeds proprietary drift analytics; the result becomes a clean component of your post-market evidence. We don't rebuild for each device — the engine is device-agnostic and takes a new input.

How it works

Three stages, one continuous loop.

01 · Independent network

An external benchmark

Performance is compared across an independent, multi-site network — a cross-site reference you can't assemble from your own deployments alone. That outside view is what separates a real signal from local noise.

02 · Drift analytics

We detect, not just display

Performance is measured against the device's registered validation, with confidence intervals and a defined natural range. We flag true drift — sustained movement outside the range — and stay quiet on noise.

03 · Regulatory output

The filing stays yours

We deliver audit-ready performance evidence into your post-market reporting. You own the regulatory filing; we supply the independent data behind it.

Statistical rigor

We show the uncertainty, not just a number.

A single accuracy figure hides what your quality and regulatory teams — and the reviewer on the other side — actually need to see. Every metric is reported as three lines: the lower bound, the point estimate, and the upper bound. The uncertainty is explicit, and it tightens as real-world cases accumulate.

Drift is then defined the defensible way: the confidence interval crossing outside the expected range, not the point estimate alone. A statistically grounded trigger your reviewers can stand behind — not a judgement call.

Sensitivity
Tracked with LCI · point · UCI against the device's registered baseline.
Specificity
Tracked independently — a device fails two ways, and they matter differently.
Natural range
The expected wobble for a given case volume. Inside it is noise; outside and sustained is drift.
Site balance
Case contribution by site is shown, so a benchmark skewed by one dominant site is visible — never hidden.
Independent
An external, multi-site reference your own deployments can't replicate.
Fits your cycle
Aligned to the recurring post-market reporting rhythm you already run.
Provenance
An auditable chain from site to report — the record a reviewer can follow.
Why independent

You can't grade your own homework.

Your own data managers can run quality and connectivity, but they can't be the neutral benchmark for your own device — the independence is structurally required. The value only holds if the acceptance bar is set objectively, by a party that isn't the one being measured.

And because the benchmark only strengthens as more independent data accrues, the assurance you can put in front of a regulator gets stronger over time, not weaker.

Scope

Deliberately tight

We monitor instrument and application performance against initial validation to detect drift — not adverse-event reporting, literature reviews, or full-PSUR authoring.

Data

Anonymized at source

Performance data is anonymized at the source, so the central layer holds no personal data — clean by design, not by exception.

First pilot

Lowest-friction way in

The first engagement runs on the manufacturer's own retrospective validation data — no hospital, no live integration. We structure site access once a pilot defines what's needed.

See it on a device like yours.

We can walk the same engine across whatever your device does, and show what independent monitoring would look like on it — against your own validation data.