If your device uses AI to read an image or reach a diagnostic decision, and a regulator expects continuing proof it still performs, VarunaForge AI fits. The engine is device-agnostic; the obligation is not. Same method, any modality.
The metric differs by device — sensitivity, specificity, concordance, time-to-result. The need doesn't: an independent, ongoing benchmark that proves the model still performs in the real world, against the claim it was cleared on.
Pathology, radiology, ophthalmology, dermatology, cardiology, oncology — wherever a model interprets an image, performance moves with the scanner, the site, and the population. We track it against the registered claim.
Where a model produces a diagnostic, prognostic, or measurement output, drift travels with the population, the instrument, and the workflow. The same independent benchmark applies.
If your device has a validated performance claim, it can drift away from it — and it can be watched. The engine adapts to the metric that matters for your device, not the other way round.
A cross-site reference you can't build from your own deployments — the only place an objective read on your real-world performance can come from.
Sensitivity and specificity tracked with confidence intervals, not a single number, so a flag means a real, sustained move outside the expected range — not noise.
Output that drops straight into your post-market file for your notified body or the FDA — proactive, deliberately collected, and yours to own.
The first engagement runs on your own retrospective validation data — no hospital integration, no live data plumbing, no disruption to your team.
Tell us what your device does and where you are with post-market evidence, and we'll show you what independent monitoring looks like against your own validation data.