New Join early access: stress-test your medical imaging models in minutes. → Book with founders ×

Build models that never break in the wild.

Carez AI generates and stress tests datasets so models perform beyond the demo. From radiology to robotics defense and autonomous systems we help you prove reliability before deployment.

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How it works

Upload model

Drop in your CV model — medical, robotics, industrial, or automotive.

Fast setupCommon formats (DICOM/PNG/MP4)

Generate edge-case data

Create diverse scenarios: lighting & weather, motion, vendor/scanner mix, rare classes.

Lighting / WeatherMotionVendor mix

Stress-test & find failures

Surface blind spots and regressions with drift, bias, and OOD checks.

Drift / OODBias checks

Ship with evidence

Export reports & CI/CD hooks to prove production- or clinical-grade readiness.

BenchmarksCI/CD hook

Industry solutions

Start with radiology — then show how the engine scales everywhere computer vision breaks.

Science first. Built by researchers pushing AI in medical imaging.

OE logoAriseHealth logo2020INC logo

Backed by leading institutions

ERA NYC NVIDIA Inception Google for Startups Creative Destruction Lab

Build trust in every model release with Carez AI Engine

Generate cohorts → preview slices → validate bias & AUROC → ship CI/CD-ready evidence.

→ Start generating (book with founders) Browse synthetic datasets

THE CAREZ SYNTHETIC DATA ENGINE

An AI stack built for edge-case reality

Abstracted, modular, and battle-tested in medical imaging. Extensible to any vision domain.

01.0

Cohort Composer

Compose diverse cohorts: modality mix, vendor variance, dose/motion, and labeled lesions — at scale.

02.0

Slice Explorer

Preview thousands of slices instantly; window/level and skim cohorts without breaking flow.

03.0

Stress-Test & Bias Radar

Quantify robustness with AUROC/F1 by subgroup, bias detection, and slice-level regressions.

04.0

Evidence & CI/CD

Export regulator-ready PDFs/CSVs/DICOM tags; single hook into CI/CD pipelines.

05.0

Extensible Adapters

Domain adapters for pharma, robotics, AV, and industrial vision — same core, new interface.

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Carez AI — FAQs

Carez AI — FAQs

Synthetic imaging, stress testing, and building reliable AI models

FAQs — For the Curious, Skeptical, or Just Speedrunning

Can I actually train on synthetic data?
Yes. Our datasets are built for real-world model development — not academic toy problems. Teams use them for training, validation, and stress-testing pipelines.
Is this Midjourney for medical imaging?
Pretty much. But instead of generating art, we generate structured, labeled, medical-grade datasets. No prompt engineering needed.
What’s the point if I already have data?
Even if you have real data, it's rarely diverse enough. We help you simulate rare edge cases, balance cohorts, and de-risk model performance in the wild.
Will this hold up in a regulatory study?
We’re working with hospitals and imaging labs to make sure it does. Our goal is FDA-aligned pipelines — not marketing hype.
Who’s this built for?
Life sciences AI teams. Imaging companies. R&D orgs that need 10× faster dataset iteration without waiting on IRBs or patient pipelines.
Have a question not covered here? Email the founders.