A protocol your reviewers can follow
Define the label, edge cases, reviewer qualifications, and acceptance criteria before the first study is assigned.
Clinical data operations for medical AI
Expert-grade annotation for medical AI
Turn a focused clinical labeling question into a defensible dataset—with the protocol, expert review, QA evidence, and provenance your ML team needs.
- Start narrow
- One clinical question
- Review deeply
- Expert + QA evidence
- Scale deliberately
- Controls with demand
MGD
Needs reviewAnnotation workspace
Study CT-0204 · review in progress
AXIAL · 2.5 mmSlice 61 / 142
Model proposal · v1.0
Review checklist
✓ Label protocol applied
✓ Ontology mapped
○ Expert sign-off
Provenance
Model suggestion → expert correction → QA decision → versioned delivery
Decision
Ready for QA
The operating model
A better system for the work behind medical AI
The useful asset is not a quick label. It is a dataset whose clinical meaning, review path, and permitted use remain clear long after delivery.
AI-Assisted Annotation
Start with a model-assisted proposal, then keep the human reviewer in control of every decision and correction.
Expert Review & QA
Build annotation protocols, reviewer calibration, adjudication, and acceptance criteria around the clinical question—not raw throughput.
Security & Compliance
A governed data workflow begins with de-identification, access boundaries, documented provenance, and a clear readiness path.
Your Models, Your Data
Keep model outputs, expert corrections, ontology choices, and quality evidence connected to the dataset they came from.
Quality by design
How a defensible dataset takes shape
Every correction improves the evidence trail—not just the next model proposal.
AI pre-labels
Models generate initial annotations
Experts review
Clinicians correct, not label from scratch
QA validates
Blind review against gold standards
Dataset delivered
Versioned, audit-ready, yours
Models improve
Corrections fine-tune the next round
Built for the real work
Make your data program an asset—not a black box.
The strongest teams establish the clinical definition and evidence model first, then let tools accelerate the workflow.
Evidence your ML team can reuse
Keep annotation history, QA decisions, and data-use limits with every delivered dataset.
A practical enterprise path
Begin with a focused workflow, then add controls only when your data, customers, and deployment require them.
Governance from day one
Build confidence into the data, not just the interface
Enterprise readiness is earned through implemented controls, evidence, and repeatable operating practice—not a badge on a website.
- Designed for de-identified medical AI data workflows
- Clinical quality, not speed alone, defines delivery
- Provenance, data-use policy, and ontology are part of the dataset
- Run it as SaaS, in your cloud, or on-premises — data stays in your environment
- A staged path from focused pilot to enterprise deployment
Start with the clinical question that matters most.
Tell us the modality, labeling objective, reviewer expertise, and delivery evidence your team needs — we'll come back with a plan.