DICOM

·

NIfTI

·

Pathology

·

Ultrasound

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Multimodal

Multi‑site

Clinical‑Grade Data Infrastructure for Medical AI

Trinzz turns raw clinical data into traceable, validated, audit‑ready datasets—so models deploy safely, stay reliable, and pass scrutiny.

Book a Demo Request a Reliability Audit
Reliability architecture
Infrastructure
Clinical Data SourcesDICOM · WSI · OMICS · EHR
↓
Expert Validationreview · consensus · QC
↓
Reliability Scoringagreement · calibration
↓
Certification Artifactstraceability · evidence
↓
Continuous MonitoringPSI · drift · edge cases
Show your stakeholders the evidence

Hospitals and enterprise partners don’t buy “accuracy.” They buy defendable reliability — proof of validation, governance, and monitoring that survives procurement and clinical risk review.

1

Audit your dataset

Quantify reliability risk, traceability gaps, and drift sensitivity — with a structured scorecard.

2

Certify releases

Produce certification artifacts: evidence package, release criteria, and sign-off discipline.

3

Monitor in production

Detect distribution shift (PSI), edge-case exposure, and reliability degradation early.

Request an Audit See audit deliverables →
Reliability is not a claim. It’s a tracked system — with thresholds, evidence, and release discipline.
Outcomes
What stakeholders receive.
Your data becomes a product: measurable, traceable, and ready for deployment conversations.
Evidence Pack
SOPs • reviewer logs • QC reports • release notes
Reliability Scores
QC strength • shift readiness • subgroup risk
Monitoring Plan
PSI thresholds • calibration tracking • edge-case workflow
How it works
From raw hospital exports to certified dataset releases.
A production-grade pipeline for biomedical AI teams who cannot afford silent failures, undocumented edits, or untraceable ground truth.
1) Ingest & Normalize
De‑identify, normalize formats, capture provenance, and standardize metadata across sites and modalities.
Explore ingestion →
2) Annotate with Clinical Controls
Expert-in-the-loop workflows with QA gates, sampling plans, and reviewer accountability.
Explore annotation ops →
3) Certify, Release, Monitor
Versioned releases with evidence packs, reliability scores, and monitoring triggers for drift and edge cases.
Explore reliability →
Modules
Everything required to defend your data in production.
Not a marketplace of annotations. A system of record for clinical-grade datasets.
Ingestion & De‑ID
DICOM/NIfTI/pathology ingest, PHI handling patterns, metadata capture, provenance.
Annotation Ops
Task design, labeling tools, workforce orchestration, SLA controls, sampling.
QC + Consensus Engine
Multi-pass review, disagreement resolution, quantified concordance, escalation pathways.
Governance & Lineage
Dataset versioning, change logs, traceability graphs, audit-ready documentation.
Reliability Monitoring
PSI/drift, calibration tracking, edge-case detection, subgroup risk monitoring.
Dataset Certification
Release criteria, acceptance thresholds, certification reports, reproducible builds.
What you get from Trinzz

Infrastructure-grade deliverables — not consulting slides. Designed to withstand audits, procurement, and production variability.

Reliability Scorecard

Agreement, calibration, stability, subgroup robustness — with release thresholds.

Traceability Map

Lineage from source → label → reviewer → version → release decision.

Certification Report

Audit-ready evidence package: methodology, QC coverage, risks, mitigations.

Monitoring Baselines

PSI & drift triggers, alerts, and documented remediation playbooks.

Built For Reliable Medical AI

Diagnostic AI Companies

Move from pilot accuracy to enterprise deployment with certified dataset reliability.

Enterprise salesProcurementMonitoring
OpenRequest audit

Hospital Networks

Deploy AI without introducing clinical risk — standardize evidence and governance.

GovernanceRiskSign-off
OpenBook review

Pathology / WSI

WSI variability demands consensus, traceability, and drift baselines — by default.

WSIConsensusQC
OpenRequest audit

Oncology Datasets

Cancer AI requires rare-case coverage, subgroup robustness, and governed releases.

Rare casesSubgroupsRegistry
OpenRequest audit

Multiomics & Drug Discovery

Foundation models need traceable multi-modal datasets with reproducibility discipline.

GenomicsReproducibilityScale
OpenBook review

Foundation Model Teams

Build pretraining datasets with governance, stability monitoring, and release discipline.

ScaleDriftGovernance
ContactInfrastructure →
Trusted by Healthcare Enterprise Teams
🔒 SOC2 Type 2
🛡️ ISO 27001
📋 HIPAA Compliant
🌍 GDPR Compliant

Certification reports & BAA available on request

Everything Your AI Team Needs To Ship Compliant Models

Annotate, review, and manage DICOM, NIfTI, WSI, and 3D datasets — built for production-grade medical AI.

Built For Speed

AI-Assisted Labeling

Built For Accuracy

Multi-Step Review

Built For Compliance

Secure And Private

Build For Ease

Native viewers for DICOM, NIfTI, WSI

WHAT YOU CAN EXPECT?

Proven Efficiency Gains For Medical Imaging AI

9x

Faster annotation workflows

67%

Reduction in manual review time

51%

Lower dataset preparation costs

63%

Improvement in label consistency

* based on internal benchmarks

Supported by

Trinzz is built for teams building the future of medical AI
Diagnostic AI
Hospital Networks
Pathology Labs
Oncology Programs
Drug Discovery
Foundation Models

Request a Reliability Audit

Get a clear assessment of dataset risk, traceability gaps, reliability scores, and the fastest path to certification — tailored to your modality and deployment timeline.

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