Trinzz
Trinzz provides clinical-grade data infrastructure for medical AI teams, transforming raw hospital exports into certified, audit-ready datasets with traceability, reliability scores, and production monitoring. The platform manages ingestion, de-identification, expert annotation with QA gates, and versioned certification across DICOM, NIfTI, pathology, and ultrasound modalities. It includes native viewers and AI-assisted labeling, with internal benchmarks showing 9x faster annotation workflows and 63% improvement in label consistency.
- Healthcare Technology
- Artificial Intelligence
- Regulatory & Compliance Technology
- Software Only
- Data & Analytics
- Digital Health
- Enterprise Software
Funding
Funding not disclosed
Founders
Founder details are not available yet.
Product
Problem
Biomedical AI models often fail in production due to unreliable, untraceable, and poorly governed training data standards, exposing teams to silent errors, undocumented edits, and untraceable ground truth. Hospitals and enterprise partners demand proof of validation, governance, and monitoring that meets procurement and clinical risk review standards, which typical annotation marketplaces do not provide.
Solution
Trinzz provides a clinical-grade data infrastructure platform that transforms raw hospital exports into certified, audit-ready dataset releases. The platform manages the full pipeline—ingesting and normalizing data, expert-in-the-loop annotation with QA gates, and versioned certification with reliability scores and monitoring alerts. It produces evidence packs, reliability scorecards, and traceability maps that withstand audits and procurement scrutiny, helping teams move from pilot accuracy to enterprise deployment. The platform includes native viewers for DICOM, NIfTI, whole-slide imaging, and volumetric data and supports multimodal, multi-site workflows with de-identification, provenance capture, and metadata standardization.
Target Audience
Primary customers are diagnostic AI startups, hospital networks, enterprise labs, pathology and oncology programs, foundation model teams, and multiomics and drug discovery groups that require defensible, certified dataset reliability for production deployment.
Features
- Ingestion & de-identification for DICOM, NIfTI, pathology, ultrasound, and multimodal data with provenance capture and metadata standardization
- AI-assisted labeling and multi-step QC consensus engine with disagreement resolution, sampling plans, and reviewer accountability
- Governance and lineage tracking with dataset versioning, change logs, traceability graphs, and audit-ready documentation
- Reliability monitoring using PSI drift detection, calibration tracking, and edge-case subgroup risk alerts
- Dataset certification with release criteria, acceptance thresholds, and reproducible certification reports
- Native viewers for DICOM, NIfTI, whole-slide imaging, and volumetric data integrated into annotation workflows
- SOC2 Type 2, ISO 27001, HIPAA, and GDPR compliant infrastructure with BAA available on request