System of RecordAudit TrailsVersioned ReleasesExpert Consensus
Trinzz Platform
A dataset operating system for biomedical AI: ingestion → annotation → QC → governance → monitoring → release.
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Overview
One platform for building defensible datasets.
Trinzz is designed for environments where “good enough” data becomes clinical risk. Every action produces evidence: who changed what, why it changed, and what quality gates it passed.
Traceability graph
Provenance → transformations → labels → reviews → releases, captured end-to-end.
Quality gates
Sampling plans, QC rules, acceptance thresholds, and release criteria.
Evidence packs
Exportable artifacts for audits, clinical validation, and customer due diligence.
Module
Ingestion & De‑Identification
Normalize multimodal data from multiple sites while preserving clinical context and enforcing privacy controls. Capture metadata and provenance so every asset can be traced back to its origin and transformation steps.
CapabilityWhat it enables
Format normalization (DICOM / NIfTI / WSI / US)Unified pipelines across modalities and vendors
De‑ID workflows + PHI handling patternsPrivacy-safe operations with documented controls
Provenance captureLineage from source export to final dataset
Metadata standardizationSearchable cohorts, consistent training/eval splits
Module
Annotation Operations
Define tasks, route work to appropriate expertise, and enforce review policies. Every annotation is paired with who labeled it, what guidelines were used, and what QC it passed.
Task design
Protocols, label schemas, edge-case definitions, and training sets for labelers.
Workforce orchestration
Credentialed experts, tiered review, SLAs, and throughput controls.
Guideline enforcement
Built-in checks, required fields, and structured feedback loops.
Module
QC & Clinical Consensus
Replace subjective “looks good” reviews with measurable quality. Track disagreement, resolve disputes, and quantify concordance across reviewers.
Multi-pass review pipelines with escalation and dispute resolution
Reviewer concordance metrics (e.g., κ), acceptance rates, and error taxonomies
Targeted rework queues and guideline updates based on failure patterns
Module
Governance & Dataset Lineage
Treat datasets as regulated products: version, sign, reproduce, and defend. Maintain audit trails that connect every label to its evidence.
Versioned releases
Immutable releases with change logs and reproducible builds.
Access controls
Role-based permissions and least-privilege operating models.
Exportable documentation
SOPs, policies, reviewer logs, and evidence for audits.
Module
Monitoring & Drift Readiness
Production reliability depends on detecting distribution shift early and having a defined intervention workflow. Trinzz provides measurable triggers and operational playbooks.
PSI / drift triggers
Compare expected vs actual distributions by bins, set thresholds, and alert.
Calibration tracking
Monitor confidence reliability over time and across cohorts.
Edge case monitoring
Capture “unknown unknowns” through structured sampling and review.
Want a platform walkthrough with your dataset?
We’ll map your current pipeline, identify reliability risks, and show what “audit-ready” looks like in practice.
Book a demo Request a Reliability Audit
FAQ
Common questions from clinical and enterprise buyers.
Do you provide annotators or only software?

Both. Trinzz can run expert-in-the-loop programs and/or integrate with your existing workforce while enforcing QC and traceability.

How do you make quality measurable?

We define acceptance thresholds, sampling plans, and concordance metrics (e.g., κ) and export reports per release.

Can you support multi-site, multi-device deployments?

Yes. We standardize metadata, track distribution shift, and define monitoring triggers by site/device/cohort.