Trinzz — Diagnostics Data Infrastructure

Oncology Data Infrastructure

Oncology AI Requires Structured Intelligence — Not Just Data

Cancer is heterogeneous across patients, within tumors, across time, and across modalities. Most AI systems collapse this complexity into averaged performance metrics.

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SOC 2 Type II ISO 27001:2022 HIPAA aligned Release gates + evidence packs Monitoring triggers

We convert fragmented oncology signals into:

Trinzz builds structured oncology intelligence layers designed for foundation models, translational AI, and regulatory-grade deployment.

Harmonized multimodal corpora Version-controlled releases Subgroup-stratified performance systems Longitudinally indexed intelligence layers

CG‑DRSDeployment readiness score: quality + traceability + drift readiness.

ConsensusAgreement metrics + adjudication — defensible truth.

QCError taxonomy + pass rates per cohort.

DriftTriggers per protocol/site bin + escalation.

The Oncology Heterogeneity Failure

Most oncology AI fails in deployment because: .

Rare mutation cohorts are underrepresented

Tumor microenvironment signals are ignored

Longitudinal progression is flattened

Performance metrics are averaged across subtypes

Worst-cohort performance is unmeasured

Governance Checklist

Change control, QC, consensus, lineage, drift triggers.

Reliability is measurable

We turn trust into numbers—so decisions become objective.

Signal	Definition	How it prevents failure
Consensus	Silent performance collapse in minority cohorts	Overfitting to dominant molecular subtypes
QC	Subgroup stratification frameworks	Worst-cohort performance quantification
Drift	Longitudinal treatment-line indexing	Progression-aware dataset modeling
Release gate	Measurable reliability tracking across releases

Multimodal Oncology Intelligence Layer

Our infrastructure integrates:

Signal	Definition	How it prevents failure
Consensus	Whole Slide Imaging (WSI) pathology	Radiology (CT, MRI, PET)
QC	Genomic mutation panels	Bulk and single-cell transcriptomics
Drift	Proteomic pathway signatures	Longitudinal treatment outcomes
Release gate	ML-ready feature stores	Cross-modal embedding spaces

Agentic Governance Architecture

Oncology data evolves continuously. Our agentic system deploys specialized agents to ensure integrity, validation, and release safety across the data lifecycle.

Ingestion agents Continuously acquire structured and unstructured oncology datasets.

Ontology alignment agents Map clinical vocabularies to standardized ontologies.

Schema validation agents Enforce structural and relational data integrity.

Subgroup drift detection agents Detect statistical and clinical cohort deviations.

Release gate agents Control dataset promotion through governance checkpoints.

Monitoring + escalation agents Trigger alerts, reviews, and remediation workflows.

Longitudinal Intelligence Layer

Cancer progression is temporal. We structurally preserve time as a governed data dimension rather than collapsing it.

We structurally preserve

Treatment line sequencing Ordered therapy exposure across care pathways.

Recurrence mapping Indexed relapse intervals and disease return states.

Progression event indexing Time stamped clinical progression markers.

Survival vector modeling Structured survival trajectories per cohort.

This enables

Time aware survival models Temporal risk stratification and survival prediction.

Treatment optimization AI Sequence aware therapy decision systems.

Combination therapy simulation Modeled response across regimen permutations.

Real world evidence modeling Outcome analytics across longitudinal cohorts.

Trinzz builds the oncology intelligence substrate beneath precision medicine. We partner with frontier AI labs and translational oncology groups that require infrastructure — not annotation.

Get a Reliability Readout (CG‑DRS) in days Send a sample + manifest. We return a risk register, validation blueprint, and monitoring triggers your team can operationalize immediately.

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