For National Diagnostic Networks
Standardize AI
across sites—without silent failure
Multi-site drift, scanner variance, and inconsistent SOP adherence break models in production. Trinzz provides the governance + monitoring layer that keeps network-wide diagnostic AI stable, auditable, and defensible.
SOC 2 Type II
ISO 27001:2022
HIPAA compliant
Worst‑site reporting
Drift thresholds
1
Baseline every siteStratify by protocol + scanner. Identify worst‑site gaps (not averages).
2
Standardize QC + SOPOne definition of “acceptable” rolled out network‑wide.
3
Monitor and remediateDrift triggers per site bin + routed remediation loopbacks.
Network-scale governance
Trinzz installs release discipline, evidence packs, and drift monitoring so AI performance does not collapse across heterogeneous sites.
Worst‑site reporting
Protocol bins
Change control
What changes with infrastructure
A pilot becomes a program with measurable guardrails.
| Without reliability layer | With Trinzz | |
|---|---|---|
| Site variability | Hidden until failure | ✓ Quantified worst‑site gap + remediation |
| QC + truth | Different SOPs | ✓ Standardized QC + consensus artifacts |
| Dataset releases | Untracked updates | ✓ Versioned releases + change logs |
| Monitoring | Reactive | ✓ Drift thresholds + escalation playbooks |
Bottom line: You cannot scale AI across sites without measuring and controlling distribution shift per site.
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.