ABOUT TRINZZ
Trinzz builds the data backbone behind regulated medical AI, transforming complex imaging into deployment-ready training systems with measurable reliability, structured expert validation, and audit-grade governance.
Medical AI has reached an inflection point. Models can segment tumors, classify lesions, and detect anomalies with impressive performance. Yet hospitals hesitate to deploy.
The bottleneck is no longer model capability. It is trust.
Trinzz exists to redefine what "data quality" means in healthcare. Not volume. Not labeling throughput. But measurable reliability, structured disagreement modeling, calibration integrity, drift monitoring, and audit-ready traceability.
We build data systems designed for deployment, not just demonstration.
AI models show strong benchmark performance in controlled environments.
Clinical environments require reproducibility, auditability, and risk controls.
Reliable medical AI demands measurable expert agreement, lifecycle monitoring, and governance discipline.
Medical AI fails not because of intelligence, but because of infrastructure. Trinzz builds the infrastructure layer that transforms experimental models into production-ready systems.
Trinzz specializes in complex biomedical data environments across multimodal workflows. We design datasets that meet the expectations of hospitals, regulators, and frontier AI teams.
We are not a generic annotation vendor.
We design reliability frameworks for regulated medical AI.
At Trinzz, a dataset qualifies as deployment-ready only if it satisfies explicit reliability thresholds.
Download our comprehensive guide to clinical-grade data standards
These criteria transform "data quality" from a marketing claim into a measurable governance standard aligned with global regulatory expectations.
with Structured Confidence Capture
Calculating κ, ICC, and Subgroup Stability
(ECE)
with Version Control
with Drift Detection and PSI Thresholds
Reliability is not assumed. It is continuously measured, logged, and governed across the data lifecycle.
Trinzz is designed to operate within regulated healthcare environments.
Our systems support secure workflows, encrypted data handling, role-based access controls, and audit-aligned documentation practices.
We build with enterprise security and compliance alignment in mind, enabling healthcare AI teams to move confidently from pilot to production.
Founder & CEO, Trinzz
Trina Das builds the clinical-grade data backbone powering frontier AI in precision medicine and diagnostics. As Founder and CEO of Trinzz, she focuses on one defining challenge in healthcare AI: transforming fragmented, multimodal medical data into measurable, auditable, and deployment-ready systems through expert governance, quantified quality controls, subgroup robustness validation, calibration monitoring, and lifecycle drift management.
Prior to founding Trinzz, Trina designed and scaled large distributed technology platforms and global expert networks, developing rigorous methodologies for human–AI collaboration, quality standardization, and incentive-aligned reliability at scale. These principles now underpin Trinzz's deployment-readiness framework.
Her leadership has been recognized internationally, including Forbes 30 Under 30 Asia and acknowledgment at the White House by President Barack Obama as one of the most impactful emerging leaders under 25.
At Trinzz, her mission is clear: to establish measurable standards that make medical AI trustworthy, reproducible, and ready for institutional and regulatory scrutiny.
Partner with Trinzz to transform complex biomedical data into clinical-grade infrastructure.