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Expert medical data for AI

Provable clinical reliability for AI.

Verikris connects frontier AI labs and life-sciences teams to a worldwide network of English-trained, licensed physicians — and ships every dataset with a measured reliability score. Independent. Auditable. Clinical-grade.

Reliability readout: physician reads converging on injected ground truth with an illustrative consensus reliability figure
Reliability readoutillustrative

Built to the bar regulated buyers demand

  • Double- and triple-blind consensus
  • Ground-truth calibration
  • HIPAA (aligned)
  • SOC 2 (in progress)
  • ISO 27001 (in progress)
  • ISO 13485 (roadmap)
  • GxP (roadmap)
The reliability engine

Reliability isn't asserted. It's measured — on every case.

The same four-stage pipeline runs under every deliverable, whether we evaluate your model's clinical outputs or build a dataset from the ground up.

Pipeline: protocol, blind reads, calibration, and reliability score
  1. Stage 02

    Independent blind reads.

    Multiple credentialed physicians assess each case blind to one another — removing the single-annotator bias that generalist crowds bake in.

  2. Stage 03

    Ground-truth calibration.

    Verified gold-standard cases are injected continuously to score each physician's reliability on the exact task at hand.

  3. Stage 04

    The reliability score.

    Each deliverable ships with quantified agreement and error metrics — a number your team — and your regulators — can audit.

Who we serve

Two buyers. One standard of proof.

Illustrative: model output graded by physicians with a rubric agreement score
For AI labs

Clinical evaluation & expert data at the frontier.

Expert evals, RLHF, and clinical-reasoning data from licensed physicians — delivered fast, scored for reliability, from a partner independent of any model builder.

  • Physician-graded model outputs with measured agreement
  • Neutral by design — no ownership by any competing lab
  • Scalable without trading rigor for throughput
Records flowing through an audited de-identification, annotation, and validation chain
For life sciences

Clinical data your compliance team can stand behind.

Annotation and evaluation for real-world evidence, pharmacovigilance, and clinical records — with the provenance, audit trail, and de-identification regulated work requires.

  • Audit-ready provenance and reliability metrics
  • De-identification by Safe Harbor or Expert Determination
  • Data kept in-region — reviewed, never exported
Why Verikris

The proof layer the frontier is missing.

Abstract globe with verified physician nodes connected worldwide
Worldwide network · English-trained, licensed physicians
  1. 01

    Provable, not asserted.

    Blind consensus and ground-truth calibration turn expert judgment into a number you can audit — the metric incumbents don't publish.

  2. 02

    A physician network built for scale.

    A managed worldwide network of English-trained, licensed physicians — clinical depth at a fraction of incumbent cost.

  3. 03

    Neutral by design.

    Independent of every model builder. Your unreleased data never passes through a competitor's pipeline.

Trust

The auditable trust layer, not a badge in the footer.

Regulated buyers ask one question first: Can I prove this data is sound? Verikris is built to answer it — reliability methodology, data residency, and compliance posture documented and open to audit.

Trust & Compliance →
  • Reliability scoring documented and reproducible
  • Physician equivalence validated by sampling
  • Data reviewed in secure enclaves — no PHI exported
  • BAA-ready · no training on client PHI
Founders

People who have built and validated data at scale — in regulated markets.

Not brokers who found a trend. A team that has run licensed platforms, safeguarded patient data across borders, and built the machinery that makes expert data trustworthy.

About the team →
Get started

See what provable reliability looks like on your data.

Bring a clinical evaluation or a dataset you can't yet trust. We'll show you the reliability score — and how we arrive at it.