Nevyon DataLabs turns pathology images, medical imaging, and clinical data into real-world evidence based predictions — built as production-ready AI, not research prototypes that never leave the lab.
Too much healthcare AI never leaves the lab. Nevyon DataLabs exists to close that gap — building pipelines that are as rigorous about deployment and privacy as they are about model performance, so pharmaceutical and healthcare teams can act on the results with confidence.
GDPR-compliant infrastructure built in from the first design decision, not added before an audit.
Every prediction traces back to the data it came from — nothing is a black box.
Pipelines a client's own team can run and maintain, not a notebook that only works once.
Four areas, from raw clinical data through to a decision a pharmaceutical or healthcare team can act on.
Whole-slide imaging pipelines, digital pathology, and medical imaging (CT, MRI) — data types that need domain-specific handling to be useful for AI.
Deep learning and domain adaptation methods applied to messy, real-world clinical data — not benchmark datasets.
Turning model output into evidence a research or clinical team can stand behind, with a clear line back to the underlying data.
Cloud-based infrastructure that gets a pipeline from a working notebook to something a client's team can run in production.
GDPR-compliant data handling built in from the first design decision, not bolted on before a compliance review.
Getting the unglamorous data plumbing right — the part that determines whether everything downstream actually works.
A straightforward path from a client's raw data to something their team can act on.
Audit what's actually available — pathology slides, imaging, structured records — and what's realistic to build from it.
Develop the pipeline against real, messy clinical data, with privacy safeguards built in from the start, not added later.
Ship production-ready infrastructure the client's own team can run, monitor, and maintain after the engagement ends.
PhD in computational pathology (University of Stavanger), with a background in electrical engineering and around seven years of experience in AI/ML and medical imaging — spanning whole-slide imaging pipelines, histopathology, and clinical imaging workflows.
For pharmaceutical and healthcare organizations working on AI-driven, real-world evidence based research.
