Real-world evidence, from your own data, without ever exporting it.
Sovereign by design: hosted on OVHcloud, infrastructure qualified SecNumCloud 3.2 and HDS-certified, your data never leaves. Safety signals, label extensions, comparative effectiveness: Polnor turns your real-world data into evidence inside your own cloud, with a pipeline where almost every step runs itself.
Is a marketed drug associated with an adverse event?
A pharmacovigilance question, answered on real-world data. You define exposed and comparator cohorts, adjust for confounders, and read a risk estimate, without a single row leaving your cloud.
| cohort | n | event rate | HR (adj.) |
|---|---|---|---|
| exposed | 41,802 | 3.1% | 1.28 |
| comparator | 41,802 | 2.4% | ref |
Illustrative interface and figures, shown to explain the workflow. Not results from a Polnor customer.
Everything up to the cohort is automated.
By the time your team writes that query, the hard part is done: the platform has already ingested, de-identified, standardized and quality-checked your data. You start where the question starts.
Your real-world data, at the FHIR standard then OMOP
Claims, EHR extracts and lab feeds are normalized to FHIR R4 then converted to OMOP CDM with OHDSI mapping and quality control. The comparator matching you rely on runs on standardized concepts, not free text.
| source | → OMOP concept | std |
|---|---|---|
| atc:X | ingredient X | S |
| icd10:I26 | Pulmonary embolism | S |
| loinc:718-7 | Hemoglobin | S |
The platform does the plumbing. Your team does the science.
The repetitive, compliance-heavy work runs itself, so your epidemiologists spend their time on judgment, not preparation.
Evidence you can defend, with a trail behind it.
Every access to patient data is logged, PHI is classified, and export follows EHDS-aligned manifests. Because data never leaves your SecNumCloud/HDS cloud and we never train our models on it, the provenance of your evidence is unambiguous.
From raw RWD to a defensible estimate, in your cloud.
30 minutes: we run the pipeline on a case like yours and show you where the automation ends and the science begins.