Every multi-site study starts over: new sites, new schemas, new mapping, new six months. Axiomera turns fragmented research data into cohorts that are standards-bound, confidence-scored, and traceable field by field back to the source value. Inside your own cloud, with raw records never leaving the sites that hold them.
"Does time-to-next-treatment differ by EGFR co-mutation in stage IIIB NSCLC?" Same fact, five spellings. And some of it isn't a field at all.
EGFR L858R+ "exon 21 L858R" unstructured only
Each source resolves into FHIR resource shapes. Observation, Condition, MedicationRequest. Standard format; not yet shared meaning.
FHIR · OMOP-ready
Codes snap on with calibrated confidence. Below threshold routes to a human reviewer, not to production.
SNOMED · LOINC · conf 0.97 0.68 → review lane
One longitudinal timeline. Index date, biomarker date, treatment lines, progression. Multiplied across every site in the study. The sites keep their data.
47 sites still lit
What ships is not a merged record. It's an evidence artifact: cohort N, data dictionary, inclusion/exclusion, per-field lineage, append-only audit ID, reproducible.
0 raw records moved
The statistical plan is rarely the bottleneck. Getting 47 institutions to mean the same thing by the same date is. Most of the effort in a multi-site study is spent before a single analysis runs, and most of it gets thrown away when the next study adds a site.
In oncology, the variables that decide a study. Biomarker status and test date, stage, progression, recurrence. Frequently exist only as unstructured text. That is why manual chart abstraction is still the default, and still the cost ceiling.
December 2025's final guidance on real-world evidence for medical devices lets sponsors submit without participant-level identifiers. It changed the form of the submission, not the standard. Sponsors are still expected to demonstrate data provenance, document every preprocessing step, and address sources of bias with the same rigor applied to trial data.
Lineage is recorded as the data is transformed. It isn't a document written from memory at the end of the study.
Each binding carries a calibrated confidence value and the identity of whoever approved it when it fell below threshold.
An append-only log means last year's cohort can be rebuilt exactly as it was, including the mapping decisions in force at the time.
None of these are analytics problems. They are all the same data problem, which is why one foundation clears all six.
Feasibility across N sites without an N-month ETL project per site. Ships an analysis-ready cohort with a data dictionary and lineage attached.
Assemble a comparator cohort across institutions where the arms live in different systems. Federated by construction, since nothing has to move.
Recover the variables that exist only in unstructured text. Biomarker status and test date, histology, stage, progression. Bound to standard terminologies with confidence scores instead of abstracted chart by chart.
Academic-industry collaborations, disease registries, and cross-border networks where partner sites will collaborate but never transfer rows. A container per site; results move, data doesn't.
Genomics, pathology, imaging, and clinical data joined on the same patient timeline. So a translational hypothesis can be tested against real cohorts, not one study's curated extract.
One harmonized layer reused across safety-signal monitoring, label-expansion evidence, and payer/HTA dossiers. Instead of three curated datasets that disagree.
The research question spanned 47 U.S. healthcare institutions with no central repository and no transfer path. Which is precisely why it had been unrunnable. Axiomera performed semantic binding and harmonization in place, at each institution. Each site computed on its own records, only differentially private gradient histograms crossed the network, and the federated model reached an AUROC of 0.76 for 30-day readmission, an absolute gain of 0.08 over institutional baselines. Published in Informatics in Medicine Unlocked (Elsevier).
Every harmonized value traces to the exact source value in the exact source system.
Quality-scored and human-approved. Entries are added, never edited.
Low-confidence bindings escalate to a person before anything is committed.
Snowflake native app, AWS, GCP, or Azure. With your BAAs unchanged. Snowflake partnership signed; SOC 2 Type II in progress; one USPTO patent with a Notice of Allowance.
Send a study design that died on data access, or a data dictionary from one difficult site. We'll walk your team through exactly how each field gets bound, scored, and traced. And what the cohort looks like on the other side.