How the platform works
Our platform turns raw clinical records into analysis-ready datasets at scale. Each abstracted value stays connected to its source record, timestamp, supporting evidence, and rationale. Disease-agnostic by design, Savant has been used to deepen insights into more than 30 distinct therapeutic areas.
A new paradigm
clinical context → usable evidence- challenge_01
Study-defining variables remain buried in notes, pathology, and radiology.
with_savantVariables are structured to your project guidelines and delivered in your schema.
- challenge_02
Repeated, copied, and conflicting mentions fragment the patient journey.
with_savantClinical events resolve into a longitudinal view, with each value connected to its source evidence.
- challenge_03
Manual abstraction constrains cohort size, turnaround, and repeatability.
with_savantAutomated and expert-reviewed curation supports work from focused cohorts to millions of records.
A structured, analysis-ready dataset built around the questions your team needs to answer — with the provenance and workflow to support review, collaboration, and iteration.
Capabilities
what the pipeline can doDisease-Agnostic
There’s no new model to train for each disease. The same platform has produced datasets across 30+ therapeutic areas, from rare hematology to women’s health.
Grounded Reasoning & Citations
Every value ships with its verbatim evidence and a pointer to the source document. 100% of extracted values are source-traceable.
Dynamic Data Refresh
When new records arrive or your data model evolves, the dataset re-runs. Study questions change; your pipeline shouldn't stall.
Longitudinal Interpretation
Scattered mentions become coherent patient timelines across diagnosis, therapy lines, response, and progression. Each event is dated and de-duplicated.
Robust QC
Integrated automated and expert human QC validates outputs against the underlying source evidence.
Error Classification
An independent evaluation pass classifies each disagreement by type, including missed evidence, wrong date, and over-extraction, so fixes target causes rather than symptoms.
Integrated quality control
quality controlQuality isn't a sampling afterthought. Automated and expert human QC is integrated throughout the workflow, validating extracted values against source citations and expert manual abstraction. Every result remains traceable to its source, within a quality framework mapped to FDA guidance and designed to stand up to regulatory scrutiny.
We never train on customer data, and every engagement runs in a dedicated environment. HIPAA-compliant; SOC 2 Type 2 (Security, Confidentiality & Privacy).
Sample-cohort iteration
Before any full-scale run, we validate the data model on a sample cohort with your clinical reviewers, refining definitions until they’re clinically sound.
Automated quality checks
Automated evaluation checks outputs systematically across the cohort, surfacing patterns that require attention before they scale.
Manual validation
Expert abstractors validate outputs against the underlying source and citation trail.
What you get
deliverablesTimeline: new projects stand up in 4–6 weeks.
Want to see a full delivery — schema, joins, QC report and all? We'll walk through one on a demo →
| variable | value | date | source |
|---|---|---|---|
| therapy_start | ruxolitinib | 2022-11 | plan section |
| spleen_size | 18.2 cm | 2024-03-04 | imaging report |
| hemoglobin | 9.8 g/dL | 2024-03-11 | lab report |
| biomarker_status | JAK2 V617F positive | 2024-03-11 | history section |
Walk through the pipeline on your use case.
Bring a variable list — or just a research question — and we'll show you how the data model, extraction, and QC would run against it.