AI-powered data prep for real-world auditability
Analysis-ready data, proven on a trial the industry had shelved. Harmonize messy clinical and biological data into an auditable foundation, so every downstream analysis runs on firm ground.
of organizations lack or are uncertain about the right practices.¹
Clinical trial records and other valuable data can be trapped in thousands of PDF reports. Multi-site studies that theoretically measure the same endpoints may inadvertently use different instruments, measurement ranges, or lab-derived protocol differences. Genomic datasets could have bias and conflicting annotations, and so much more.
Combining these typical sources of information without proper biological expertise doesn’t produce actionable insights; it just produces data clutter and noise.
The Input
Your team provides data as it is. No pre-cleaning and no standard formatting required.
Clinical trial reports, PDFs, scanned clinical annotations, and hand-written notes
Multi-site studies, clinical performance studies with mismatched endpoints and disparate study size
Multi-omic data sets derived from different technologies and annotation versions
Data records fragmented across disconnected data systems, spreadsheets, and computer drives
The Work
01
Pull raw data out of your existing databases and file it into one place
Fix errors, fill gaps, and remove duplicates so the data is reliable and easy to understand
Put everything into a consistent format across different datasets
04
05
Every connection, mapping, and feature is a logged, validated, scientific judgement call performed by a scientist. Not a guess by a black-boxed system.
Human oversight is embedded at the checkpoints that matter most.
Your scientific team signs off on the decisions that govern every finding before analysis can even begin.
The Output
Cleaned, harmonized data that is traceable and auditable. You understand the reasoning and give approval before implementation on your precious datasets.
The Outcome
A foundation precise enough to trust downstream, instead of every later step in your AI journey struggling to compensate for upstream data problems.
Completed Engagement – Eleison Pharmaceuticals & H. Lee Moffitt Cancer Center
pages extracted, structured, and validated
from fragmented files to analysis-ready data
greater mean survival in Cluster A identified upon re-analysis
Reference 1: GARTNER, 2025. 2. Naleid, N., Gosik, K., Tambe, A., Savkli, C., Beltran, J. F., & Kim, R. D. (2026). Data-driven subtyping and differential glufosfamide benefit in pancreatic adenocarcinoma. Journal of Clinical Oncology, 44(2_suppl), 755. https://doi.org/10.1200/JCO.
