Healthcare data de-identification for software testing and AI model training
Eliminate PHI in developer environments and hydrate your workflows with realistic synthetic healthcare data to accelerate innovation while safeguarding patient privacy.
HIPAA compliance and developer productivity, delivered
Automated data refresh
Automate data pipelines to de-identify and synthesize electronic health records on demand, so you can keep staging up to date with every commit and provide your RAG system with a steady flow of realistic data.
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Guaranteed compliance
Eliminate risk in lower environments and unblock offshore teams by leveraging Safe Harbor data de-identification techniques or validating data synthesis via Expert Determination.
Accelerated innovation
Enable efficient shift-left testing and fine-tune models more effectively with high-fidelity synthetic healthcare data that captures all edge cases, to shorten your release cycles and deploy products faster.
Realistic data de-identification and synthesis
Work across healthcare data sources to apply secure data masking and synthesis techniques that maintain relationships within PHI, whether it’s structured, semi-structured, or free-text data.
Unstructured data extraction and standardization
Extract data from messy, complex formats, such as PDFs of clinical notes, into a standard format convenient for model training. Support for TXT, DOCX, PDF, CSV, XLSX, TIFF, XML, PNG, JPEG, JSON, and more.
Expert Determination for HIPAA
Partner with our expert determination provider to certify HIPAA-compliant data de-identification.
Optimized performance for PB-scale
Eliminate lags in data provisioning with a platform specifically architected to support large data volumes, whether in cloud databases or unstructured data stores.
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Make healthcare data usable for software and AI development.
