Maximize model training by securely leveraging your sensitive data
De-identify your sensitive free-text data for use in model training and gain actionable insights to optimize your outcomes, without compromising privacy.
Unlock your data for LLM fine-tuning and general model development
Prevent sensitive data leakage
Automatically detect and de-identify dozens of sensitive entity types in free-text data to keep private information out of your models.
Preserve data realism
Substitute sensitive entities with realistic synthetic data to create a "hidden-in-plain-sight" solution that enhances both privacy and model quality.
Ensure HIPAA compliance
Partner with our expert determination provider to certify HIPAA-compliant data de-identification.
Automated entity-based data synthesis
Replace sensitive data with indistinguishably realistic synthetic values to retain your data’s richness and preserve its statistical properties.
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.
Multilingual Named Entity Recognition (NER)
Automatically identify dozens of sensitive entity types in free-text data with Textual’s proprietary, best-in-class multilingual machine learning models for NER.
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Build performant models on your data without limitations
