De-identify sensitive data for AI/ML initiatives at scale while maintaining HIPAA compliance.
Upload unstructured datasets—such as clinical notes, call transcripts, or patient reports—into Tonic Textual for processing.
Tonic Textual uses advanced NLP to identify PHI and other sensitive data, supporting both redaction and transformation workflows to meet HIPAA de-identification standards.
Once redaction is complete, export your dataset for third-party evaluation. Tonic ensures that output formatting and metadata align with certification requirements.
A trusted, independent Expert Determination partner assesses the dataset to certify that re-identification risk is “very small,” as defined under HIPAA.
Upon approval, your dataset is officially certified as de-identified under the Expert Determination standard—enabling safe, compliant downstream use.
Tonic.ai generates, validates, and delivers complete, high-fidelity datasets, engineered for your model or system.
With streamlined workflows and expert guidance, Tonic Textual users achieve HIPAA certification faster and without sacrificing accuracy, security, or audit readiness.
When building and training AI and ML models, seamlessly replace PII with synthetic alternatives — preserving essential context within the data while remaining compliant.
Follow a proven path and work with trusted partners to certify data and establish pressure-tested compliance.
Tonic’ai’s platform was designed with the expert determination process in mind; with de-identification methods that are aligned with HIPAA standards and ensure that all data that passes through our tools is compliance-ready.
When expert determination is a requirement, Tonic.ai provides a direct path through a single contract. With access to Tonic.ai solutions, expert guidance, and direct access to our certification partner, you get everything you need to achieve HIPAA compliance through one seamless engagement.
We work directly with leading expert determination vendors—with a proven track record of success—to ensure a seamless, secure, and reviewer-aligned certification process.
AI-powered synthetic data from scratch and mock APIs
Modern test data management with high-fidelity data de-identification
Unstructured data redaction and synthesis for AI model training