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.
1000+
Data engineering hours saved
35+
Detected PII entity types
15+
Supported sources and file formats
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.
The all-in-one platform for unstructured data extraction and 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.
The Tonic.ai product suite
Tonic Fabricate
AI-powered synthetic data from scratch and mock APIs
Tonic Structural
Modern test data management with high-fidelity data de-identification
Tonic Textual
Unstructured data redaction and synthesis for AI model training
"Tonic removed a major blocker for us by enabling our teams with data that mirrors the size, shape, and feel of our production data. And by guaranteeing privacy for HIPAA compliance, Tonic allows us to share that data safely with our off-shore development teams, too."
Nemo Nemeth
Head of Data Products
Let's chat.
Leverage the full potential of your unstructured data in AI development. Connect with our team to learn more today.
Resources
Learn more about unstructured data de-identification with Tonic.ai’s in-depth technical guides and blog articles.
Clinical data extraction: how to unlock critical health information
Tonic for the Enterprise
Managing test data from multiple sources without losing consistency
Test Data Management
Synthetic data for agentic workflows: A guide
Data synthesis
Named Entity Recognition for data compliance automation
Data privacy in AI
Your attack surface is your data. Mythos is the proof.
Data privacy
Benchmarking OpenAI's Privacy Filter: What it gets right, and where PII detection still needs real data
Technical deep dive
From off-limits to AI-Ready: Preparing unstructured data directly in Microsoft Fabric with Tonic Textual
Product updates
How redaction software can help government agencies comply with FOIA
Data de-identification
Frequently asked questions
How does Tonic.ai support AI and ML model training?
Tonic.ai enables teams to train, test, and validate machine learning models using privacy-safe data that reflects real world patterns without exposing sensitive information.
Why is production data sometimes problematic for model training?
Production datasets often contain regulated or proprietary information that cannot be freely shared with data science teams or external partners. This creates delays, limits experimentation, and increases compliance risk during model development.
Can Tonic.ai be used for repeated training and evaluation cycles?
Yes. Teams can use Tonic.ai to generate consistent or varied datasets on demand, making it easier to compare model performance, run experiments, and iterate without reintroducing privacy risk.
How does Tonic.ai help improve model accuracy?
By preserving statistical properties and real world data behavior, Tonic.ai allows models to learn from representative data scenarios rather than oversimplified or overly sanitized datasets.
How does Tonic.ai support governance and responsible AI initiatives?
Using synthetic and de-identified data helps organizations reduce exposure to sensitive information while supporting internal governance, audit requirements, and responsible AI practices.
Build performant models on your data without limitations
Make your sensitive data usable for LLM fine-tuning and custom AI model training today.