Keep sensitive data secure within your RAG system

De-identify sensitive free-text data for your RAG system to harness the power of RAG while protecting privacy.

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1000
+
Data engineering hours saved
35
+
Detected PII entity types
Dozens
Supported sources and file formats

Build and deploy privacy-first RAG systems

Bring an end to critical bugs in production and accelerate your release cycles by fueling your staging and QA environments with data that mirrors the complexity of production.

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 RAG system.

Bring an end to critical bugs in production and accelerate your release cycles by fueling your staging and QA environments with data that mirrors the complexity of production.

Accelerate RAG development

Extract complex, messy data from PDFs, images, CSVs, and more into a standardized, easy-to-develop-with markdown format.

Bring an end to critical bugs in production and accelerate your release cycles by fueling your staging and QA environments with data that mirrors the complexity of production.

Control data access

With reversible tokens, your RAG system can display the original text to users while ensuring the LLM processes only the redacted data.

Detect, extract, and redact sensitive entity types in unstructured data to continuously refresh your RAG system while ensuring data privacy

Contextual data redaction with tokenization

Substitute sensitive information with reversible or non-reversible tokens to maintain data consistency across your dataset.

Unstructured data extraction and standardization

Extract data from messy, complex formats, such as PDFs of clinical notes, into a standard format convenient for RAG ingestion. Support for TXT, DOCX, PDF, CSV, XLSX, TIFF, XML, PNG, JPEG, JSON, and more.

Automated data refresh

Automatically update your RAG system with new and modified files each time the pipeline runs to keep your application current.

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

Resources
Learn more about unstructured data de-identification with Tonic.ai’s in-depth technical guides and blog articles.
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CCPA: Understanding how synthetic data can help achieve compliance

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Data is the new code: the evolution of software development

Tonic.ai editorial

Tonic.ai product updates: June 2025

Product updates
Accelerate development with high-quality, privacy-respecting synthetic test data from Tonic.ai.Boost development speed and maintain data privacy with Tonic.ai's synthetic data solutions, ensuring secure and efficient test environments.