Achieve data privacy and AI optimization

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.

In AI model training

Retain your data’s richness and preserve its statistics by replacing PII with synthetic values, to ensure optimal model training for LLM fine-tuning and custom models.

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.

In RAG systems

Provide LLMs redacted data while optionally exposing the unredacted text to approved users. Automate pipelines to extract and normalize unstructured data into AI-ready formats.

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.

In LLM workflows

Redact sensitive information prior to using it within LLM prompts to prevent sensitive values from ever entering the chatbot 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.

In your lower environments

Accelerate data science based development with realistic test data that ensures data utility and data privacy throughout your lower environments

See Textual protect your data in real-time

Our proprietary NER models automatically identify entities in your text data to prevent potential privacy vulnerabilities in your AI development. Textual can de-identify any sensitive entities it detects via redaction or LLM synthesis.

Industry-leading sensitive data detection, redaction, and synthesis

1

Input

Connect Textual to your data store or upload files in any format via an intuitive UI or by feeding text directly into the Textual SDK.

2

Extract

Automatically extract your free-text data and detect over thirty sensitive entity types with Textual’s multilingual NER models.

3

Protect

Leverage granular controls to de-identify your data consistently, either through redaction or realistic synthesis, replacing sensitive values while maintaining semantic integrity.

Optionally certify that PHI data de-identification is HIPAA-compliant through our partnership with an expert determination provider.

4

Deliver

Output your protected data in its original file format or in a standardized, markdown format optimized for model training and RAG systems. 

Image Support for all your data formats

Support for all your data formats

90% of enterprise intelligence is locked up in files across the business. With Textual, you can unlock unstructured enterprise data however and wherever it’s stored:
.csv
.txt
XML
.pdf
HTML
JSON
.pptx
.docx
.png
.jpeg
.xls
+ more

Accessible where your data lives

Deploy Textual seamlessly into your own cloud environment through native integrations with cloud object stores, including S3, GCS, and Azure Blob Storage, or leverage our cloud-hosted service.

Available through your cloud provider

Burn down your cloud commitments by procuring Textual via the Snowflake Marketplace, AWS Marketplace, and Google Cloud Marketplace.

Featured

Resources

Learn more about Tonic Textual by way of technical deep dives, guide, and webinars.
See all
Quickly building training datasets for NLP applications
Data privacy in AI
Data anonymization vs data masking: is there a difference?
Data de-identification
Static vs dynamic data masking
Data de-identification
De-identifying your unstructured data in Databricks with Tonic Textual
Tonic Textual how-tos
Data anonymization: a guide for developers
Data de-identification

Secure your sensitive free-text data with Tonic Textual.

Leverage the power of generative AI while safeguarding your most important data.
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.