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.
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.
Redact sensitive information prior to using it within LLM prompts to prevent sensitive values from ever entering the chatbot system.
Accelerate data science based development with realistic test data that ensures data utility and data privacy throughout your lower environments
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.
Automatically extract your free-text data and detect over thirty sensitive entity types with Textual’s multilingual NER models.
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.
Output your protected data in its original file format or in a standardized, markdown format optimized for model training and RAG systems.
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.
Burn down your cloud commitments by procuring Textual via the Snowflake Marketplace, AWS Marketplace, and Google Cloud Marketplace.
For the utmost in data security and control, deploy Textual on premises using Kubernetes or Docker, in the event that your data is too sensitive to live on the cloud.