De-identify sensitive free-text data during chatbot interactions in real time to safeguard data from LLM consumption.
Automatically detect and de-identify dozens of sensitive entity types in free-text data to keep private information out of your chatbot interactions.
Safely leverage user inputs by detecting and removing Personally Identifiable Information (PII) or Protected Health Information (PHI) in real time.
With reversible tokens, the chatbot can display the original text to users while ensuring the LLM processes only the redacted data.
Replace sensitive data with reversible tokens to maintain consistency between chatbot prompts and the underlying RAG system for optimal RAG retrieval.
Automatically identify dozens of sensitive entity types in free-text data with Textual’s proprietary, best-in-class multilingual machine learning models for NER.
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