Build and deploy privacy-first RAG systems
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.
Accelerate RAG development
Extract complex, messy data from PDFs, images, CSVs, and more into a standardized, easy-to-develop-with markdown format.
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.
Optimize your RAG system without data limitations
