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.
Frequently asked questions
Tonic.ai helps teams build and evaluate RAG systems using privacy-safe structured and unstructured data. This allows organizations to connect LLMs to realistic internal knowledge sources without exposing sensitive information.
RAG systems depend on accurate context retrieval. Poor quality or over-redacted data reduces relevance, increases hallucinations, and weakens model confidence. Tonic.ai preserves meaning, structure, and relationships so retrieval results reflect real production behavior.
Tonic.ai supports structured databases, semi-structured records, and free text content such as support tickets, documents, and knowledge bases that are commonly indexed for retrieval.
By generating synthetic data or safely de-identifying text and records, Tonic.ai minimizes the exposure of personally identifiable information (PII) and confidential information while enabling internal data to be used for experimentation and deployment.
Yes. Teams can simulate realistic retrieval scenarios, validate grounding accuracy, and stress-test RAG pipelines using safe datasets that mirror real world complexity.
AI platform teams, data engineering teams, and security conscious enterprises all use Tonic.ai to accelerate RAG adoption while maintaining strong privacy and governance controls.
