Tonic.ai launched a secure data lakehouse for LLMs, Tonic Textual, to enable AI developers to securely leverage unstructured data for retrieval-augmented generation (RAG) systems and large language model (LLM) fine-tuning. Tonic Textual is a data platform designed to eliminate integration and privacy challenges ahead of RAG ingestion or LLM training bottlenecks. Leveraging its expertise in data management and realistic synthesis, Tonic.ai has developed a solution to tame and protect siloed, messy, and complex unstructured data into AI-ready formats ahead of embedding, fine-tuning, or vector database ingestion. With Tonic Textual:
- Build, schedule, and automate unstructured data pipelines that extract and transform data into a standardized format convenient for embedding, ingesting into a vector database, or pre-training and fine-tuning LLMs. Textual supports TXT, PDF, CSV, TIFF, JPG, PNG, JSON, DOCX and XLSX out-of-the-box.
- Detect, classify, and redact sensitive information in unstructured data, and re-seed redactions with synthetic data to maintain the semantic meaning. Textual leverages proprietary named entity recognition (NER) models trained on a diverse data set spanning domains, formats, and contexts to ensure sensitive data is identified and protected.
- Enrich your vector database with document metadata and contextual entity tags to improve retrieval speed and context relevance in RAG systems.
