Redacting sensitive text data in JSON with Tonic Textual
Tonic Textual is designed to help clients handle unpredictable free-text data found in things like chat logs and sensitive documents. We've extended its capabilities to JSON, allowing you to use contextual analysis to identify sensitive values in JSON properties, and then apply redactions to them ... Read More
Tonic Validate is now on GitHub Marketplace! (Part 2)
Tonic Validate is a free, open-source library for evaluating RAG and LLM based applications. We recently announced a new listing on GitHub Marketplace that provides a GitHub Actions template to run Tonic Validate against code changes on every commit. Today, we’re following up with an additional listing that allows you ... Read More
Tonic Validate is now available on GitHub Marketplace!
Tonic Validate, our free, open-source library for evaluating RAG and LLM-based applications, can be run entirely as a GitHub Action. And it's now available for quick deployment on GitHub Marketplace! ... Read More
Tonic.ai Achieves HIPAA Compliance Certification, Ensuring Enhanced Security for Protected Health Information
We are proud to announce that we have successfully completed our HIPAA certification, marking a significant milestone in our commitment to data security and privacy. This achievement underscores our dedication to providing secure data environments for our clients, particularly those in the healthcare industry handling protected health information (PHI) ... Read More
Deep dive: small vs large language models for token classification
Check out this talk from Ander Steele, Head of AI at Tonic.ai, which was first presented at ODSC East in Boston, and explores the evolving landscape of named entity recognition (NER); specifically comparing the performance of small language models versus large language models for token classification tasks ... Read More
Demo: fine-tuning LLMs with Tonic Textual
In this blog post, Tonic.ai’s Head of AI, Ander Steele, walks through a live demo of how Tonic Textual can be used to automatically de-identify protected health information (PHI) within unstructured data—making it safe and compliant for fine-tuning large language models (LLMs) ... Read More
Evaluating open-source tools for data masking
Can you use open-source tools to mask sensitive production data for use in testing and development? We explore the available options and weigh the pros and cons of relying on DIY data masking solutions ... Read More
How to prevent data leakage in your AI applications with Tonic Textual and Snowpark Container Services
Tonic Textual provides advanced Named Entity Recognition (NER) and synthetic replacement of sensitive free-text data. Today, we are excited to announce that Tonic Textual is now available on the Snowflake Data Platform via Snowpark Container Services (SPCS). SPCS enables you to run containerized workloads directly within Snowflake, ensuring that your ... Read More
De-Identifying Your Text Data in Snowflake Using Tonic Textual
Discover how Tonic Textual revolutionizes data privacy in Snowflake. Learn to create and implement a UDF for secure, compliant free-text data use in our latest article ... Read More
Using custom models in Tonic Textual to redact sensitive values in free-text files
Learn how Tonic Textual uses trained models to identify the sensitive values in your free-text files, and how you can create your own custom models to use in addition to Textual's collection of built-in models ... Read More
