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Tonic Structural is a platform that generates synthetic data and transforms sensitive structured data into safe, de-identified, and realistic datasets. It supports a variety of databases and integrates seamlessly with enterprise-scale systems, enabling teams to generate production-like data for use in testing and development without exposing sensitive information. By leveraging advanced masking techniques, data synthesis, subsetting, and referential integrity preservation, Tonic Structural ensures that your team has realistic test data that behaves just like the original. This allows organizations to work with secure, compliant data in non-production environments, speeding up workflows and improving product quality.

Tonic Textual is a platform for de-identifying and synthesizing sensitive information found in unstructured data. It uses advanced Natural Language Processing (NLP) techniques, including proprietary Named Entity Recognition (NER) models, to identify and protect sensitive information, like personally identifiable information (PII) or protected health information (PHI), while maintaining the data's readability and utility. By replacing sensitive details with realistic but non-identifiable alternatives, Tonic Textual allows organizations to safely use unstructured text data for model training, AI development, and LLM implementation. This ensures privacy compliance with regulations like GDPR and HIPAA without compromising the usefulness of the data for AI innovation.

Tonic Ephemeral is a platform for creating on-demand, temporary data environments which are automatically destroyed after use. By rapidly spinning up data in isolated environments, Tonic Ephemeral enables teams to test and develop efficiently without the overhead of managing persistent data environments. This approach reduces resource usage, streamlines workflows, and ensures data privacy by integrating with Tonic’s de-identification and synthesis tools. Tonic Ephemeral is ideal for supporting CI/CD pipelines, improving test efficiency, and maintaining compliance with data privacy regulations like GDPR and HIPAA.

Tonic Validate is an open-source framework for rigorously evaluating your RAG system, providing metrics and visualizations to monitor the performance of each component in your RAG system during development and in production. Validate offers collaboration and compliance features so that technical and non-technical teams can work together to develop production-ready, enterprise RAG systems.

Data de-identification is the process of removing or altering personally identifiable information (PII) or other sensitive data to protect individual privacy. The goal is to transform the data so that individuals cannot be readily identified, while still retaining the data’s utility for tasks like analysis, software testing, AI development, or research. Techniques for data de-identification include masking, generalization, encryption, and data synthesis. Proper de-identification ensures compliance with privacy regulations like GDPR and HIPAA, enabling organizations to use and share data safely without exposing sensitive information.

Synthetic data is artificially generated data that mimics the structure, patterns, and relationships of real-world data, without containing any actual sensitive information. It is often used as test or training data in software development, machine learning, and analytics to validate systems, train models, and simulate real-world scenarios. When generated effectively, synthetic data maintains the utility of production data while ensuring privacy and compliance with regulations. As test data, synthetic data allows teams to work in secure, non-production environments without risking exposure of personally identifiable information (PII) or other sensitive content. By preserving the statistical properties and relationships of real data, it provides a realistic, safe, and compliant alternative for development and testing workflows.