Tonic.ai Solutions for the Public Sector
Turbocharge development and streamline compliance with high-fidelity test data
Rapid access to quality test data is a critical component of today’s CI/CD workflows. Without it, developers face delayed release cycles, bugs in production, and the burden of maintaining ineffective workarounds that are an endless drain on resources—not to mention the risky “workaround” of using sensitive production data in lower environments.
Build better, faster, and safer by hydrating your environments with high-fidelity test data from Tonic, the all-in-one platform for data masking, subsetting, and synthesis built for today’s developers. Through an intuitive UI and extensive database support, Tonic makes it easy to generate realistic test data on demand that looks, acts, and feels like production data because it’s made from production data.
Realistic data to drive productivity
Built-in privacy to simplify compliance
Extensible connectivity to maximize efficiency
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Tonic Structural
- Accelerate development, ensure safety, and improve quality with high-fidelity test data from Tonic, an all-in-one platform designed for data masking, subsetting, and synthesis. Built for modern developers, Tonic offers an intuitive interface and extensive database support, making it easy to generate realistic test data on demand that mirrors production data since it is derived from production data.
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Tonic Ephemeral
- Tonic Ephemeral streamlines the creation of isolated test databases, enabling quicker feature deployment and reduced compute costs. It allows for on-demand or automatic database generation as part of your CI/CD pipeline, facilitating testing, bug reproduction, demos, and other needs.
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Tonic Textual
- Introducing Tonic Textual, the world's first secure data lakehouse optimized for LLMs, which tackles integration and privacy issues prior to RAG ingestion or LLM training. By connecting Textual to any standard cloud object store, it automatically scans a variety of file types, extracts clean text, tags the data, and offers optimal chunking. During this process, Textual can also transform and protect sensitive data in unstructured text into redacted entities or synthetic data that fits the context. The outcome is a secure, scalable data pipeline with rich metadata, ideal for embedding or training LLMs.