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Tonic.ai is a synthetic data platform that gives engineering and AI teams on-demand access to safe, realistic data for software development, testing, and AI model training. Instead of waiting on slow data provisioning cycles or risking sensitive data exposure, teams use the Tonic platform to generate, de-identify, and subset both structured and unstructured data at scale.

The platform includes three products: Tonic Fabricate for generating synthetic data from scratch or from existing databases, Tonic Structural for transforming sensitive production data into safe, high-fidelity test data, and Tonic Textual for redacting and synthesizing unstructured data and free-text. Each includes a built-in AI agent that replaces hours of manual configuration with minutes of chat and automated workflows.

Most organizations know they shouldn't use production data in development, testing, or training environments, but many still do because the alternatives are too slow or too unrealistic. Tonic.ai gives teams a way out.

Tonic Structural connects to your production databases, applies automated data masking and de-identification, and provisions high-fidelity test data that preserves schema structure, referential integrity, and business logic. Its patented subsetting engine creates targeted, isolated datasets so teams can work without collisions. When production data isn't available or appropriate, Tonic Fabricate generates logically consistent synthetic data from scratch or modeled on an existing database's structure and distributions. For unstructured data, Tonic Textual detects and redacts sensitive information in free-text, documents, and files.

Yes. Tonic.ai supports AI development across two dimensions: generating net-new training data and safely unlocking the sensitive data your organization already has.

Tonic Fabricate generates realistic structured datasets, unstructured text, and mock APIs on demand, with control over schema, complexity, coverage, and ground truth. Connect an existing database and let Fabricate model its patterns, or build entirely from scratch. Use it for populating reinforcement learning environments, building evaluation datasets, generating labeled data for fine-tuning, and addressing cold-start scenarios where no real-world dataset exists.

Tonic Textual detects, redacts, and synthesizes sensitive information within your existing unstructured data so AI teams can use it for model training and RAG systems without privacy risk. Together, they give AI teams what they need without dependence on scarce, sensitive, or expensive real-world data.

Tonic.ai is built for regulated environments. Healthcare and financial services organizations use the Tonic platform to maintain HIPAA, PCI, GDPR, and SOC 2 compliance across development and AI workflows without bottlenecking engineering teams.

For healthcare, Tonic Structural and Tonic Textual de-identify protected health information (PHI) in both structured databases and unstructured clinical text, with optional HIPAA Expert Determination to validate Safe Harbor compliance. For financial services, Structural masks PCI-regulated fields and Fabricate generates compliant synthetic data that mirrors the complexity of production, so development and testing proceed without exposing real customer records.

Each product addresses a different data challenge, and for many organizations, they work together:

Tonic Fabricate generates synthetic data from scratch or modeled on an existing database. Use it for new product development, AI model training, demo environments, load testing, mock APIs, or any scenario where production data doesn't exist or isn't suitable.

Tonic Structural transforms existing production data into safe, high-fidelity test data. Use it when testing and QA depend on data that mirrors production's complexity, with sensitive fields masked and referential integrity preserved.

Tonic Textual redacts and synthesizes sensitive information in unstructured data: free-text, documents, PDFs, and files. Use it when AI or compliance initiatives involve text-based data containing PII, PHI, or proprietary information.

Testing can't keep up with development when data is a ticket. Engineers wait on cross-team requests, fight over shared staging environments, and waste time scripting mock data or troubleshooting broken test suites caused by stale, unrealistic data.

Tonic Structural gives teams self-service access to high-fidelity test data with automated masking and subsetting, and schema changes sync automatically. Tonic Fabricate lets teams generate data for new features, parallel development, and load testing, without waiting on anyone. Both products include built-in AI agents that turn hours of manual configuration into a guided, conversational workflow, resulting in faster release cycles, fewer escaped defects, and engineers freed from low-value data sourcing.

Yes. Organizations frequently adopt the Tonic platform to modernize away from legacy TDM tools like Informatica TDM, Delphix, or IBM Optim, as well as homegrown scripts that don't scale.

Legacy solutions tend to be slow to refresh, brittle when schemas change, and expensive to maintain. Tonic Structural offers a modern alternative: AI-powered configuration, native connectors, automated masking that preserves referential integrity, and a patented subsetter for targeted provisioning. And where legacy tools focus narrowly on structured database masking, Tonic.ai extends coverage to unstructured data (via Textual) and net-new data generation (via Fabricate), including model training data, mock APIs, and demo environments.