Financial services data de-identification for software testing and AI model training
Accelerate development and unblock innovation by powering your lower environments and AI workflows with synthetic finance data that mirrors production complexity.
Optimize finance data generation to maximize developer productivity
Rapid data refresh
De-identify and generate realistic financial data in a fraction of the time required by legacy tools to rapidly refresh your lower environments and fuel AI initiatives with high-fidelity, compliant datasets.
Accelerated development
Enable efficient shift-left testing with fully representative synthetic financial data that captures all edge cases across cloud and mainframe data sources without introducing complexity into your workflows.
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Guaranteed compliance
Apply regulatory-specific data transformations to de-identify PII throughout structured and unstructured data, ensuring data privacy compliance with PCI and global regulations, and eliminating risk in development and AI workflows.
Consistent data de-identification across databases
Work across data sources to apply granular data masking and synthesis techniques that ensure input-to-output consistency and preserve referential integrity within your structured, semi-structured, and unstructured data.
Patented database subsetting
Shrink petabytes of data down to targeted gigabyte datasets to minimize your data footprint without breaking referential integrity for streamlined testing, efficient debugging, and reduced risk of data leaks.
Optimized performance for petabyte scale
Eliminate lags in data provisioning with data generation solutions specifically architected to support large data volumes, so that data access is never a bottleneck to efficient development.
Built-in data governance tools for visibility and security
Standardize rules for data generation to enforce security policies, and access privacy reports and audit trails with customizable RBAC to monitor and validate adherence to privacy compliance throughout your data pipelines.
Cross-platform integrations with enterprise systems
Support for all the leading data sources, from the cloud to mainframe to files, and a fully accessible API enable seamless integration with your databases, CI/CD workflows, and automated test suites.
Frequently asked questions
Tonic.ai enables banks, insurers, and fintech companies to safely use realistic financial data for development, testing, analytics, and AI initiatives without exposing sensitive customer or transaction data.
Financial data often contains personally identifiable information (PII), payment details, and other regulated information that cannot be broadly shared. These restrictions can hinder innovation and slow teams due to the lack of accessible data. Tonic.ai removes these barriers by providing privacy-safe data that behaves like production data.
Tonic.ai is commonly used for application development, QA and testing, model training, and AI initiatives such as RAG systems and LLM powered workflows.
By minimizing reliance on real customer data in non production environments, Tonic.ai reduces breach exposure, audit scope, and compliance risk while maintaining data fidelity.
Yes. Tonic.ai preserves relationships, constraints, and distributions across complex schemas such as accounts, transactions, policies, and claims, ensuring systems behave as expected.
Platform teams, quality engineering leaders, data teams, innovation teams, and compliance stakeholders use Tonic.ai to accelerate product delivery while maintaining strong governance and privacy controls.
Make sensitive financial data usable for software and AI development.
