Bugs slip through when your test data doesn’t reflect reality. If your data is incomplete, unrealistic, or outdated, your QA is failing before it even starts.
Testing with bad data is worse than not testing at all. When your test data doesn’t mirror production, edge cases go unnoticed, releases slow down, and teams scramble to fix critical bugs after launch.
The best teams are eliminating bad data with Test Data Management (TDM)—automated, production-like, and compliant test data that lets you find failures before they happen.
Your test data told you everything was fine. Your users found out it wasn’t.
Bugs that should’ve been caught earlier pull engineers off roadmap and kill momentum.
QA grinds to a halt when you fail to get the data needed. More delays. More chaos.
Every quarter deadlines get missed and the roadmap slips.
Bad test data slows sprints, hides bugs, and derails releases. But when your test data is as powerful as your production data, your team moves faster, ships with confidence, and builds better products.
If test data is broken, your tests are broken—and so is your entire development process. Great engineering teams don’t “workaround” bad data. They fix it at the source.
You wouldn’t manually configure every CI/CD pipeline—so why are you manually managing test data? TDM automates test data generation, so it’s always production-like, compliant, and ready when you need it.
Flaky tests, surprise bugs, and last-minute scrambles? Those aren’t inevitable. With realistic test data, QA cycles are tighter, releases are smoother, and your team ships confidently every time.
Tonic.ai automates test data generation, so your team can test smarter, move faster, and release with confidence—without compliance headaches.
Generate realistic, diverse datasets that mirror production environments, so your tests catch failures early.
Automatic PII detection, masking, and synthesis ensure GDPR, HIPAA, and PCI compliance—so you can test freely, without risk.
Seamless integration with databases, tools, and cloud platforms ensures every environment has the right test data, always.
Automated subsetting, masking, and synthetic data generation let devs focus on shipping, not wrangling datasets.