Curiosity's customers deliver the standard in test data management

Manually finding and making data adds up to one of the most resource-intensive, time-consuming tasks in testing and development. Automating provisioning can make the difference when striving to hit release deadlines and stay on budget.

Senior Test Data Engineer

AI is making system data more complex than ever. Developing AI-driven, AI-built systems requires diverse data that reflects intricate relationships, trends and hierarchies. Model-based data generation is perfect for overcoming this complexity.

Principal Test Data Engineer

Using generative AI to visualise complex requirements provides the clarity developers need to build quality systems. The same models then generate optimal tests, testing continuously and paying off technical debt in requirements, tests and code.

Chief Technology Officer

Testers and developers can spend 20-50% of their time on data-related activities. Accurately and automatically provisioning test data is one of the biggest and fastest wins for enterprises seeking to deliver software faster and with better quality.

Test Data Engineering Lead

Curiosity’s models ensured the reliability of complex connections between user types, so payments stay on target and private data remains secure … They identified minor and major bugs, so we could bring our product to market quickly—and guarantee our users a simple, worry-free experience.

John McElroy

President of Eljin Productions

Payment platforms and complex banking systems are perfect for model-based data generation. We can break their intricate logic down into intuitive visual models, applying algorithms that create the smallest message set needed to cover diverse combinations.

Program Manager, ISO 20022

Visual models break our system down in reusable chunks that generate the functional and visual tests we need. We can further assemble these reusable building blocks automatically as new courses are checked in, generating rigorous tests.

Greg Sypolt

VP of Quality Engineering

Curiosity’s platform enabled rigorous in-sprint testing, while facilitating cutting-edge development practices like shift left API testing, fail-fast experimentation, and test-driven API design. It worked seamlessly alongside our teams and processes.

Johnny Pitt

Founder of ThinkDonate

Importing requirements to visual models not only generates rigorous test automation at speed; it also improves the requirements and pays off technical debt. This transparency and shared vision allows critical thinking upfront, while building quality throughout the delivery lifecycle.

Product Owner

Miscommunications, silos, and a lack of transparency create bottlenecks throughout software delivery. AI can now diagram complex requirements and generate tests. This not only accelerates delivery and pays off technical debt; it also provides a collaborative vision that aligns every team.

VP of Application Delivery