Discover Curiosity's AI-powered platform for redefining outer loop software delivery and productivity
The all-in-one platform for enterprise test data needs. Understand complex data, remove sensitive information, and generate rich data for your entire organisation.
Remove non-production privacy risks
Provide data on demand to every tool and team
Create every combination needed to assure quality
Generating and masking data avoids the spread of PII to lower environments, mitigating the risk of costly data breaches and regulatory fines.
Providing on demand data to your tools and teams saves the delivery time currently wasted finding, making and waiting for complex test data.
Finding and making data for every test, user story and business requirement detects defects early and builds quality throughout your delivery.
Delivering version-controlled data to every tool, team and environment removes clashes and constraints, ensuring seamless agility.
Automatically identify and anonymise sensitive data before it’s provisioned to less-secure environments. Avoid data breaches and ensure regulatory compliance.
Eliminate privacy and legislative risks
Block PII from reaching lower environments
Leave no excuse for using production data
Generate fictitious data to cover every user story, test case and business requirement, available on demand to your entire delivery ecosystem.
Cover every scenario needed for quality
Ensure continuous data availability
Replace sensitive data in lower environments
Stop wasting delivery time finding, making and waiting for test data. Provision data using self-service forms, seamless integrations, and an automated test data toolkit.
Slash provisioning time and costs
Maximise productivity and agility
Boost quality and avoid failures
Across industries, some of the world’s largest enterprises use Curiosity's platform to deliver rich and compliant data on demand.
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
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
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
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
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
Quality Modeller 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
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
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
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
Read a free resource, or talk to an expert, to learn how you can embed productivity and quality throughout your software delivery ecosystem.
Our platform integrates with your people, processes and tools, with data connectors covering databases, mainframes, files, messages and more.
View all integrations
Rich, compliant data that is available across the entire enterprise
