XDM AI-Powered Synthetic Data

XDM generates realistic, privacy-safe synthetic test data when production data is unavailable or restricted. Create data from defined structures, formats, rules, and relationships to expand test coverage without exposing customer or business records.

Generate synthetic test data for any test execution

Effortlessly create synthetic test data tailored for every stage of your testing lifecycle. Whether for unit, integration, or system tests, XDM delivers reliable, high-volume data generation. Seamlessly scale from single entities to thousands of inter-connected records to mirror real-world complexity.

Generate synthetic test data for any test execution

New capabilities through AI-powered test data generation

With the integration of artificial intelligence, XDM significantly extends its synthetic test data generation capabilities. Leveraging powerful Large Language Models (LLMs), realistic test data can be created not only based on predefined rules but also from natural descriptions and existing data patterns. This allows both common and rare scenarios to be modeled flexibly and precisely.

New capabilities through AI-powered test data generation

Intelligent data generation with minimal effort

Simply define your data structure – for example, for employees, customers, or transactions – and the XDM controlled AI generates realistic, context-aware data records. Custom rules such as value distributions, dependencies, or unique keys can be easily integrated.

Intelligent data generation with minimal effort

Streamline collaboration between business and technical experts

The modular separation of business logic and technical specifications allows seamless collaboration between domain experts and technical architects. This ensures that synthetic data generation aligns with both business requirements and system constraints, enhancing efficiency and accuracy.

Streamline collaboration between business and technical experts

Realistic data without compliance risks

Generate highly realistic, production-like test data without using real individuals or sensitive information. Our approach ensures full compliance with data privacy regulations while maintaining the authenticity of test environments. By leveraging predefined or self-generated lookup tables, you can dynamically shape data based on any predefined base, offering maximum flexibility and accuracy.

Realistic data without compliance risks

Build complex multi-entity scenarios

Generate interconnected data for complex use cases with linked objects and structured dependencies. Our solution maintains data integrity across multiple datasets, ensuring realistic test conditions. Scripts define relationships and dependencies, enabling precise control over scenario complexity.

Build complex multi-entity scenarios

Reusable configurations for advanced scenarios

Maximize efficiency by leveraging preconfigured data generation patterns and seamlessly integrate them into complex test scenarios. Utilize statistical insights from production databases to replicate realistic data distributions, covering common trends and critical edge cases. This approach enhances test coverage while minimizing manual effort.

Reusable configurations for advanced scenarios

Align synthetic data with existing data sets

Integrate synthetic test data into existing test environments and databases. Seamlessly match your data with existing test and production datasets.

Align synthetic data with existing data sets

Flexible data export for various formats

Generate test data in multiple output formats such as CSV, JSON, XML, YAML, or databases. Direct integration with web services and test applications enables seamless workflow integration.

Flexible data export for various formats

XDM Synthetic Data FAQs

XDM Synthetic Data creates new, artificial data for development, testing, training, analytics, and automation. It gives organizations a safe way to supply realistic non-production data without copying live records.

Synthetic data is commonly used for software development, test automation, sandbox environments, training, demos, and edge-case generation. It is also useful when teams need data early in a project before production data exists.

Synthetic data is a strong fit when privacy requirements are high, source data is unavailable, or teams need more control over the scenarios they want to test. It is also useful when organizations want stronger separation from production data.

Yes. Synthetic data makes it easier to create rare conditions, negative scenarios, and larger data volumes on demand. That flexibility helps teams fill gaps that production-based datasets may not cover well.

In many cases, yes. Because synthetic data does not expose actual customer or business records, it can significantly reduce privacy risk in non-production environments.

In some cases it can, but not always. Some workloads can rely entirely on synthetic data, while others still benefit from the realism of protected production-derived data.