Sixpack

Enterprise synthetic test data solutions. In order to generate test data that accurately reflects the structure of your application or database, it must be easy to model and maintain each test data project as changes to the data model occur throughout the lifecycle of the application. Maintain referential integrity of parent/child/sibling relationships across the data domains within an application database or across multiple databases used by multiple applications. Ensure the consistency and integrity of synthetic data attributes across applications, data sources and targets. For example, a customer name must always match the same customer ID across multiple transactions simulated by real-time synthetic data generation. Customers want to quickly and accurately create their data model as a test data project. GenRocket offers 10 methods for data model setup. XTS, DDL, Scratchpad, Presets, XSD, CSV, YAML, JSON, Spark Schema, Salesforce.

As physical customer interactions shift into digital, we can no longer rely on real-life conversations. Customers express their intents, share their needs through data. Understanding customers and testing our assumptions about them also happens through data. And privacy regulations such as GDPR and CCPA make a deep understanding even harder. The MOSTLY AI synthetic data platform bridges this ever-growing gap in customer understanding. A reliable, high-quality synthetic data generator can serve businesses in various use cases. Providing privacy-safe data alternatives is just the beginning of the story. In terms of versatility, MOSTLY AI's synthetic data platform goes further than any other synthetic data generator. MOSTLY AI's versatility and use case flexibility make it a must-have AI tool and a game-changing solution for software development and testing. From AI training to explainability, bias mitigation and governance to realistic test data with subsetting, referential integrity.