MostlyData

As described on AWS Marketplace.

Flux’s Synthetic Data Generator empowers users to create high-quality, customizable datasets that mirror the structure and patterns of real-world data while protecting privacy. Powered by LLM-driven algorithms, it enables organizations to safely test applications, train machine learning models, and conduct data analysis in scenarios where real data cannot be used due to sensitivity or compliance restrictions. It also helps produce more diverse datasets to improve system robustness and innovation. Whether for application development, AI training, or experimentation, this feature ensures data confidentiality while accelerating progress.

AWS Services Used in this solution are: Bedrock, SageMaker, Lambda, RDS, S3, and Comprehend.

Highlighted by the publisher on AWS Marketplace.

- Privacy-Preserving Data Creation: Ensures sensitive information is never exposed by generating synthetic datasets that mimic real-world structures and patterns without using actual customer data. Ideal for regulated industries like healthcare, finance, and insurance.

- Customizable & Diverse Datasets: Allows teams to define parameters and tailor data outputs to match specific testing, training, or analysis needs. Enables generation of edge cases and diverse scenarios to improve model accuracy and application robustness.

AI-Driven Realism: Powered by LLM-based algorithms, the generator produces data that closely resembles real-world behavior, ensuring reliable results for machine learning training, system testing, and analytics—all while maintaining compliance and security.