Question: I need a solution that helps create realistic datasets for AI model testing and evaluation, while protecting sensitive data.

Gretel Navigator is an AI-powered system for generating, editing and amplifying tabular data. It can be used in two modes: Create for generating plausible data from scratch, and Edit for modifying and amplifying existing data with SQL or natural language prompts. It can be used in a variety of ways, including creating evaluation datasets, data augmentation and personalizing product demos. It's well suited for training foundation models and fine-tuning large language models, so it's a good option for AI model testing.

Another option is Tonic. Tonic is designed to accelerate engineering velocity by generating realistic, secure test data that matches production complexity while protecting data privacy. It can be integrated with a variety of data sources and has flexible pricing. Among its features are data transformation, unblocking local development and ensuring data freshness across environments. That makes Tonic a good option for engineering teams that want to build better and faster while protecting privacy.

For those who want a self-service option, MOSTLY AI offers a synthetic data generation platform based on GenAI. It can generate anonymous synthetic data to ensure privacy compliance and supports high-accuracy data for AI/ML use cases. The platform is designed for enterprise customers with easy integration and certifications for security and compliance. It's good for data sharing, AI/ML development and testing & QA.

Last, Snaplet is a tool that generates realistic, production-like seed data for relational databases. It can help developers save time and improve accuracy with instant seed data generation and production-like data transformation. Snaplet also offers advanced features like data anonymization and subsets, making it good for local development and CI/CD workflows. This tool is good for coding locally, end-to-end testing and debugging.