Gretel Navigator Tabular generates production-quality synthetic data optimized for AI and machine learning development from prompts, schema definitions, or seed examples. Unlike single-LLM approaches to data generation, Navigator employs a compound AI architecture specifically engineered for synthetic data, combining top open-source SLM models fine-tuned across 10+ industry domains. This purpose-built system creates diverse, domain-specific datasets at scales of hundreds to millions of examples while preserving complex statistical relationships and offering increased speed and accuracy compared to manual data creation.

Top use cases:

• Creating synthetic data for LLM training and fine-tuning

• Generating evaluation datasets for AI models and RAG systems

• Augmenting limited training data with diverse synthetic samples

• Creating realistic PII/PHI data for model testing