Gretel AI

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Training specialized agentic systems requires extensive, high-quality datasets that are often scarce, siloed, or sensitive. Synthetic data eliminates this bottleneck by creating diverse datasets at scale for any domain to accelerate AI agent development. Synthetic data can help solve challenges such as: “By 2026, 75% of businesses will use GenAI to create synthetic customer data, up from less than 5% in 2023.” Generative AI can be used to create data for high-quality conversations, capturing domain-specific language, intent variations, and rare edge cases, overcoming the limitations of scarce real-world transcripts. By enriching training data with tailored dialogues, it improves conversational AI accuracy, adaptability, and the ability to handle nuanced, multi-turn interactions. Targeted evaluation and benchmark datasets, such as domain-specific question-answer pairs, can be used to measure and enhance retrieval-augmented generation (RAG) system performance. Side-by-side comparison of multiple models on the same use case ensures consistent, fair evaluation and informed model selection. Low-resource domains like proprietary coding languages or underrepresented languages benefit greatly from realistic, complex synthetic text data—enhancing AI models’ reasoning, accuracy, and overall performance. NeMo Safe Synthesizer creates privacy-safe versions of sensitive data with default configurations designed to meet data privacy regulations such as HIPAA and GDPR, providing seamless access to synthetic medical data without regulatory or privacy constraints—enabling vast knowledge sharing both internally and externally. Design high-fidelity synthetic document datasets for large-scale AI model training in tax form validation, legal documents, mortgage approvals, and other structured data applications.