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High-quality data is essential to strong machine learning performance, for both training and rigorous evaluation purposes. However, organizations have traditionally relied on manual data labeling, brittle scripts, or limited real-world datasets. These approaches are slow to scale, costly to maintain, and difficult to adapt as requirements evolve. And as models grow more capable and use cases more specialized, gaps in both training data and evaluation coverage quickly become major bottlenecks. Oumi addresses this challenge by automating synthetic data generation throughout the model development workflow. Given a clear task definition, Oumi generates a structured data recipe and then produces the corresponding data at scale. This enables rapid iteration without sacrificing control, allowing you to systematically vary complexity, style, and structure while maintaining consistency, coverage, and quality.

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What you can synthesize

Oumi incorporates data synthesis as an iterative, repeatable part of your machine learning workflow. You can rapidly prototype datasets, expand small or imbalanced data, and evolve training data alongside your models. Some examples of what you can build with Oumi’s data synthesis include:
  • Question-answer datasets for training chatbots
  • Instruction-following datasets with varied complexity levels
  • Domain-specific training data (legal, medical, technical)
  • Conversation datasets with different personas or styles
  • Data augmentation to expand existing small datasets
With Oumi’s data synthesis capabilities, teams can rapidly prototype, iterate, and scale training datasets while maintaining control over structure, diversity, and quality. By shifting data creation from manual effort to rule-driven generation, you can accelerate model development and unlock use cases that would otherwise be limited by data availability.

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What’s next

How It Works

Learn how Oumi data synthesis works.

Recipes

Find out what goes inside a data synthesis recipe.