Synthetic data generation addresses the chronic shortage of labeled training data by creating artificial but realistic samples. Our generation framework supports text, tabular, image, and multimodal data types, each with domain-specific generation strategies.
For text data, the generator uses controlled language models that can produce content matching specified attributes such as topic, style, reading level, and sentiment. Constitutional constraints prevent generation of harmful, biased, or factually incorrect content.
Tabular data synthesis preserves statistical properties of the original dataset including marginal distributions, correlations, and conditional dependencies. Differential privacy guarantees ensure that no individual record from the source data can be reconstructed from the synthetic output.
Quality validation compares synthetic data against holdout real data using distribution divergence metrics, downstream task performance benchmarks, and human evaluation panels. Only batches that pass all three validation stages are approved for training use.