DataFramer, a synthetic data power tool.

This listing is for private offers. Reach out to us at info@dataframer.ai  to learn more about our usage and annual licensing options.

DataFramer is a precision synthetic data tool that lets you instantly generate multiple types of realistic datasets while keeping full control over every requirement, distribution, and configuration.

From a small set of seed examples or no samples at all, you can create large, realistic, and privacy-safe synthetic datasets for AI, machine learning, and analytics use cases across Healthcare, Insurance, Finance, and other regulated or data-sensitive domains.

DataFramer generates:

Single-file samples

Documents

Multi-file datasets

Tabular data

Text-to-SQL datasets

other complex structured and unstructured formats (excluding image and audio)

This makes it ideal for LLM evaluation, post-training, text analytics, and traditional ML workflows.

Every dataset is automatically revised and pre-evaluated for highest quality, consistency, and realism, with optional human expert review and labeling available when you need domain-specific accuracy or high-stakes validation.

1. Key Capabilities

Generate multiple types of datasets including documents, multi-file structures, tabular data, and text-to-SQL pairs.

Change data property distributions to match real-world behavior or intentionally rebalance scenarios.

Tune attribute distributions such as demographics, product types, risk levels, or time-based patterns.

Generate rare events and edge cases to strengthen model robustness.

Enforce fairness constraints to reduce bias and improve downstream model performance.

Maintain full control over schema, constraints, and data behavior no black-box generation.

2. Primary Use Cases

Data creation: generating net-new synthetic datasets from no seed samples.

Data augmentation: enriching or transforming existing datasets for ML and LLMs.

Data anonymization: producing privacy-safe replacements for PHI, PII, and sensitive operational data.

Data expansion: scaling limited, sparse, or regulated datasets to production-ready size.

3. Quality & Governance Differentiators

Pre-evaluated synthetic datasets with automated revision, validation, and consistency checks.

Optional human expert reviews and labeling for domain-sensitive or high-risk data.

Fine-grained control over distributions, constraints, and configuration parameters.

Built for teams that require both flexibility and governance across AI, analytics, and Responsible AI workflows.