DataFramer

Generate, anonymize, and simulate reality-grounded, diverse datasets from your own data for testing, evals, and fine-tuning ML/AI models.

DataFramer helps AI teams take their own data further — creating realistic, privacy-safe datasets for testing, evaluation, and post-training without exposing sensitive production records.

DataFramer works from your data, adding diversity while preserving the structure, distributions, and constraints your models depend on.

Why teams use DataFramer

AI teams often get blocked because:

• their seed data isn’t enough
  Generate diverse, scaled datasets without starting from scratch.

• their real data is off-limits
  Anonymize sensitive records while keeping structure intact.

• their data doesn’t cover what models will face in production
  Simulate edge cases, rare scenarios, and real-world variation missing from existing samples.

How it works

DataFramer supports a seed-based workflow for enterprise AI data readiness:

1. Seed input from manual samples or production data
2. Anonymize sensitive records when needed
3. Analyze schema, structure, distributions, and patterns
4. Configure variation, volume, edge cases, and format mix
5. Generate realistic datasets across complex formats
6. Use the outputs for model evaluation, testing, and fine-tuning

Built for real enterprise data

DataFramer works with any textual dataset — any format, any domain, any complexity, including:

• long-form documents and PDFs
• structured and semi-structured records
• nested and hierarchical data
• multi-file workflows
• high-variability business inputs

Best-fit use cases

• LLM and AI evaluations
  Build stronger eval datasets with better coverage across common, rare, and edge-case scenarios.

• Privacy-safe testing
  Use realistic datasets for testing and iteration without exposing sensitive production data.

• Anonymization for AI workflows
  Transform restricted real-world data into safe seed inputs for downstream generation and evaluation.

• Fine-tuning and dataset expansion
  Extend sparse datasets with more realistic variation while preserving fidelity to source patterns.

Enterprise-ready

Built for teams in regulated and data-sensitive environments.
Your data never has to leave.

Learn more at https://www.dataframer.ai