README.md

---
tags:
- synthetic-data
- data-generation
- data-anonymization
- simulation
- llm-evaluation
- fine-tuning
- testing
- privacy
- enterprise-ai
- regulated-industries
pretty_name: DataFramer
license: other
---

# 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**