# DataFramer

> DataFramer is an AI-powered synthetic data generation platform that helps enterprise teams generate, anonymize, augment, and simulate realistic datasets for LLM evaluation, model training, and compliance testing.

DataFramer's GAAS platform (Generate, Augment, Anonymize, Simulate) enables data scientists and AI engineers to create privacy-safe, high-quality synthetic datasets at scale — using seed documents or from scratch — without compromising on data diversity or fidelity.

## Key Pages

- [Home](https://dataframer.ai/)
- [AIMon Product](https://dataframer.ai/products/aimon)
- [Research](https://dataframer.ai/research)
- [Blog](https://dataframer.ai/blog)
- [About](https://dataframer.ai/about)
- [DataFramer Docs](https://docs.dataframer.ai/)
- [AIMon Docs](https://docs.aimon.ai/)

## What We Do

- **Generate**: Seed-based and seedless synthetic data generation using leading LLMs (Claude, GPT-4, Gemini)
- **Augment**: Expand and transform existing datasets while preserving statistical distributions
- **Anonymize**: Privacy-safe PII removal and synthetic replacement compliant with HIPAA, GDPR, and SOC 2
- **Simulate**: Edge case generation, rare scenario simulation, and adversarial data creation

## Use Cases

- LLM evaluation dataset creation
- Fine-tuning and RLHF training data
- Healthcare EHR synthetic records
- Insurance benefit verification call datasets
- Text-to-SQL synthetic pairs
- Financial fraud transaction data
- Compliance auditing benchmarks

## Published Research

- [All Required, In Order: Phase-Level Evaluation for AI–Human Dialogue in Healthcare and Beyond](https://proceedings.mlr.press/v317/kulkarni26a.html) — AAAI/PMLR 2026 — Kulkarni, Lyzhov, Chaitanya, Joshi
- [INSURE-Dial: A Phase-Aware Conversational Dataset & Benchmark for Compliance Verification and Phase Detection](https://arxiv.org/abs/2602.18448) — arXiv 2025 — Kulkarni, Lyzhov, Joshi, Chaitanya
- [HalluciNot: Hallucination Detection Through Context and Common Knowledge Verification](https://arxiv.org/html/2504.07069v1) — arXiv 2025 — Paudel, Lyzhov, Joshi, Anand

## Blog Posts

- [How to Generate 50K-Token Documents: Same LLM, Different Results](https://dataframer.ai/posts/long-text-generation-dataframer-vs-baseline)
- [Generation of Synthetic Text2SQL LLM Data with 100% Validity Using Dataframer](https://dataframer.ai/posts/amplifying-claude-haiku-text-to-sql)
- [Generating 1,000 EHR Records with Exact Distributions](https://dataframer.ai/posts/generating-1000-ehr-records-with-exact-distributions)
- [Building a Cyber Insurance Evaluation Dataset in 3 Easy Steps](https://dataframer.ai/posts/building-cyber-insurance-evaluation-dataset-in-3-easy-steps)
- [Generate Fraud Transaction Synthetic Data with DataFramer](https://dataframer.ai/posts/generate-fraud-transaction-synthetic-data-with-dataframer)
- [Generate Synthetic Data with DataFramer MCP Server](https://dataframer.ai/posts/generate-synthetic-data-with-dataframer-mcp-server)

## Company

DataFramer is founded by Puneet Anand (CEO) and a team of researchers and engineers focused on making high-quality synthetic data accessible to every AI team. Headquartered in the United States.