From the DataFramer team
Guides, benchmarks, and insights on AI evaluation, synthetic data, and LLM reliability.
Why DataFramer
Puneet Anand Synthesizing Fraud Transaction Scenarios Before Production
Create realistic, labeled fraud datasets on demand with Dataframer. Test, benchmark, and train before attacks reach production.
Gabriel Marrocos Generate Synthetic Data with the Dataframer MCP Server
See how the Dataframer MCP server lets you generate diverse synthetic data directly from your AI coding assistant.
Alex Lyzhov Why Test Data Management Is The Hidden Bottleneck in AI Development
Synthetic data is reshaping how engineering and AI teams think about test data, and DataFramer sits at the center of that shift. Learn how synthetic-first TDM solves privacy, scale, edge case, and cost challenges.
Puneet Anand How to Generate 50K-Token Documents: Same LLM, Different Results
TL;DR We compared Dataframer vs raw Claude Sonnet 4.5 for long-form text; Dataframer overwhelmingly won on diversity, style fidelity, length, and quality.
Alex Lyzhov Generation of Synthetic Text2SQL LLM data with 100% validity using Dataframer
TL;DR: How we used Dataframer to generate diverse and complex text-to-SQL samples using only Claude Haiku and how you can do the same for LLM evaluation and training with minimal effort.
Alex Lyzhov Building a Cyber Insurance Evaluation Dataset in 3 Easy Steps with DataFramer.
Learn how to scale a few real cyber insurance samples into a complete evaluation and training dataset using a three step workflow that controls distributions and quality checks.
Puneet Anand How to Generate Multi-file EHR Datasets for 1000 patients with exact distributions
Generate privacy-safe synthetic EHR/EMR datasets from a few patient samples. See how DataFramer turns limited EHR data into rich medical and insurance datasets in 5 steps with the exact required distributions.
Puneet Anand The Essential Guide to Synthetic Data
This guide explains what synthetic data is, how it's generated, and why it matters across industries like finance, healthcare, insurance, and technology. It covers benefits, case studies, generation techniques, vendor comparisons, anonymization, and real-world case studies showing synthetic data in action.
Puneet Anand The Full Data foundation Behind HDM-2's Hallucination Detection Success Over GPT-4o: From Training Data, Evals, Validation, to Benchmark datasets
DataFramer, born out of AIMon Labs, built the full data foundation behind AIMon's' HDM-2 — training data, evaluation sets, validation pipelines, and HDM-Bench — powering an open-source hallucination detection model that beat GPT-4o and GPT-4o-mini across every major benchmark.
DataFramer Team