Insights on the problems that block AI teams from shipping

Why AI Projects Stall Between Prototype and Production

The demo worked. Now leadership wants confidence, coverage, and usable data before it ships to production.

Tue Apr 07 Read post

Problem Pillars

Reality-Grounded Synthetic Data Generation: Why Random Values Break Enterprise AI

Random values can fill a table. They usually cannot preserve how the table behaves.

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LLM and Agentic Evaluation: Why Eval Dataset Coverage Matters More Than Size

The issue is rarely too few eval rows. It is eval data that misses the real spread of cases, slices, and failure modes.

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Synthetic Data for Test Data Management: Solving AI Development's Hidden Bottleneck

Test data scarcity is the hidden bottleneck blocking AI teams from shipping.

Applied Examples ▸ Eval teams

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Applied Examples

How this actually gets solved

Benchmarking Coding Agents as Math Auditors: A Synthetic Financial Document Dataset

We built a financial benchmark with planted errors to test Claude Code as a math auditor — no manual labeling needed.

Applied Examples ▸ Trading / drift

Thu Mar 19 Read

Synthesizing Fraud Transaction Scenarios Before Production

Generate labeled fraud scenarios on demand so you can train and benchmark before attacks reach production.

Gabriel Marrocos

Thu Feb 19 Read

Applied Examples

Long-Form Synthetic Data Generation: Same LLM, Dramatically Different Results

Same LLM, dramatically different results. DataFramer vs raw Claude on 50K-token document generation.

Applied Examples ▸ Agentic analytics

Mon Jan 12 Read

Synthetic Text-to-SQL Data Generation with 100% SQL Validity Using Claude Haiku

How we generated 500 diverse, 100% valid text-to-SQL samples for LLM evaluation and fine-tuning using only Claude Haiku.

Applied Examples ▸ Insurance data platforms

Fri Dec 19 Read

Building a Cyber Insurance Evaluation Dataset in 3 Easy Steps with DataFramer.

Scale a few real cyber insurance samples into a full evaluation and training dataset in three steps.

Applied Examples ▸ Healthcare claims

Wed Oct 15 Read

How to Generate Multi-file EHR Datasets for 1000 patients with exact distributions

Turn two patient samples into 1,000 privacy-safe EHR records with exactly controlled distributions in five steps.

Applied Examples ▸ AIMon Labs

Wed Oct 15 Read

How a 3B Model Outperformed GPT-4o on Hallucination Detection: The Training, Evals, Validation, and Benchmark Synthetic Data Pipeline Behind HDM-2

A 3B open-source model beat GPT-4o at hallucination detection, built entirely on DataFramer-generated training and eval data.

Tue Apr 15 Read

Resources

Checklists, guides, and reference material

Resource

Golden Datasets, Eval Data, and Fine-Tuning Sets: Built by DataFramer

Raw LLMs can't generate multi-file records, edge cases, or labeled data at the fidelity AI teams need.

Thu Mar 26 Read

Resource

Generate Synthetic Data with the DataFramer MCP Server

Generate diverse synthetic data directly from your AI coding assistant using the DataFramer MCP server.

Tue Feb 17 Read

Resource

A Practical Guide to Evals, Testing, and Fine-Tuning with Synthetic Data

Why AI teams keep running into the same data problems, and what it actually takes to get past them.