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Dataframer is a platform for generating high-quality synthetic datasets at scale. Give it example data or describe what you need, and it generates thousands of new samples that match your target patterns, distributions, and structure — across documents, spreadsheets, multi-file packages, and more.

Quickstart

Generate your first dataset in 5 minutes

Core Concepts

Understand how Dataframer works

Complete Guide

Step-by-step walkthrough of all features

API & MCP

Programmatic access via Python SDK or MCP

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How it works

Dataframer uses a three-stage pipeline: 1. Upload Seed Data (optional) Upload sample data that represents what you want to generate. A sample is the unit that gets imitated - in CSV/JSONL it’s a row, in multiple files it’s a file, in multiple folders it’s a folder. Can be text documents, code files, SQL queries, or multi-file structures. 2. Create Specifications AI analyzes your seeds to create a specification - an editable blueprint capturing data structure, properties, distributions, and patterns. 3. Run Generation Generate thousands of new samples based on your specification. Configure quality settings, validation, and model selection.

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Use cases

  • LLM evaluation & benchmarking: Generate diverse test datasets to evaluate and stress-test AI models
  • Training data: Create balanced, labeled datasets for model training and fine-tuning
  • Fraud detection: Synthesize rare fraud scenarios for pre-production testing of detection systems
  • Insurance & healthcare: Generate multi-file application packages, EHR datasets, and claims data
  • Privacy & compliance: Produce synthetic datasets that preserve statistical properties without exposing PII
  • Testing & QA: Create realistic test sets, edge cases, and adversarial scenarios

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Key features

  • Long-form & complex documents: Generate documents up to 50K+ tokens with consistent structure, style, and formatting
  • Multi-format support: CSV, JSON, JSONL, PDF, DOCX, text files, and multi-file/multi-folder structures
  • Seeded or seedless: Learn from example data, or generate from a natural language description alone
  • Distribution control: Define and enforce probability distributions, property dependencies, and conditional relationships
  • Quality validation: Iterative refinement with evaluation loops, revision cycles, and built-in conformance checks
  • Flexible model selection: Choose from multiple LLMs optimized for different tasks and budgets

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Next steps

Get started

Follow the quickstart guide to generate your first dataset