Dataframer is a synthetic data generation platform that builds safe, scalable, and realistic text and tabular datasets. It provides multiple mechanisms to control data generation, including using your own samples as seeds. It lets you build, test, and deploy AI systems without exposing sensitive information.

A 3-step process is typical: 1. Upload Seed Samples – Provide example data (CSV, TXT, JSON, JSONL, MD, PDF). 2. Automatic Analysis – Dataframer analyzes data properties and axes of variation (patterns, attributes, distributions). 3. Generate Synthetic Data – Creates new datasets that mirror the statistical fidelity of your originals, without leaking PII/PHI. However, the platform also supports workflows where you don't have to provide examples (seedless generation).

Dataframer evaluates your data both during and after generation for quality and conformance—how well the generated data matches your requirements and target distributions for each data property. Apart from that, you can chat with your generated data to explore and get a deeper understanding of its sttructure and content. Dataframer also provides features that make it easy for expert humans to manually label generated datasets.

You can upload CSV, TXT, JSON, JSONL, PDF, or Markdown files individually or in folders. • Up to 300 files and 50MB total • In CSV and JSONL formats, each row/line is treated as a sample. • You can also upload multiple folders where each folder serves as a single seed sample.

No. You can generate data in seedless mode without providing any examples while maintaining full control over generation. If you do want to provide examples for structure, style, or content, uploading 2 samples is often enough for Dataframer to learn the structure and generate larger, balanced datasets.

Anonymization removes identifiers from real data, but risks re-identification. Dataframer creates entirely new synthetic records that preserve statistical accuracy without exposing original sensitive values or identifiers.

Yes. Dataframer was designed with privacy, fairness, and compliance in mind. Enterprises in healthcare (HIPAA), finance (SEC, GDPR), and government use cases can safely train and test AI systems with synthetic data.

• Healthcare: Synthetic EMRs for model testing and training without risking PHI. • Finance & Insurance: Fraud detection, Transaction data, AML, KYC, fair lending. • Conversational AI: Multi-turn chatbot training and edge-case testing. • Market Research: Synthetic survey panels and digital twins. • Text2SQL: Synthetic SQL queries for data validation and testing. • Traditional ML: Classification, anomaly detection, recommendations. • Many more...

For text extraction and NLP tasks, Dataframer uses a long-sample generation algorithm that creates realistic, complex documents (e.g., contracts, medical notes, research papers) to stress-test extraction models.

Yes. Dataframer gives you control over: • Your generation objectives which are automatically transformed into a data specification. • The data properties (axes of variation) (e.g., demographics, time, categories) with their probability distributions. • Closed-source or open-source models powering the generation. • The algorithm choice (short-form vs. long-form vs. red-teaming).

Generated datasets screened for quality and diversity issues multiple times throughout and after generation. Statistical property matching and fairness checks are also accessible in our workflows.

• Save time: Cut data preparation cycles from months to weeks. • Reduce cost: Avoid expensive manual collection/annotation. • De-risk compliance: Train AI safely without exposing sensitive data.

Dataframer offers flexible deployment options: • Hosted: Use Dataframer's managed cloud service for quick setup and maintenance-free operation. • On-premise: We are prepared to deploy in days using Kubernetes on any popular cloud (AWS, Azure, GCP) or custom cloud infrastructure for enhanced security and control.