Dataframer is a synthetic data generation platform that transforms small examples of your data into safe, scalable, and realistic datasets. It lets you build, test, and deploy AI systems without exposing sensitive information.
How does Dataframer work?
Dataframer follows a 3-step process:
1. Upload Seed Samples – Provide example data (CSV, TSV, TXT, JSONL, MD).
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.
How do I trust Dataframer?
DataFramer provides you a conformance score, and evaluations for 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 review the generated datasets.
What formats can I upload?
You can upload CSV, TSV, TXT, JSONL, or Markdown files individually or in folders.
• Up to 300 files total
• 40MB.
• In CSV/JSONL formats, each row/line is treated as a sample. You can also upload multiple folders for multi-file subjects.
Do I need a lot of data to get started?
No. Even a handful of representative seed samples is enough for Dataframer to learn the structure and generate larger, balanced datasets.
How is Dataframer different from anonymization or masking?
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.
Can I use Dataframer for compliance-heavy industries like healthcare or finance?
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.
What are common use cases that DataFramer can help me with?
• 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...
How does Dataframer handle long-form text?
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.
Can I control the output?
Yes. Dataframer gives you control over:
• The data properties (axes of variation) (e.g., demographics, time, categories).
• The size of the generated dataset.
• The algorithm choice (short-form vs. long-form).
How does Dataframer ensure quality?
Generated datasets are validated against the statistical properties of the seed data. Bias detection, drift monitoring, and fairness checks can be built into your workflows.
What's the ROI of using Dataframer?
• 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.
How can I deploy Dataframer?
Dataframer offers flexible deployment options:
• Hosted: Use Dataframer's managed cloud service for quick setup and maintenance-free operation.
• On-premise: We deploy using Kubernetes-based deployments on any popular cloud platform (AWS, Azure, GCP) or custom cloud infrastructure for enhanced security and control.
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