DataFramer

Precision Synthetic Data for your AI, under your control.

Roll AI out 70% faster at a fraction of the cost by instantly simulating, augmenting, generating and anonymizing datasets.

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Supporting Datasets For:
Patient Histories Text2SQL Long-form Text Extraction Insurance Applications EHR Records Red Teaming Fraud Detection Financial Statements Transactions Legal Contracts
SOC2T2 Compliance HIPAA Compliance VPC Compliance
Used to power leading AI applications at:
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AI Leaders

Build Trustworthy AI

Privacy-preserving datasets (HIPAA, GDPR, SOC2) that comply with the strictest regulations.

Fill demographic and behavioral gaps to ensure fairer, bias-free models.

Synthetic "safe data" for faster POCs — prove value without waiting for real customer data.

Value Proposition Layers
AI Builders

Test and train your models for the real world, including the rare edge cases

    Fill gaps and simulate rare or dangerous scenarios at scale.

Simulate fraud attempts, rare medical conditions, or complex financial scenarios at scale.

Augment human-labeled data with synthetic generation: humans focus on nuance, AI handles volume.

Build resilient models that don’t break in the wild.

scenarios
Chatbot UX
Financial Documents
Text Extraction

Privacy-Safe AI Evaluations and Development

Dataframer generates fully synthetic datasets that preserve statistical fidelity while removing or masking PII/PHI. Enterprises can test and train models without exposing customer data.

  • Compliance with HIPAA, GDPR, SOC2
  • Build AI without risking leaks
  • Unlock access to restricted datasets for faster iteration

Smarter, Safer Conversational AI

Dataframer simulates multi-turn dialogues, including rare or adversarial scenarios, to stress-test chatbot logic before deployment.

  • Train bots on rare/edge cases
  • Improve handling of context over long conversations
  • Reduce failure modes and hallucinations

Bias-Free, Realistic Tabular Data

Dataframer expands tabular datasets with realistic synthetic records that mirror true numerical distributions (e.g., transactions, claims). Gaps and imbalances are corrected automatically.

  • Fairer AI decisions across demographics
  • Safe financial data that's accurate to distributions
  • Fill gaps in edge cases for risk/fraud modeling

Boost Model Accuracy with Synthetic ML Data

Dataframer generates rare events and minority-class examples, strengthening training datasets for anomaly detection, classification, risk scoring, and recommendation engines.

  • Improve recall on rare anomalies
  • Reduce false negatives in risk models
  • Better personalization for recommendations

Stronger Models for Text & Document AI

Dataframer creates synthetic long-form documents with labeled entities, section structures, and complex layouts. Perfect for training extraction models without licensing or compliance hurdles.

  • Train on larger, richer document sets
  • Handle edge cases (nested entities, long spans)
  • Reduce annotation costs for long text corpora
UI and API

Generate pre-evaluated datasets
with an easy-to-use UI or API

AI Accuracy Assessment
AI Assessment Report AI Security Assessment AI Governance Dashboard
features

Why DataFramer?

Structured Workflow with API Access

Dataframer combines a clear three-step workflow (Seed, Analysis, Generation) with full API integration. This balance of transparency and automation ensures scalable synthetic data generation with strong governance.

Control over Data Properties (Axes of Variation)

The platform automatically identifies attributes and variables in the seed data before generation. This gives teams precise control over dataset diversity and ensures better coverage of underrepresented scenarios.

Evaluation Built In

Continuous evaluation is embedded in the platform, including quality, validity, diversity, PII, and fairness checks. Enterprises can validate and label generated datasets without relying on separate external tools.

Text-First by Design

Purpose-built for structured and unstructured text, including formats like CSV, Parquet, SQL extracts, JSON, and JSONL document corpora. Optimized for enterprise NLP and LLM evaluation and fine-tuning.

Designed for Developers and Enterprises

Easy defaults and fast setup make Dataframer accessible for small teams, while scalability, compliance features, and reporting address enterprise-level requirements.

Fairness and Bias Mitigation

Built-in controls allow balancing of underrepresented groups and validation of fairness during generation. This ensures synthetic datasets are inclusive, representative, and trustworthy.

FAQ

Frequent questions and answers

What is Dataframer?
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.
How does Dataframer work?
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).
How do I trust Dataframer?
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.
What formats can I upload?
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.
Do I need my own data to get started?
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.
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: • 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).
How does Dataframer ensure quality?
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.
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 are prepared to deploy in days using Kubernetes on any popular cloud (AWS, Azure, GCP) or custom cloud infrastructure for enhanced security and control.

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