Synthetic Data Generation for Privacy-Safe AI Training
When real-world data is scarce, imbalanced, or legally sensitive, synthetic data is the solution. We generate GDPR and HIPAA-compliant synthetic datasets that mirror real-world distributions — enabling your AI teams to train, evaluate, and iterate without privacy risk or data acquisition bottlenecks.
The Problem
Healthcare AI teams cannot use real patient records for training without extensive consent and de-identification workflows. Financial AI teams face similar constraints with transaction data. Meanwhile, rare-event classes (fraud, medical errors, equipment failures) are chronically underrepresented in available datasets — creating models that fail exactly when they matter most.
Our Solution
We apply a combination of statistical modeling, LLM-based generation, and domain expert review to produce synthetic data that is statistically indistinguishable from real data while containing zero actual personal information. Our pipelines are configurable by domain, schema, class distribution, and language.
Core Capabilities
Privacy-Safe Dataset Generation
We generate statistically representative synthetic records that preserve the distributional properties of sensitive real-world data without containing any actual personal information. Outputs are GDPR Article 4(1) compliant (not personal data) and HIPAA Safe Harbor certified — enabling use cases that are impossible with real patient or financial records.
Class Imbalance Correction
Real-world datasets are rarely balanced. Fraud is rare. Rare diseases are rare. We generate targeted synthetic minority-class samples to achieve configurable class ratios (e.g., 1:1, 1:10, 1:100), dramatically improving model performance on the edge cases that matter most for business and safety outcomes.
Evaluation-Driven Development (EDD) Test Sets
We generate adversarial, out-of-distribution, and corner-case test examples to stress-test your models before deployment. Our EDD sets cover linguistic edge cases, formatting anomalies, ambiguous inputs, and known failure modes — enabling rigorous model evaluation without collecting sensitive real data.
Business Impact
Teams that supplement real datasets with synthetic data typically reduce data acquisition costs by up to 30% while increasing minority-class model performance by 15–40%. For regulated industries, synthetic data can be the difference between a product that can legally ship and one that cannot.
- Zero real PII — GDPR Article 4(1) compliant by construction
- HIPAA Safe Harbor compatible for healthcare AI applications
- Configurable class ratios for imbalanced classification tasks
- Up to 30% reduction in data acquisition costs vs. real-data collection
- EDD test sets that expose model weaknesses before production deployment
Unlock data you couldn't collect before
Tell us your schema, target domain, and privacy constraints. We'll return a sample synthetic dataset within 48 hours so you can validate quality before committing to a full engagement.
Request a Free PoC