Synthetic Data Generation
for the AI Era
Generate high-quality, privacy-compliant synthetic data to accelerate your AI and machine learning projects. Powered by our novel ML model combining Gemini and DeepSeek architecture, backed by Carnegie Mellon professors.
Complete Solution for the AI Era
Our advanced ML architecture combines transformer-based models with differential privacy mechanisms to generate high-fidelity synthetic data. Built on PyTorch with custom attention layers and privacy-preserving optimizers.
High-Quality Synthetic Data
Generate synthetic data using our advanced transformer-based architecture with multi-head attention mechanisms and custom embedding layers, preserving complex relationships and statistical properties.
Privacy by Design
Built-in (ε, δ)-differential privacy guarantees with configurable privacy budgets. Our architecture ensures mathematical privacy bounds while maintaining data utility through advanced composition theorems.
Statistical Accuracy
Preserve high-dimensional correlations using copula-based modeling and advanced GANs. Our validation pipeline ensures KS-test p-values > 0.95 for marginal distributions.
Enterprise Scale
Distributed processing with CUDA-accelerated PyTorch backend. Handle millions of records through optimized batch processing and parallel generation pipelines.
Secure Infrastructure
End-to-end encryption with AES-256, secure key rotation, and isolated compute environments.
Fast Integration
RESTful APIs with OpenAPI specification, native SDKs for Python/JavaScript, and direct connectors for major data warehouses including Snowflake and BigQuery.
Join the Waitlist
Be among the first to experience the future of synthetic data generation.