TECHNOLOGY
A proprietary generative architecture, conditioned for control and benchmarked against real-market ground truth — built to move beyond a single historical path.
METHODOLOGY
Generative modeling, grounded in microstructure
Developed from denoising-diffusion and cross-attention methods adapted to market data, the engine learns the statistical fingerprint of real markets — volatility clustering, fat tails, and cross-asset correlation — then generates paths that preserve those properties rather than smoothing them away.
generative architecture diagram
conditioning controls diagram
Conditioning controls for precise scenarios
Explicit controls steer regime, volatility level, and tail severity, so teams can request exactly the conditions they need to test — not just resampled history. Conditioning adapts to new regimes while preserving the market patterns learned in pre-training.
VALIDATION & FIDELITY
Synthetic output is only useful if it behaves like real markets where it should and stresses where it must. Every series is measured against the distributional and tail properties of real data before it ships.
Return distribution overlay
synthetic vs. real · KDE
Tail behavior (Q–Q)
quantile alignment
ARCHITECTURE & DEPLOYMENT
CORE
Generative engine
API
REST & streaming endpoints
SDKs
Python, R, and a typed JS client
DEPLOY
Managed cloud or isolated on-prem
API-First
Built to drop into existing pipelines.
Secure Deployment
Cloud or on-prem, fully isolated.
Scenario Testing
Explore conditions that never occurred.
Model Validation
Robust validation against ground truth.