Features
Blazingly fast portfolio analytics engine
Customizable dashboards and reports
Real-time risk and performance monitoring
Scenario simulation and stress testing
Integration with major data providers
API-first architecture for custom workflows
Version-controlled model storage
Role-based access control for teams
Automated scheduling of analysis jobs
Interactive data visualizations and charts
Support for multi-asset-class portfolios
Historical analytics and backtesting
High-fidelity synthetic data generation via TabularARGN
Agentic data science automation for training/sampling
Natural language AI Assistant executing Python code
Mock data generation for safe experimentation and testing
Simulated data for edge-case and what-if modeling
Multi-table synthesis with referential integrity
Time-series support and data rebalancing
Differential privacy with temperature control
Deployment on Kubernetes or Red Hat OpenShift
Open-source Synthetic Data SDK under Apache v2 license
Data connectors for Databricks, AWS, Snowflake, BigQuery
Real-time data access from production systems (e.g., Databricks)
Star schema and nested sequences support
REST API and Python Client for programmatic access
Conditional simulation and seeded generation
