Features
AI coach (ADA) with personalized recommendations
Developer analytics benchmarked against 50,000 devs
4-layer statistical analysis and normalization
Collaboration health metrics from communication channels
What-if scenario analysis with causal data models
Track lead time, review time, and code quality
Monitor focus time and developer well-being
Identify areas of engineering effort investment
Manage board expectations with predictive insights
Fitness tracker style interface for individual devs
Integration with version control and project management
Real-time dashboards for engineering leaders
Quantify cost of engineering initiatives on people and code
Measure sustainability of collaboration patterns
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
