Features
Automated underwriting model population from OMs, rent rolls, T-12s
Cell-level citation to source document, page, and table
Conflict detection across multiple source documents
Custom chart of accounts mapping
Audit trail for every extracted data point
Hotspot detection across 450,000 US trade areas
Buy-box criteria matching
Saved search alerts (email and in-app)
1, 3, and 5-mile radius analysis
Supports self-storage asset class
Kickoff to first extraction in 2 business days
Processes PDF, Excel, and other deal doc formats
No model training on client data (data privacy)
Isolated environments per client
Dedicated onboarding with live support
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
