Features
Ingest unstructured data from SharePoint and S3
OCR, parse, and chunk files in a single pass
Automated metadata tagging at thousands of files per minute
Sensitive data detection at petabyte scale
Quality and relevance scoring per file
Custom taxonomy design and autobuilding
Slice datasets by relevance, topic, time, quality, or sensitivity
Write enriched metadata back to source systems
Export AI-ready datasets to RAG pipelines and retrieval systems
Continuous monitoring and auto-refresh of datasets
Deploy in your own cloud environment
Integrate with any model or LLM endpoint
Centralized governance for tags, definitions, owners, and accuracy
UI for business teams plus APIs and Python SDK
Handles petabytes of data
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
