Turns Google Sheets, CSV, Excel, and Parquet files into durable, queryable data for AI agents. Lets clients securely discover datasets, inspect semantic models, run bounded analytics, and manage approved semantic overlays.
Turns warehouse/lakehouse tables into a governed entity-relationship knowledge graph exposed through MCP, enabling AI agents to answer multi-table business questions without hard-coded SQL or large schema prompts.
Turns a folder of CSV, Parquet, and JSON files into a single SQL-queryable source for AI agents, supporting JOINs across files with read-only sandboxed access.
Enables natural language search and analysis of uploaded PDF, CSV, and Excel documents using retrieval-augmented generation and MCP tools, providing contextual answers to user queries.