Connecting Relational Schemas to AI with Zero Data Movement

Traditional enterprise analytics pipelines rely on heavy, expensive ETL jobs that replicate sensitive production data across multiple cloud warehouses, data lakes, and third-party SaaS vendors. For regulated institutions—such as tier-1 banks, healthcare systems, and government agencies—this pattern creates unacceptable compliance risks, expands the attack surface, and drives up cloud egress costs.

ZeroQueries solves this bottleneck through an in-place conversational relational analytics architecture. Instead of moving raw tabular data to an external AI model, ZeroQueries inspects the database metadata and schema constraints locally. It builds an in-memory directed schema graph representing all primary keys, foreign keys, and indexes.

When a business user asks a natural-language question, the engine queries the schema knowledge graph rather than raw table rows. A deterministic Abstract Syntax Tree (AST) parser validates every generated SQL token against strict read-only rules before execution on a dedicated read replica.

The result is instant analytics delivered directly to decision makers in under three seconds, with mathematical verification that no raw records or PII ever leave the organization's secure perimeter.