Cut Text2SQL token costs up to 81%, and stop paying for wrong answers: a Neo4j semantic layer across BigQuery and Databricks

Across two deliberately hostile catalogs (278 Census tables on BigQuery and 264 legacy tables on Databricks), a Neocarta semantic layer in Neo4j makes Text2SQL agents faster, cheaper, and more accurate. On Census it cut tokens up to 81% while lifting a cheap model from 2/4 to 4/4; on Databricks flagships it saved up to 54% (roughly $1,527 → $693 per month at 10K queries on Opus 4.5 as of August 2026), with every model at 11/11. And without the layer, that same cheap model burned 66,687 tokens and still gave up with no answer. A semantic layer sits between your data and whatever consumes it (a BI tool, or now an AI agent) and turns opaque physical schema into business meaning. And its inputs aren’t only databases: it harvests metadata from warehouses and lakes, data catalogs, query logs (real join paths and usage), and business glossaries or ontologies. Point an agent straight at raw tables and it re-derives your joins, definitions, and table choices on every prompt, guessing which of a dozen “revenue” tables is the real one. The semantic layer is what removes the guessing. A semantic layer sits over many sources (warehouses, catalogs, query logs, and glossaries), not…