Compiled context served through MetaKarta MCP
Structured AI consumption in Snowflake Cortex Analyst and Databricks Genie
Column-level lineage from the answer back to the source
Grounding validated in Context Sandbox before production
AI queries against raw schema produce inconsistent answers. The model infers what "ARR" means from column names and gets it wrong in a new way every time. Text-to-SQL breaks on metric fragmentation.
AI governance mandates are arriving faster than the technical path to meet them. Provenance, auditable outputs, and traceable answers are all being asked for, and a prompt workaround supplies none of them.
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Canonical metric definitions, column-level lineage, catalog entries, and governance policies all sit in one shared metadata repository. MetaKarta MCP exposes them to AI consumers through two toolsets, Metadata Management Tools and Semantic Hub Tools.
Agents also reach governed definitions through structured AI consumption in Snowflake Cortex Analyst and Databricks Genie, because Semantic Hub has already compiled those definitions into the warehouse's native format.
Context Sandbox runs a real business question against the ontology before anything reaches production, showing which definitions the answer used, which relationships it traversed, and which assets it touched.
Test the claim yourself, on your data, with the grounding visible. (locked string, verbatim)
Same answer, AI and BI. Proven accuracy. Deterministic.
Every answer traces from the response, through the transformation, back to the source column.
Compiled context, deterministic SQL. No compute tax, no token tax.
MetaKarta establishes the shared metadata foundation organizations have been trying to patch together for years: trusted BI, reliable AI, and defensible governance, compiled into every architecture, every cloud, and every tool. No runtime dependency. No vendor lock-in. No greenfield start required.
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