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.
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.
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.
The assistant works out what "revenue" means from column names and table structures, while the dashboard runs a definition someone coded years ago. Two sources of meaning produce two numbers. When the agent and the dashboard both resolve against the same governed definition, they return the same answer.
Two paths. External agents connect through MetaKarta MCP, a single server with two toolsets: Metadata Management Tools for lineage, catalog, and governance metadata, and Semantic Hub Tools for compiled context and deterministic SQL. Snowflake Cortex Analyst and Databricks Genie read the governed definitions directly, because Semantic Hub has already compiled them into the warehouse's native formats of Snowflake Semantic Views and Databricks Metric Views.
Any MCP-compatible agent can connect to MetaKarta MCP, and Cortex Analyst and Genie read the compiled definitions natively. For AI-generated documentation inside MetaKarta, you pick the LLM provider: Anthropic, Google, Ollama, OpenAI, or Azure OpenAI.
Access runs on per-user tokens, so an agent sees exactly what the person behind it is authorized to see. Every response returns the metadata evidence that produced it, permissioned to the requesting user.
Context Sandbox runs a real business question against the ontology and shows which definitions the answer used, which relationships it traversed, and which assets it touched. Ontology Modeling binds business entities, relationships, and hierarchies to the semantic models and physical columns that implement them, so what you test in the sandbox is what the agent reads in production.
An agent working from raw metadata pays twice: compute to traverse a graph and assemble context, then tokens to generate SQL that often needs several attempts. Semantic Hub Tools hand the agent compiled context and deterministic SQL built from definitions your stewards approved, so both of those costs drop out.
MetaKarta traces the context an agent used to answer: from the response, through the governed definition, back to the source column.
