For enterprise data teams whose AI agents, BI tools, and warehouses disagree, MetaKarta traces each definition to its source, gives it an owner and version history, and compiles it into Snowflake, Databricks, Power BI, Tableau, and more. Your agents get the same definitions as compiled context. You keep your business logic, and nothing sits in your query path.
Vendor neutral
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Parser-based lineage
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Compile-time architecture
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Shared metadata repository
Inconsistent reporting, unreliable AI, and governance you can't demonstrate share one root cause: business logic fragmented across the stack. MetaKarta fixes it by enforcing shared definitions across the systems you already use.
Every team works from the same business definitions, so board reports and dashboards agree no matter who built them or where the data came from.
AI agents read the governed business definitions your people already use. Without them, an agent works from raw schemas and conflicting definitions, and leaves no audit trail.
Business definitions keep their owner and version history everywhere they're used, so an audit, a regulatory request, or an internal review is answered from the record.
Every data team fields the same hard questions from the business. MetaKarta answers each of them from one set of governed definitions, compiled into the tools where the question comes up.
Most enterprises run several metadata tools, bought at different times for different problems and held together with custom integrations that need constant care. MetaKarta’s Data Lineage, Data Catalog, Data Governance, and Semantic Compiler read from and write to one metamodel, so nothing has to be integrated or synchronized after the fact.
Build lineage by parsing the code that moves your data: SQL, ETL jobs, BI semantic models, pipeline definitions, and even COBOL copybooks. It is column-level and cross-system, from source to the report or AI output it feeds. Parsed code captures what was designed to run, including paths run-time logs never see. Lineage by design, not by observation.
Any number traces to source in seconds, at the column level
Lineage runs from source through transformation to AI output
Full version and configuration management supports audit answers
Document every asset in the estate and make it findable, with profiling, semantic discovery, and social curation built in. Every entry carries its source, its owner, and its lineage, so context arrives with its evidence attached. It reads from the same repository as lineage, governance, and semantics, so it stays current with the systems it documents.
Definitions are findable with lineage attached
Agents and humans share the same governed context
Ownership and meaning are documented where people look
Define policy once and let the platform carry it. Stewardship workflows assign ownership and accountability, automated classification finds PII and sensitive data at scale, and access controls compile into the systems where the data is actually used. Every governance decision is versioned, so you can answer what a policy said, who changed it, and when.
Stewardship assigns ownership and accountability
Policy travels with the definition into an AI agent's context
Version history, policy, and audit trail live in one view
Import and model business logic once, then compile and deploy it into native formats, including Snowflake Semantic Views, Databricks Metric Views, Power BI semantic models, and Tableau data sources. The artifacts are native and customer-owned, with no proprietary run-time dependency. AI agents ground on the same compiled definitions your BI tools run, so the same question gets the same answer everywhere.
Portable, customer-owned native artifacts are deployed to targets
Definitions are native in an AI agent's grounding context
The enforced definition is the audited definition
“Knowing exactly where a number comes from, how it was transformed, and what it means in each system is a hard requirement for a regulated insurance environment. MetaKarta gives our teams traceability across the entire chain, and it works at the design level, not just at runtime. That’s what makes it defensible.”
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.
MetaKarta is a Metadata Management Platform from Meta Integration Technology, Inc. It connects data lineage, data catalog, and data governance on one shared metadata repository, and compiles governed business definitions into the native formats of databases, BI tools, and AI agents. The platform is built on nearly 30 years of OEM metadata technology embedded in products from Microsoft Purview, Informatica from Salesforce, Oracle, IBM, and Qlik Talend.
MetaKarta connects to your existing estate through 400+ connectors covering legacy and modern systems, on-premises and cloud. It harvests metadata from the tools you already run, then compiles governed definitions back into them natively. Nothing sits in your query path and there's no runtime dependency, so current workflows keep working exactly as they do today.
No. MetaKarta integrates with the databases, pipelines, and BI tools you already have deployed, and value starts with the estate as it stands. Many teams do consolidate point tools for lineage, cataloging, or governance over time, since those functions run on MetaKarta's shared repository, but displacement is optional.
MetaKarta delivers governed business definitions, relationships, policies, and provenance to AI systems. Agents query through structured consumption in Snowflake Cortex Analyst and Databricks Genie, or connect via MetaKarta MCP, and each answer comes back with the supporting compiled context behind it. Column-level lineage provides an auditable provenance chain from data source through transformation to output, ready for emerging AI governance mandates.
MetaKarta includes a full data catalog and goes further: it compiles governed definitions into the systems that consume data. Documentation describes what should be true. Compilation makes it true. That's the difference between recording a definition and enforcing it.
Most teams start delivering value within weeks. MetaKarta deploys alongside existing systems and harvests metadata through connectors, so initial lineage and catalog coverage lands quickly, with scope growing from there. Full enterprise rollouts vary with the complexity of the estate.
Yes. MetaKarta runs as hosted SaaS, self-hosted in your own cloud, or entirely on-premises within your own infrastructure, providing feature parity across all 3 deployment models.
