A Global Premium Automaker Powers Governed Data Products and Agentic AI with Collate
From self-hosted OpenMetadata OSS into a managed AI context layer with Collate
Operational burden of running a context layer at enterprise scale
Building a knowledge graph and semantic layer for agentic AI
Automotive
Cloud data warehouse, cloud platform, BI tools, dbt, OpenMetadata, open-source data-quality tooling
A Fragmented Data Landscape That Siloed Data Understanding
The automaker built its data capabilities through years of departmental autonomy, with individual teams choosing their own tools and setting their own standards. Centralizing around a single data platform surfaced a fragmented estate: overlapping catalog tools, inconsistent metadata, and no shared definition of what "good" data looked like. The data platform team had to unify the estate without slowing the domains that depended on it.
The company previously ran a legacy commercial data catalog but found it offered limited new capability for what it cost, with weak account support. This led the team to explore OpenMetadata as an alternative, though it meant maintaining parallel tools with divergent metadata.
Scaling OpenMetadata into production across the enterprise was non-trivial. For the number of departments and data assets the company had, the work of patching, upgrading, scaling, and supporting a self-hosted context layer grew heavy, pulling a specialized platform team toward infrastructure maintenance and away from governance work.
The company handles sensitive manufacturing and supply-chain data and enforces a rigorous security policy, including a prohibition on broad query access to its cloud data warehouse. Any tooling had to run inside the company's own cloud environment and clear its minimum cybersecurity requirements before touching production data.
When the company started building AI agents on its data warehouse's built-in AI service, semantic ambiguity became a blocker. The same term, like "order," could mean a production order or a sales order depending on context. Without a shared semantic layer, AI agents couldn't reason reliably over the company's data.
A Managed, AI-Driven Context Layer with Collate
The automaker migrated from self-hosted OpenMetadata to Collate's managed service, deployed to a BYOC (bring-your-own-cloud) instance inside its own cloud environment. The conversion let the team keep the open standard it had chosen, hand off the operational work, and consolidate away from overlapping tools.
Collate runs on the OpenMetadata standard the company had already adopted, and the migration was quick and automated, preserving the team's investment in the open ecosystem. As a managed service inside the company's cloud, Collate took patching and scaling work off the platform team.
The legacy commercial catalog was deprecated, and the Collate service now serves as the lens into the company's data landscape. Collate connects the cloud data warehouse, BI tools, data-quality tooling, and dbt, and gives business domains a single place to discover, understand, and trust data.
Collate is deployed entirely within the company's own cloud environment, with credentials held in the company's own key vault. Instead of broad warehouse access, the deployment maps warehouse access to the company's existing identity provider, meeting security requirements while still capturing metadata context and semantic understanding automatically.
To resolve the ambiguity blocking AI adoption, the company is adopting Collate's auto-generated knowledge graph and ontology as a semantic layer, exposed through API and MCP (Model Context Protocol). External agents reason over governed definitions instead of a custom-built graph database, distinguishing between a "production order" and a "sales order" to better understand the data.
Building the Foundation for Governed Data Products and Agentic AI
The automaker replaced the operational drag of a siloed toolset and self-hosting with a single managed context platform, setting up a roadmap for increased data trust and agentic AI initiatives across its enterprise.
The data team leverages Collate's data quality test cases, rules, and dashboarding to understand data quality across its landscape. Data contracts provide clear expectations between data producers and consumers across manufacturing, supply chain, and other business domains.
The company runs Collate to power a data product strategy built on standardized templates that bundle quality, contracts, and metadata into reusable, governed packages for data team self-service. Collate is the central place where the company defines, publishes, and discovers these trusted data products.
The company has become a roadmap-shaping design partner. Working with Collate, the team helped design customizable intake forms for data products, domains, and glossary terms, with required fields and custom properties to ensure consistent, standardized processing for these data assets.
The team is moving toward computational governance that cuts manual compliance work and supports trustworthy agentic AI over its data estate, grounding AI agents in governed, unambiguous definitions built on Collate's knowledge graph and semantic layer.