You bought the catalog. You ran the scans. You assigned the owners. And then it stopped. Most enterprise catalogs are accurate inventories and nothing more — rules defined but never run, lineage inferred rather than observed, descriptions written once and decaying ever since. We close the gap between governance as intent and governance as execution.
What changes
Each one is a packaged capability, built to work with the data catalog you already own rather than replacing it.
Bring every AI agent in the enterprise under a single governed lifecycle — registered, owned, risk-assessed, and linked to the data and use cases it touches.
Your security tooling can see the agents. It still cannot answer for them. An inventory tells you an agent exists; it does not tell you who is accountable, what risk tier it sits in, or which governed data it reads.
An enrichment pipeline that reads your technical metadata, grounds it in your business glossary, and generates steward-approved descriptions and classifications at a scale humans cannot match.
Catalog scans tell you an asset exists. They cannot tell you what it means. Generic AI writes descriptions from column names; it does not ground them in your glossary, your taxonomy or your policies — so nothing it produces is safe to publish unreviewed.
Runtime lineage captured from actual pipeline execution — not inferred from code — and published into your catalog as the governance system of record.
Most catalog lineage is a guess. It depends on which connectors exist, thins out across orchestration and transformation layers, and describes relationships it has inferred rather than movement it has observed.
Quality rules governed in your catalog, executed at scale in your warehouse, with results published back so the catalog shows verified quality rather than aspirational policy.
Your catalog can define a quality rule. It usually cannot run the one you actually need. Native engines are built for single-table, column-level checks — which is not where real quality problems live.
Stand up the catalog properly the first time — metamodel, ingestion, glossary, dictionary, workflows, and the training that makes people actually use it.
Most catalog implementations succeed technically and fail organizationally. The scans run, the assets appear, and then nothing happens, because nobody defined what good looks like or who is responsible for it.
The accountability structure that makes every other solution hold — councils, decision rights, RACI, role definitions, and the training pathways that turn business owners into stewards.
Technology surfaces issues. Accountability resolves them. Without defined ownership and a forum that meets, every finding becomes someone else’s problem.
Every one of these runs the same architectural play. That is deliberate — it is an integration pattern rather than a product, so it absorbs new platform features instead of being obsoleted by them.
Governance is authored where the business already works — rules, glossary, ownership and policy live in your catalog as the system of record.
The heavy lifting runs where it belongs: rule evaluation, lineage capture and enrichment happen in the warehouse, the orchestrator and the AI platform.
Verified results are written back to the catalog, so it reflects what is actually true rather than what someone once intended.
Every one of these started with a governance claim an organization could not evidence. Tell us which one you would struggle to prove.
