The enterprise standard
for AI context and control

Collibra is the Enterprise AI Control Plane — the layer of context and control that sits above every data platform, model, and agent. Built on eighteen years of ontology engineering, it turns scattered data into governed context: the certified knowledge about an organization. It lets teams certify and govern context, serve it to the AI models and agents that consume it, and govern what those systems do, across any data, any model, any agent, without moving your data or locking into a single platform.

Ontology is the digital twin of the knowledge an organization has. It is a formal knowledge graph of an organization’s business concepts, terms, and relationships (including lineage, business rules and definitions, semantics and other organizations specific information) that gives AI agents and data consumers the governed context they need to act on data with confidence. Unlike a glossary, an ontology captures how concepts connect, linking customers to contracts, products to policies, and data to decisions. In Collibra, ontologies power intelligent discovery, lineage, and AI-ready governance across the enterprise.

Context governance is the practice of making enterprise context (meaning, trust and permissions/policies) trustworthy enough that an AI agent can rely on it for every decision. Context governance is operationalized through governed, curated, certified, semantically enriched data products that carry their context with them. Agents and humans don't search through ungoverned lakes. They pull from a marketplace where every product carries its meaning, lineage, quality signals, and policy constraints so the agent's three questions are answered before it ever touches the data.

AI governance is the set of frameworks, processes, and controls that ensure the AI agents and systems within enterprise deploys are grounded in trusted context, operate within defined boundaries, and can be traced and audited. It's how organizations manage the risks that surface when AI meets the business: unclear ownership, poor traceability, unreliable data, model bias, and regulatory exposure.

Context engineering is the practice of structuring and delivering the right data, instructions and knowledge to AI systems so they can understand intent and produce accurate, relevant outputs. Context engineering goes beyond prompt engineering to shape everything the AI needs to act effectively, using both structured and unstructured data sources.

Deasy Labs turns just about any unstructured data, your company's enormous SharePoint, decades of emails, a million PDFs, into the perfect dataset for whatever your team is actually building with AI. For the first time, your governance policies reach all of your data. Automatic tagging and metadata enrichment surface what you have and what's relevant to your AI project. Filtering and sensitive data detection make sure the wrong content never gets in, eliminating wasted tokens. The result: less time wrangling data, more time shipping AI that delivers on accuracy.