In work, an ontology is a shared model of a customer’s business objects, such as plants, work orders and shipments, with their properties and the links between them, mapped onto the underlying tables so that people and applications use the same terms. Palantir’s Ontology documentation describes this as an operational layer of objects, properties and links on top of integrated datasets, virtual tables and models, with action types that capture data from the people who use it and orchestrate decisions that connect to existing systems. The idea reaches beyond Palantir: AWS says its FDE teams deploy a semantic layer into the customer’s own AWS account that publishes a governed, versioned knowledge graph for agents to reason over. Source 1AWS invests $1 billion to embed AI forward deployed engineers with customersPublisherAbout AmazonSource typecompany blog In BI tools, “semantic layer” usually means shared metric definitions over a warehouse, which is close to an ontology but not the same thing.
Why it matters in interviews
The idea is useful when a design or decomposition prompt involves several systems that each define “customer”, “asset” or “order” differently. A strong candidate names the objects before drawing pipelines, writes one out (WorkOrder { id, asset_id, opened_at, status } linked to Asset), says which source system is authoritative for each property, and names who owns each definition. Then say how an Asset gets one identity when the maintenance system and the ERP key it differently: the match rule, where unmatched records go, and who reviews them. That is where the design gets hard, so expect to be pushed there. Include what users can do, such as closing a work order, not only what they can read.
If you interview for a role built on Palantir’s platform, use its words: object types, properties, link types and action types. An April 2026 Palantir blog post names a software architecture that revolves around the Palantir Ontology as the key differentiator in its AIP AgentCamps. Source 2Connecting Agents to Decisions (Palantir Blog)PublisherPalantir BlogSource typecompany blog
The lesson Reference architecture: unifying fragmented data covers the matching and ownership work under the model.