Five concepts that define the Palantir Ontology system.
The Ontology models how your business actually operates — objects like plants, warehouses, and customers with all their complex, interconnected relationships. It is the ground truth of your enterprise, not a copy of how other systems need it.
Every decision-centric system requires three things: data (current state of the business), logic (how to reason about it), and actions (how to affect the real world). The Ontology unifies all three into a single semantic layer.
When data, logic, and actions come together in the Ontology, you get a rich digital twin that reflects how your business is operating, how to reason about it, and what actions you can take to change it — in real time.
The Ontology SDK (OSDK) lets developers generate a typed SDK from your Ontology — a custom SDK of your business and processes. Build mobile apps, React frontends, back-end integrations, or embed agentic workflows into existing systems.
LLMs were not trained on your business's data or processes. The Ontology gives AI models the context — data, logic, and actions — to reason about your specific enterprise. The goal over time: automate more of your business with AI and humans working together on the same Ontology.
Chad walks through each layer of the Palantir Ontology system in 5 minutes.
Chad introduces the Ontology as the nouns and verbs of the business. A live Palantir AIP demo shows Object Types (Plant, Warehouse, Customer) populating the Ontology trapezoid plane — representing the manufacturing supply chain.
300+ out-of-the-box connectors to AWS, Salesforce, Oracle, IBM and more. MMDP enables virtualized data from Snowflake, Databricks, and BigQuery. All data pulls into the Ontology seamlessly regardless of source.
Data Sources, Logic Sources, and Systems of Action anchor the foundation. Logic ranges from Excel spreadsheets and rule-based models to ML forecasts and third-party optimizers. Systems of Action write back to SAP, Oracle, ServiceNow, and edge devices — wherever the decision must take effect.
AIP Logic integrates generative AI models that have full access to the Ontology's context. The LM can call deterministic models, reason about business state, and drive write-back actions — orchestrating complex decisions end-to-end without human swivel-chair work.
The complete Palantir Ontology stack: Data Sources → Logic Sources → Systems of Action → Ontology Plane → Analytics & Workflows → Automations → Products & SDKs → AI + Human Teaming. All layers visible. The goal: automate more of your business over time as AI and humans collaborate on a shared Ontology.
High-score frames extracted from the video, ordered by informational density.
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AI + Human Teaming — Complete Ecosystem View
The definitive summary frame. All layers of the Palantir Ontology system rendered in a single diagram: from data sources through AI + human collaboration roles. Timestamp: 4:52.
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DATA SOURCES / LOGIC SOURCES / SYSTEMS OF ACTION — the architectural backbone introduced at 0:59.
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ANALYTICS & WORKFLOWS layer — first expansion above the Ontology plane at 2:58. Palantir Foundry analytics and workflow icons.
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AUTOMATIONS layer — AIP Logic Action Types representing generative AI-driven automation at 3:17.
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PRODUCTS & SDKs — OSDK and product icons showing how developers consume the Ontology. The extensibility story of AIP at 4:09.
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Complete 3-layer Ontology system at peak clarity — Object Types in trapezoid, full DATA / LOGIC / ACTION layers below at 2:24.
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Final closing shot — all layers rendered together at 4:59. Ideal summary / recap image.
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Split layout first appears: Chad with live Palantir AIP Ontology demo — empty trapezoid with PREVIEW MODE banner at 0:12.
today we're going to talk about the ontology what it is why it matters how you actually use it to build decision-centric systems in the real world so if we think about what is an ontology it's the
nouns and verbs that make up your business right so in this manufacturing example i have plants and warehouses that i'm supplying plants from warehouses right i'm shipping product to customers so this really reflects the ground truth about how your business is actually operating there's complex interconnected relationships right so as we model this into the ontology the goal is to actually model how your business is actually operating not how these other systems need it
so they can work so if we think about that in a decision-centric system what do i need to be able to make decisions right i need three things i need the data i need the logic and i need the actions right so i need the data the logic the actions the data represents what the current state of my business is the logic about how do i think about those things and the actions i can take to affect
the real world right so if we can dig into that a little bit further here on the data sources right i'm connecting to all my different enterprise systems whether they're homegrown out-of-the-box software legacy new stuff in the web doesn't matter we have 300 out-of-the-box connectors that you can actually connect to stuff or with mmdp you can connect to things like snowflake and databricks and bigquery and virtualized data that's already sitting in those enterprise data lakes and pull it in and just use that as part of your ontology right so this really is about making it seamless
connecting to that data across your business then sources of logic right so these could be really simple that could be an excel spreadsheet rules-based logic it could be ml models forecast third-party optimizers you bought they can be living inside of the platform outside of the platform we integrate with all these different tools no matter where the model or sources of logic are we want to integrate that and actually model that into the semantic object about how like for example a warehouse works what's the logic associated about how to think about that warehouse then the last is
the systems of action right so if i think get the data i get the logic to how to think about it now what actions can i take and how do i make it real so whether it's sap and i want to write back to sap to create an sto to move product from a to b across my network right those should be modeled in here as the actions i can take they can be other things that are on the plant floor they can be financial systems you name it we'll connect into those whether they're legacy or they're on-prem they're at the edge they're in the cloud they can be web hooks you name it we connect with those things
to really drive back those actions into the enterprise right so if we bring all the data the logic the action together here then i have this ontology that is very rich showing me my actual digital twin of how my business is operating how to think about it with the logic and what actions
i'm going to take to affect that system so then on top of that we can then build these workflows and analytics analytics really are a byproduct of the workflows that i'm building so a workflow across many different business processes or business systems that i connect together the people the process and i really then start to bubble up the insights there and then we start to add other
things in here like automations right so now can we have generative ai models integrated through aip and aip logic for example that now are helping actually go reason about it because they have access to the ontology the lm now has access to context not just the data the context about how my business operates so the lm can have access to the logic about how to think about it so the lm can call a deterministic model it can then drive an action to write back so instead of people having to swivel share from a dashboard over to another tool to figure out how to make it real we'll orchestrate those complex actions on the back end to make it real that's where really where automations come in bringing reasoning into this process and ontology really is that context of how your business is operating because the lms were not trained on your business's data or processes then last is the
products and sdk so this is where we can build data products rich applications whether that's in a mobile app for the plant floor it's a custom react app to interact with my customers or it could be through the ontology sdk where i actually create a custom sdk of my ontology so an sdk of my business and business process that i can build different applications back integrations i can integrate that sdk that has agented workflows into my existing applications lots of different really great stuff that you can do by powering different sdks throughout your business this really opens up the way that you integrate it and really improves the usability which improves speed and time to value so all of this comes
together with the goal of ai and humans working together on the ontology and the goal over time is to automate more and more of your business so really what you need is the ontology to model the data the logic the actions to drive decisions in your business