AI Infrastructure and Ontology

6 posts · Sep 2021 – Apr 2026

What this trail covers

This post details the evolution of Palantir's AI infrastructure and its integration with the Ontology, focusing on how the Ontology serves as a foundational layer for AI systems. It highlights advancements in how AI models leverage the Ontology for context, reasoning, and data access, and how the infrastructure supports the deployment and scaling of these Ontology-aware AI capabilities. The Ontology is presented as a decision-centric architecture that integrates data, logic, action, and security.

All Palantir trails

Apr 2026Connecting Agents to DecisionsThis post elaborates on the Ontology's role in AI infrastructure by detailing how it integrates diverse data modalities (structured, streaming, unstructured, geospatial) into a semantic representation. It explains the Ontology's 'logic binding' paradigm for connecting heterogeneous logic assets (business logic, ML models, optimization algorithms) and its native modeling of actions as 'verbs' that are synchronized with operational systems. The post also emphasizes the Ontology's security architecture, which blends marking-, purpose-, and role-based policies for dynamic, granular access control across human and agentic workflows.
Apr 2026Connecting Agents to DecisionsThis post elaborates on the Ontology's role in decision-centric software architecture, detailing how it integrates data, logic, action, and security. It emphasizes the Ontology's ability to represent decisions rather than just data, capturing 'decision data' generated by users and agents. The post outlines how the Ontology unifies diverse data modalities, connects heterogeneous logic assets (business logic, ML models, optimization algorithms), and models actions as 'verbs' that are synchronized with operational systems. It also highlights the Ontology's security architecture, which blends marking-, purpose-, and role-based policies with dynamic lineage tracking.
Nov 2025MediumThis post introduces the Ontology Software Development Kit (SDK) for Palantir AIP, enabling developers to programmatically interact with and extend the Ontology. It details the SDK's capabilities for defining and manipulating Ontology objects, relationships, and actions, facilitating the creation of custom AI agents and applications that leverage the Ontology's rich semantic context. The post emphasizes the SDK's role in abstracting complex data interactions and enabling developers to build sophisticated AI-driven workflows.
Nov 2025MediumThis post details how the Palantir Ontology acts as a foundational layer for AI systems, enabling AI models to leverage the Ontology for context, reasoning, and data access. It emphasizes the infrastructure's role in deploying and scaling these Ontology-aware AI capabilities, presenting the Ontology as a decision-centric architecture that integrates data, logic, action, and security.
Oct 2025MediumThis post details the evolution of Palantir's AI infrastructure and its integration with the Ontology, focusing on how the Ontology serves as a foundational layer for AI systems. It highlights advancements in how AI models leverage the Ontology for context, reasoning, and data access, and how the infrastructure supports the deployment and scaling of these Ontology-aware AI capabilities. The Ontology is presented as a decision-centric architecture that integrates data, logic, action, and security.
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Jun 2024MediumThis post introduces the concept of 'Virtual Tables' within Palantir's Artificial Intelligence Platform (AIP). Virtual Tables allow AIP Agents to query and interact with data sources that are not directly integrated into the Ontology. This is achieved by defining a schema for the virtual table and providing a mechanism for AIP to translate these queries into calls to external APIs or databases. This capability enhances AIP's ability to access and utilize a wider range of enterprise data without requiring full data integration, thereby improving the intelligence and responsiveness of AI agents.
Nov 2023Building with Palantir AIP: Semantic SearchThis post introduces the concept of building semantic search applications using Palantir AIP. It details the steps involved: connecting to data sources with Virtual Tables, generating text embeddings using models, building data pipelines with Pipeline Builder, storing embeddings in an Ontology-powered Vector Store, creating a semantic search function using the Ontology, orchestrating LLM logic with AIP Logic, and building a user-facing application with Workshop. It highlights how AIP enables rapid development of production-grade AI workflows by leveraging the Ontology for context and data access.
Sep 2021How Palantir Foundry Helps Customers Build and Deploy AI-Powered Decision-Making ApplicationsThis post details the integration of Palantir Foundry with AWS SageMaker for building and deploying AI-powered decision-making applications. It outlines a four-stage process: data preparation using Foundry's connectors and ontology, model development with SageMaker Studio Notebooks and Autopilot, model integration from SageMaker Endpoints and AWS AI services into Foundry's ML environment, and model operationalization using Foundry Application Builder and Simulation Engine for feedback loops and retraining. The post emphasizes the role of Foundry's ontology in providing a semantic layer for AI models.