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Ontology-driven Enterprise Adaptability

12 posts · Dec 2022 – May 2026

What this trail covers

This post details the evolution of Palantir's AI-enabled Customer Service Engine (CSE), focusing on its implementation at a large tools manufacturing, wholesale, and retail business. It highlights the challenges of building scalable, accurate, and consistent AI for customer service and introduces a multi-agent architecture to address context window constraints, query response times, and scalability. The system leverages an Ontology tailored to the partner's operations, integrating data from various enterprise systems (SAP, Salesforce, UPS, ERPs) and utilizing approximately 50 deterministic 'tools' (retrieval mechanisms, similarity search, pricing/availability models, external web-hooks) for AI agents to use. The CSE achieved over 90% query resolution within six weeks, with complex cases escalated and a feedback loop for continuous learning. Key impact metrics include faster response times (<10 seconds for resolution actions), reduced repeat queries (75+%), and improved fraud detection.

All Palantir trails
  1. Enterprise Business Software and the Mixed-Up Chameleon ProblemThis post introduces the concept of an 'ontology-driven enterprise adaptability' layer that sits above traditional ERP systems. It details how a shared ontology can act as a Rosetta Stone for enterprise systems, enabling interoperability between legacy and modern systems. The post describes an adaptability and extension layer powered by the ontology, where business logic, workflows, and user experiences can be built, including the use of agentic AI for tasks like data migration, reconciliation, and validation. This approach aims to allow organizations to evolve their unique processes while keeping the ERP core stable and standardized, and it redefines data migration as an ontology-driven, iterative capability rather than a one-time trauma.
  2. Enterprise Business Software and the Mixed-Up Chameleon ProblemThis post elaborates on the ontology-driven enterprise adaptability model by framing the problem as the 'Mixed-Up Chameleon Problem' where businesses are forced to conform to rigid ERP systems, losing their unique value. It emphasizes that modern software can adapt to business needs, not the other way around. The post details how an interoperability layer above the ERP, powered by a shared ontology, allows for an adaptability and extension layer. It highlights the role of agentic AI in accelerating data migration and integration, transforming it from a manual, multi-year effort into an automated, iterative process. The core contribution is the reframing of enterprise software architecture to prioritize business uniqueness and adaptability, with the ERP serving as a stable system of record for accountability rather than a constraint for operations.
  3. Connecting Agents to DecisionsThis post elaborates on the Palantir Ontology's role in decision-centric enterprise architecture, detailing how it integrates data (including 'decision data' and lineage), logic (business logic, ML models, optimization algorithms), and action (execution and orchestration). It introduces the concept of 'decision data' and 'decision lineage' as crucial for AI-driven learning and agentic memory, and emphasizes the Ontology's ability to connect heterogeneous logic assets and synchronize actions back to enterprise substrates. The security section is expanded to include dynamic, runtime-computed policies that blend marking-, purpose-, and role-based controls.
  4. Connecting Agents to DecisionsThis post introduces the native modeling of actions within the Palantir Ontology, enabling human and AI-driven actions to be safely staged as scenarios. It details how these actions are governed with the same granular access controls as data and logic primitives and securely written back to enterprise substrates (transactional systems, edge devices, custom applications, etc.). This directly addresses the capability of connecting agents to enterprise decisions by providing a cohesive, decision-centric model that unifies data, logic, and action, with comprehensive security and lineage tracking.
  5. How Palantir Foundry Extends Your Data PlatformsThis post details how Palantir Foundry extends traditional data platforms by integrating data, models, and decisions to fuel operational connectivity. It emphasizes Foundry's role as an operating system that coordinates the interplay of these components, enabling dynamic and scalable decision-making. Key extensions include advanced model/AI integration and creation across the MLOps lifecycle (supporting native integrations, code workbooks, model management, and model objectives), a dynamic ontology and semantic layer that digitally represents the organization's data and logic (encoding semantics, kinetics, and permissions), modular workflows and applications for read-write operational capabilities (using no/low-code builders and open APIs), and decision orchestration for writing results back into the ontology and operational systems (via bidirectional connectors, simulations, and full lineage), creating feedback loops for organizational learning.
  7. A Better ConversationThis post introduces the Customer Service Engine (CSE) and its application at a large tools manufacturing, wholesale, and retail business. It details the challenges of building scalable, accurate, and consistent AI for customer service, leading to the adoption of a multi-agent architecture to overcome context window constraints and improve query response times. The system leverages an Ontology tailored to the partner's operations and integrates data from SAP, Salesforce, UPS, and ERP systems. Approximately 50 deterministic 'tools' (retrieval mechanisms, similarity search, pricing/availability models, external web-hooks) were developed and incorporated into Palantir AIP for AI agents. The CSE achieved over 90% query resolution within six weeks, with complex cases escalated for human review and a feedback loop for continuous learning. Key impact metrics include faster response times (<10 seconds for resolution actions), reduced repeat queries (75+%), and improved fraud detection.
  8. MediumThis post introduces the Ontology Software Development Kit (SDK), a new capability that empowers developers to build and integrate applications with the Palantir Ontology. It details how the SDK facilitates the creation of custom agents and workflows, enabling more sophisticated AI-driven enterprise solutions. The post highlights the use of the SDK in enhancing the AI-enabled Customer Service Engine (CSE) by allowing for tailored agent development and seamless integration with the Ontology. This contribution focuses on the developer experience and extensibility of the Ontology platform.
  9. Building with Palantir AIP: Data Tools for RAG / OAGThis post introduces Ontology Augmented Generation (OAG) as an evolution of RAG, detailing how LLMs can leverage deterministic logic tools and actions through the Palantir Ontology. It highlights the use of HyperAuto for autonomous data integration from systems like SAP to create ontology objects, the importance of Data Health and Data Lineage for data integrity, and the application of AIP Logic to build AI-powered functions that utilize the Ontology for context and decision-making. The post demonstrates a practical application of these capabilities in addressing a simulated supply chain disruption.
  10. MediumThis post details the evolution of Palantir's AI-enabled Customer Service Engine (CSE), focusing on its implementation at a large tools manufacturing, wholesale, and retail business. It highlights the challenges of building scalable, accurate, and consistent AI for customer service and introduces a multi-agent architecture to address context window constraints, query response times, and scalability. The system leverages an Ontology tailored to the partner's operations, integrating data from vari
  11. MediumThis post details the evolution of Palantir's AI-enabled Customer Service Engine (CSE), focusing on its implementation at a large tools manufacturing, wholesale, and retail business. It highlights the challenges of building scalable, accurate, and consistent AI for customer service and introduces a multi-agent architecture to address context window constraints, query response times, and scalability. The system leverages an Ontology tailored to the partner's operations, integrating data from vari