Diagram showing enterprise data connected through an Ontology to analysis, operational workflows, AI agents and integrations, with governed actions and writebacks under shared governance, security and auditability.
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Capabilities

Leveraging ICT for business with diverse data from ERP, CRM, logistics, and autonomous systems requires an alternative architecture for optimal analysis, AI, and feedback.

Data, logic, and action - combined into a enterprise decision model designed for human-AI teaming.

How Operational Intelligence Works

The digital twin

Operational intelligence connects trusted data, business context and the ability to act. Palmakers brings these together through Palantir Foundry and Enterprise AI, with the Ontology representing the objects, relationships and actions that define how your organisation operates.

To grasp the value of this new architecture, we begin with three essential decision-making elements:

  • Data (information for decisions)
  • Logic (decision evaluation process)
  • Action (decision implementation).

Data

Build a trusted foundation from enterprise systems, documents and operational signals. Governed data products provide consistent, reusable information for business teams, applications and AI.

Logic

Apply business rules, analytical models, human judgement and AI to evaluate situations and determine the next step. The Ontology gives this reasoning the business context it needs.

Logic

Data is crucial, but decision-making requires a balance with logic and AI. Despite AI-driven decisions, employees shape workflows, utilizing AI's learning and reasoning to automate tasks, enhance efficiency, and analyze data for informed decisions. Ontology connects human and machine logic, akin to business logic in customer service interactions. The next step involves developing the model for executing the decision (the action).

Action

Build a trusted foundation from enterprise systems, documents and operational signals. Governed data products provide consistent, reusable information for business teams, applications and AI.

Data

Organizational data is rapidly expanding in volume, variety, and velocity, taking diverse forms such as (un)structured, streaming, edge data, and decision-generated data from employees. This "decision data" includes details about:

  • decisions made,
  • options evaluated,
  • implications for data changes, and the subsequent updates to source systems.
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Logic

Data is crucial, but decision-making requires a balance with logic and AI. Despite AI-driven decisions, employees shape workflows, utilizing AI's learning and reasoning to automate tasks, enhance efficiency, and analyze data for informed decisions. Ontology connects human and machine logic, akin to business logic in customer service interactions. The next step involves developing the model for executing the decision (the action).

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Action

The Ontology facilitates secure execution of actions as scenarios, ensuring data and logic security, and enabling secure writebacks to business entities. Closing this "action loop" in real-time decision-making distinguishes an operational system from an analytical system. In summary, Ontology integrates data, logic, and actions into a comprehensive decision model, driven by a modular architecture from data integration to end-user workflows.
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The Ontology Connects It All

Ontology

The Digital Twin

The Ontology represents customers, assets, orders and other business objects together with their relationships and permitted actions. It provides an operational digital twin: a shared representation of the business that people and AI can use to understand situations and take action.

The Ontology acts as a unifying force, seamlessly integrating data, logic, and actions into a comprehensive decision model—the operational layer or "digital twin" of the organization. Representing real-world counterparts with interconnected objects, it provides rich context for both human and AI workflows. Designed to align with business intricacies, the Ontology incorporates an action loop, ensuring changed data is seamlessly communicated back to source systems.

Linked Object Types

Connect customers, orders, suppliers and assets in a shared operational model. These relationships help teams understand dependencies and trace how a change in one part of the business affects another—for example, how a delayed supplier delivery impacts production and customer commitments.

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Human & AI Workflows

Connect customers, orders, suppliers and assets in a shared operational model. These relationships help teams understand dependencies and trace how a change in one part of the business affects another—for example, how a delayed supplier delivery impacts production and customer commitments.

Operational decisions

Turn operational context into timely action. Teams can evaluate alternatives, approve changes and trigger workflows directly from the Ontology—for example, reallocating inventory or rescheduling maintenance. Record decisions and their outcomes to support traceability and continuous improvement.

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