Palantir Foundry Explained: How Its Ontology Turns Enterprise Data Into Operational Decisions

Palantir Foundry is not simply a database, dashboard platform or conventional data warehouse.

Palantir positions Foundry as its data operations platform: software designed to connect enterprise data, transform it into governed pipelines, model the organisation through an Ontology and then make that information usable inside analytics, applications and operational workflows.

That makes Foundry relevant to organisations dealing with fragmented systems, complex operational data and decisions that require information from multiple departments or platforms.

Palantir now describes its wider architecture around three main platforms: Foundry for data operations, AIP for generative AI and Apollo for software delivery. Together, these platforms form the foundation of Palantir’s enterprise software stack.

TechnologyBlog.co.za has not independently deployed or benchmarked Palantir Foundry. This article therefore focuses on verified platform architecture, features and use cases rather than providing a hands-on review.

What is Palantir Foundry?

At its simplest, Foundry takes data from different systems and makes it usable within a common operational environment.

Those sources can include structured databases, semi-structured files, unstructured information and streaming data.

Palantir says its integration framework supports structured, semi-structured and unstructured sources alongside batch, micro-batch and streaming transfer methods.

Typical enterprise sources might include:

• ERP systems
• CRM platforms
• Manufacturing systems
• Financial data
• Supply-chain systems
• Databases
• Files
• Cloud storage
• Sensor or operational data

Once connected, Foundry can clean, transform and combine the information into governed datasets.

The platform then goes a step further by mapping those datasets into an Ontology representing real-world concepts such as customers, orders, factories, products, equipment or transactions.

That Ontology is one of the biggest differences between Foundry and a system concerned only with moving data from one database to another.

Foundry starts with data integration

Enterprise data rarely lives in one system.

A manufacturer might store sales information in one platform, inventory in another and production data in a third.

Foundry’s Data Connection functionality is designed to synchronise those sources into the platform.

Palantir lists support for systems and protocols including relational databases, S3, HDFS, SFTP, FTPS and local directories, with extensibility for additional connection types.

Foundry can also connect one Foundry environment to another.

The goal is not simply to copy information.

Palantir’s architecture tracks how raw information moves through the platform and into downstream datasets, which supports lineage, troubleshooting and governance.

Data can move in batch, incremental or streaming pipelines

Foundry supports multiple pipeline approaches.

Palantir identifies three main pipeline types:

• Batch
• Incremental
• Streaming

A batch pipeline might process information at scheduled intervals.

An incremental pipeline processes only changes rather than rebuilding an entire dataset every time.

Streaming can support environments where information needs to move with much lower latency.

The correct approach depends on the use case.

A financial report generated once every night has very different requirements from an operational system responding to live manufacturing events.

Pipeline Builder provides a low-code integration layer

One of Foundry’s major tools is Pipeline Builder.

It allows users to create transformation pipelines through a visual point-and-click interface rather than requiring every workflow to be written manually in code.

Palantir says Pipeline Builder uses Spark and Flink as parts of its architecture while providing its own transformation and validation layers around them.

Users can:

• Import data
• Transform values
• Join datasets
• Create outputs
• Build incremental pipelines
• Apply health checks
• Manage changes
• Schedule pipelines

The tool is intended to allow technical and non-technical users to collaborate on the same data workflows.

Foundry also supports code-based approaches for teams that need greater control.

Foundry can push compute into external platforms

Foundry does not necessarily require every transformation to execute entirely inside its own compute environment.

Palantir’s current documentation says Pipeline Builder supports external pipelines that can push computation into external systems including Databricks and Snowflake under supported configurations.

That can matter in enterprise environments where large existing data platforms are already in place.

Instead of requiring an organisation to immediately replace every system, Foundry can operate as part of a wider data architecture.

This interoperability is an important consideration when evaluating the platform for established enterprises.

The Ontology is the centre of Foundry

The most important Foundry concept is the Palantir Ontology.

An Ontology maps technical data into concepts that people inside the organisation actually recognise.

Instead of exposing only database tables such as:

customer_orders_2026

the Ontology might represent:

Customer

Order

Product

Factory

Shipment

These become objects.

Relationships between them become links.

A customer can therefore be linked to an order, which is linked to a product, shipment and manufacturing site.

Palantir describes the Ontology as an operational representation of the organisation and says it can function as a digital twin containing both semantic elements and operational actions.

The Ontology contains both nouns and verbs

Palantir’s architecture documentation provides a useful way of understanding this.

Objects can effectively represent the nouns of the organisation.

Examples include:

• Factory
• Product
• Customer
• Vehicle
• Employee
• Purchase order

Actions can represent the verbs.

Examples might include:

• Update an order
• Reassign inventory
• Approve a request
• Change a delivery route
• Trigger maintenance

Palantir says the Ontology combines data, logic, actions and security policies into one environment that can be used by both people and AI systems.

That means Foundry is designed to go beyond simply showing users what happened.

It can also provide governed mechanisms through which users take action.

Actions can write back into operational workflows

Traditional analytics systems are often read-only.

A dashboard might tell an operations manager that inventory is running low, but the user then needs to switch to another system to do anything about it.

Foundry’s Ontology includes Action Types designed to connect decisions back into business workflows.

An action can capture user input or orchestrate a change involving another system.

This is one reason Palantir describes its platform in operational rather than purely analytical terms.

Analytics can identify a problem.

The Ontology can provide a governed mechanism for acting on it.

Functions add business logic

The Ontology can also contain functions.

Functions are used to implement business logic and calculations around objects and workflows.

For example, an organisation might implement logic that calculates:

• Supply risk
• Inventory requirements
• Maintenance priorities
• Customer eligibility
• Financial exposure

That logic can then become reusable across applications instead of being recreated independently in multiple dashboards.

Foundry includes analytics tools for different users

Not every Foundry user needs to write code.

Palantir currently lists several integrated analytics applications including Contour, Quiver and Code Workbook.

Contour

Contour is designed for visual exploration of tabular datasets.

Users can transform information and build charts without requiring a fully coded analytics workflow.

Quiver

Quiver works with objects and time-series data from the Ontology.

It provides a point-and-click analytical environment for filtering, visualisation and dashboard creation.

Code Workbook

Code Workbook is designed for more technical users.

Palantir says it can combine Python, R and SQL transformations with data engineering and data-science workflows.

This combination allows different roles to work within the same underlying data and governance environment.

Workshop turns the Ontology into operational applications

Foundry also includes tools for creating applications.

Palantir’s Workshop application can use Ontology objects and actions to create interfaces tailored to operational users.

Instead of exposing raw datasets, an organisation could build a purpose-specific application for:

• Supply-chain management
• Production planning
• Customer servicing
• Logistics
• Maintenance
• Financial operations

Palantir’s documentation recommends application-building tools such as Workshop when a use case requires more complex layouts, multi-step workflows or writeback rather than a read-only dashboard.

That reinforces Foundry’s positioning as an operational platform rather than simply an analytics package.

Foundry tracks data lineage

Data lineage is critical when many transformations occur between a source system and a final business decision.

Foundry tracks the relationships between datasets and transformations.

This can help teams determine:

• Where information originated
• Which transformation changed it
• Which downstream datasets depend on it
• What could be affected by a pipeline change

Palantir integrates lineage with its data integration and transformation architecture rather than treating it as a completely separate documentation process.

That can become particularly important in regulated or highly complex environments.

Security controls are built into the platform

Foundry provides several layers of access control.

Palantir organises data and resources into Projects and Organisations, while groups and roles determine user access.

For particularly sensitive data, the platform also supports Markings.

A Marking can impose an additional mandatory requirement before a user is allowed to access a resource.

For example, an organisation could apply a PII Marking to information containing personally identifiable data.

Being an owner of the dataset does not automatically allow someone to bypass that Marking.

This creates an additional security layer above ordinary role-based access.

Markings can protect sensitive enterprise information

Palantir distinguishes mandatory Marking controls from ordinary discretionary permissions.

A user generally needs both:

• Permission to access the resource
• Eligibility for all applicable Markings

That enables organisations to implement controls around particularly sensitive categories of information.

Possible examples include:

• Personally identifiable information
• Financial records
• Health information
• Confidential operational data

These capabilities can support a company’s governance programme, but they do not automatically make an organisation compliant with privacy regulations.

The organisation remains responsible for how data is collected, processed, retained and used.

Foundry has extensive audit logging

Foundry audit logs record user and system activity.

Palantir says its audit logs are designed to answer four main questions:

• Who performed the action?
• What did they do?
• When did it happen?
• Which resources were involved?

Audit information can assist with security investigations, compliance reviews and operational troubleshooting.

The logs themselves can contain sensitive information, including personally identifiable information, so organisations also need to control who can access audit datasets.

Governance remains the customer’s responsibility

Palantir provides technical tools for implementing data protection and governance policies.

Its own documentation nevertheless frames lawful and appropriate data use as something customers need to manage throughout the complete data lifecycle.

That distinction is important.

Buying Foundry does not automatically make a company compliant with:

• POPIA
• GDPR
• Industry regulations
• Internal governance requirements

Technology can enforce policies, but the organisation still needs to define the correct policies and legal basis for processing information.

For South African organisations, that means a Foundry deployment containing personal information would still need to form part of a wider Protection of Personal Information Act compliance programme.

Foundry and AIP are closely connected, but they are not the same thing

Palantir increasingly discusses Foundry alongside Artificial Intelligence Platform, or AIP.

The distinction is useful.

Palantir currently describes:

Platform	Primary role
Foundry	Data operations
AIP	Generative AI
Apollo	Software deployment and delivery

Foundry provides much of the governed data and operational context.

AIP adds tooling for connecting large language models and AI agents to that environment.

Apollo handles deployment and continuous software delivery across infrastructure.

The platforms therefore overlap operationally, but calling Foundry itself only an AI platform would understate its broader data and application architecture.

Foundry gives AI access to business context through the Ontology

The connection between Foundry and AI becomes clearer through the Ontology.

An LLM generally needs more than access to raw database tables if it is expected to understand how a business operates.

The Ontology can represent customers, assets, transactions and operational relationships using concepts the AI can work with.

Palantir says its Ontology automatically creates APIs and an Ontology Software Development Kit that can be used as an operational connectivity layer across the enterprise.

AIP can then use these governed objects, functions and actions to build AI-assisted workflows.

That is different from simply connecting an unrestricted chatbot directly to a corporate database.

Foundry supports machine-learning workflows too

Foundry’s logic services are not limited to generative AI.

Palantir’s current architecture includes capabilities for:

• Training machine-learning models
• Integrating external models
• Business rules
• Model operations
• Generative AI
• Agent workflows

Code Workbook and other model-development tools can also be used for traditional data-science workflows.

Foundry therefore sits across both conventional enterprise analytics and newer AI applications.

A typical Foundry workflow

A simplified enterprise workflow could look like this:

Stage	Foundry capability
1. Connect	Synchronise databases, files and external systems
2. Transform	Clean and combine information through pipelines
3. Validate	Apply quality checks and pipeline monitoring
4. Govern	Apply security, permissions and lineage
5. Model	Map information into Ontology objects and relationships
6. Analyse	Use Contour, Quiver or code-based tools
7. Build	Create operational applications in Workshop
8. Act	Use Ontology Actions to update workflows
9. Audit	Record activity through platform audit logs
10. Extend	Connect Foundry data and workflows to AIP

The actual architecture of a large deployment can be substantially more complex.

Palantir Foundry platform overview

Capability	Palantir Foundry
Platform category	Enterprise data operations
Data integration	Yes
Structured data	Yes
Semi-structured data	Yes
Unstructured data	Yes
Batch pipelines	Yes
Incremental pipelines	Yes
Streaming pipelines	Supported in applicable environments
Low-code pipeline building	Pipeline Builder
Code-based transformations	Yes
Data lineage	Yes
Ontology	Yes
Object relationships	Yes
Operational actions	Yes
Analytics	Contour, Quiver, Code Workbook and other tools
Application building	Workshop and additional developer tools
Machine learning	Yes
Generative AI integration	Through wider Palantir AIP capabilities
Access controls	Roles, Organisations, Markings and additional policies
Audit logging	Yes
APIs/SDKs	Yes
External compute integration	Supported for selected architectures

Foundry is continuously updated, so specific features can change over time. Palantir’s current documentation should be checked when planning a deployment.

Foundry is not a conventional off-the-shelf application

Businesses evaluating Foundry should understand that it is not comparable to buying a normal productivity application and giving every employee a login.

Its value depends heavily on implementation.

Organisations need to determine:

• What data should be connected
• How that data should be modelled
• Which workflows should be operationalised
• What security controls apply
• Who owns the pipelines
• Which applications users actually need

A weak underlying data model can limit the usefulness of everything built on top of it.

A successful Foundry deployment therefore requires both technical expertise and detailed understanding of the organisation’s processes.

Forward Deployed Engineering is part of Palantir’s approach

Palantir also places significant emphasis on Forward Deployed Engineering.

The company describes this as an approach where engineers work closely with customers and operational problems, with feedback feeding back into the wider product.

That implementation model is relevant because Foundry’s value is closely connected to the specific operational workflows built on top of it.

It also means organisations evaluating Palantir should consider implementation expertise and ongoing platform governance alongside software capabilities.

What is Palantir Foundry used for?

Foundry’s architecture makes it applicable across many enterprise scenarios.

Supply chain

Organisations can combine inventory, supplier, logistics and customer-order information into a common operational model.

Teams can then identify shortages, monitor supply risks or trigger changes through governed workflows.

Manufacturing

Factories can combine production, maintenance and quality information.

Equipment and production lines can be represented as Ontology objects, helping operational teams analyse conditions and act on problems.

Financial services

Foundry can integrate financial and operational datasets for analysis, risk management and governed decision workflows.

Access controls become particularly important when the environment contains sensitive customer or financial information.

Healthcare

Healthcare environments can use integrated operational data for planning and resource management.

Any deployment involving patient information requires strict governance, legal compliance and access controls.

Energy and utilities

Operational assets, maintenance information, demand and infrastructure data can be connected into a common model.

Logistics

Foundry can combine fleet, shipment, route and inventory information to support planning and operational decision-making.

Palantir says its architecture is currently used across more than 50 verticals, ranging from healthcare and manufacturing to energy, insurance and shipbuilding.

Where Foundry can become difficult

The platform’s scope is also one of its challenges.

An organisation needs enough operational complexity to justify creating a unified data and Ontology environment.

Foundry can involve:

• Significant implementation work
• Data-cleaning requirements
• Governance design
• Integration work
• Application development
• Employee training
• Long-term platform ownership

Companies should therefore begin with clearly defined operational problems rather than adopting Foundry simply because they want an enterprise AI or data platform.