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Palantir makes enterprise software that turns an organization’s data into governed analysis, operational workflows and, where enabled, AI-assisted decisions. Its four principal platforms are Foundry, AIP, Apollo and Gotham. Foundry provides the data and workflow foundation; AIP connects AI models to that foundation; Apollo manages software delivery and deployment; and Gotham belongs to Palantir’s government and mission-oriented platform family.
These are not four standalone consumer applications. Palantir presents them as connected parts of an enterprise architecture, with shared data, applications, security controls and governance. The practical question is therefore not simply “Which Palantir app do I buy?” but “How would these platforms connect information, decisions and actions in my organization?”
What Palantir does, in plain language
Palantir supplies software for organizations that need to combine data from many systems, model how their operations work, and put analysis or AI into repeatable processes. A manufacturer might connect supply, production and logistics information; a healthcare organization might coordinate sensitive operational data; a government team might use a mission-oriented environment. The exact applications depend on the customer, data and permissions configured for that deployment.
Palantir’s documentation describes a stack rather than a single database or chatbot:
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- Foundry is the data operations and workflow-development foundation.
- AIP supplies tools for connecting generative AI models to enterprise data and operations.
- Apollo manages software delivery and deployment infrastructure, including upgrades.
- Gotham is another principal platform in Palantir’s government and mission-oriented portfolio.
Palantir reported $4.5 billion in revenue for the year ended December 31, 2025 in its FY2025 Form 10-K. That is a company financial result, not a measurement of how effective the software is for any particular customer. The filing identifies Gotham, Foundry, Apollo and AIP as the company’s four principal platforms (Palantir FY2025 Form 10-K).
How the four platforms fit together
Palantir’s architecture documentation calls Foundry the foundational data operations platform, AIP the generative AI platform and Apollo the continuous delivery platform. A useful way to understand the relationship is: Foundry organizes information and operational logic, AIP applies AI within approved workflows, and Apollo delivers and maintains the software across its operating environments. Gotham is a separate principal platform in the same product family, oriented toward government and mission work.
| Platform | Primary job | What it contributes | How it relates to the others |
|---|---|---|---|
| Foundry | Data operations and workflow development | Data management, logic authoring, Ontology development, analytics, applications and workflows | Provides the operational data and application foundation that AIP can use |
| AIP | Generative-AI-enabled operations | Tools for connecting models and creating AI-backed functions, agents, workflows and applications | Uses governed enterprise context and can place AI capabilities inside Foundry workflows |
| Apollo | Software delivery and deployment management | Infrastructure management for Foundry and AIP services, continuous delivery and upgrade orchestration | Delivers and maintains platform services in their target environments |
| Gotham | Mission-oriented and government platform | A principal Palantir platform identified in the FY2025 filing | Belongs to the wider platform family; the cited materials do not provide a feature-by-feature comparison with Foundry |
Palantir describes the platform foundation as shared data, compute, security and lineage. Its application and developer materials also describe analytics, application building, workflow development and custom development through the Ontology SDK. The SDK documentation identifies TypeScript and Python package routes (platform overview, AIP, Foundry and Apollo architecture, app-building overview).
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What Foundry is used for
Foundry is the part of the stack that turns disparate enterprise information into a usable operational environment. Palantir describes capabilities for ingesting and managing data, authoring logic, developing an Ontology, analyzing information, building applications and creating workflows.
Data management and lineage
Foundry can provide a common environment for data from multiple operational systems. Lineage helps users understand where information came from and how it was transformed. That matters when an analyst, operator or auditor needs to trace a value back to its source rather than rely on an unexplained dashboard number.
Analytics and applications
Users can analyze information and build applications on the shared platform foundation. The result can be an operational interface rather than a report that someone reads and then re-enters into another system. Palantir’s documentation describes both visual application-building tools and custom development using the Ontology SDK.
Workflow development
Foundry supports workflows that connect analysis to an action: for example, identifying an exception, assigning it to a responsible person, recording a decision and updating an operational system. Which actions are available depends on the customer’s configuration, permissions and integrations.
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The Ontology: Palantir’s model of data, logic and action
In Palantir’s FY2025 filing, the Ontology is described as going beyond a traditional data representation by integrating data, logic and actions and allowing users to build interconnected workflows. It is Palantir’s software model for representing an organization’s operational world; it is not a universally accepted industry definition of “ontology.”
A conventional data store might contain tables for orders, products and locations. An Ontology-based application can represent the operational concepts those tables describe, the rules that relate them and the actions users are allowed to take. An application can therefore work with a concept such as “delayed shipment” or “maintenance event,” rather than forcing every user to reason directly in terms of disconnected rows and columns.
A practical example
Suppose a company receives a warning that a component will arrive late. A Foundry implementation could connect supplier records, inventory levels, production schedules and customer commitments. Logic can identify which orders are at risk. The workflow can then present permitted actions—such as reallocating stock, changing a schedule or escalating a decision—to the appropriate people. The Ontology is the layer that links the underlying records and rules to those operational objects and actions.
This does not mean the software automatically knows an organization’s processes. The customer must define data connections, business logic, object relationships, permissions and available actions. The value of the model depends on the quality and currency of those configurations.
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AIP is Palantir’s generative-AI platform. Palantir describes tools for connecting AI models and creating AI-backed functions, agents, workflows and applications. In practical terms, AIP is intended to let an organization use language or other model capabilities in the context of governed enterprise data and operational processes, rather than treating AI as an isolated chat window.
Connecting models to enterprise context
An AIP application can be designed to retrieve relevant organizational information, apply instructions or business logic, and return a result that fits a defined workflow. The available models, connectors, actions and controls depend on the customer’s deployment. Palantir’s overview states that feature availability can change or differ by customer, so a description of AIP should not be read as a promise that every installation includes every capability (Palantir’s platform architecture documentation).
Functions, agents and workflows
Palantir’s documented AIP concepts include:
- AI-backed functions that perform defined tasks using approved context.
- Agents that can be designed to carry out multi-step work within configured boundaries.
- AI-enabled workflows that combine model output with human review and operational actions.
- Applications that expose these capabilities through an organization-specific interface.
These terms describe product capabilities, not an assurance of autonomous decision-making. A responsible deployment still needs clear instructions, access controls, evaluation, human oversight where appropriate and a way to handle uncertain or incorrect outputs.
What Apollo does
Apollo is Palantir’s continuous delivery and deployment-management platform. The company describes it as managing infrastructure for Foundry and AIP services and orchestrating upgrades. Its role is operational: getting software to the environments where it must run and keeping those services updated.
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Where Gotham fits
Gotham is one of the four principal platforms named in Palantir’s FY2025 filing and is associated with the company’s government and mission-oriented portfolio. The available materials establish its place in the product family, but they do not provide enough neutral, feature-level information to present Gotham as simply “Foundry for government” or to produce a reliable side-by-side specification table.
That distinction is important: Foundry’s documented role is data operations, Ontology development, analytics and workflow building, while Gotham should be understood as a separate principal platform whose detailed capabilities and deployment context depend on the mission and customer.
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How a Palantir workflow can move from data to action
A typical implementation is configured around the organization’s own systems and operating rules. The following sequence explains the architecture without assuming that every customer uses every step:
- Connect source systems. Bring relevant operational data into the platform and document its provenance. Sources might include business applications, sensors, files or other approved systems.
- Define the operational model. Use Foundry to represent important entities, relationships, rules and permitted actions through the Ontology.
- Apply security and governance. Configure access controls so people and services can see or change only what their roles permit. Preserve lineage and audit information for later review.
- Build analysis and applications. Create views, calculations and interfaces that help users understand current conditions and make decisions.
- Design the workflow. Specify triggers, assignments, approvals, notifications and system updates. Keep consequential actions explicit rather than treating a model response as an automatic command.
- Add AIP where useful. Connect an approved model to relevant context and define the functions, agent behavior or workflow steps it may use.
- Test and release. Validate data quality, permissions, model behavior and action boundaries, then deliver the software through the customer’s operating environment with Apollo.
- Monitor and improve. Review outcomes, data freshness, audit records and user feedback; revise the model, logic or workflow as the operation changes.
Industries and sensitive-data settings
Palantir’s security documentation says its platform is used by healthcare providers, financial institutions, utility providers, manufacturers, telecommunications companies, airlines and pharmaceutical companies. The same documentation describes sensitive contexts that can include financial data, personal information, protected health information, controlled unclassified information and classified government data (Palantir security overview).
Those statements describe the environments Palantir says its software is designed to support; they are not an independent certification or a guarantee that a particular deployment satisfies every legal, contractual or regulatory requirement. A buyer must still assess applicable obligations, architecture, identity controls, retention policies, model governance and incident processes.
| Operating context | Potential platform use | Questions to resolve |
|---|---|---|
| Commercial operations | Coordinate supply, manufacturing, sales, service or logistics workflows | Which systems are authoritative, and which actions may the software execute? |
| Healthcare and pharmaceuticals | Analyze operational information and coordinate work involving sensitive records | How are protected data, consent, access and audit requirements implemented? |
| Financial services | Connect risk, customer, transaction and operational processes | How are segregation of duties, explainability and retention handled? |
| Government and missions | Support mission-oriented analysis and workflows, including environments with controlled or classified information | What deployment, clearance, sovereignty and authorization constraints apply? |
Customer examples: useful evidence, not a guarantee
Palantir’s corporate homepage publishes statements from named customer representatives. They are evidence of what those customers report about their deployments, not independent audits or predictions for every organization.
Walgreens VP of Product Pharmacy Jeff Hoffman is quoted as saying: “We started with the goal of piloting 10 stores within about six months. However, leveraging Foundry and AIP, we began to see real promise quickly in composing AI-powered, end-to-end workflows that allowed us to get to about 4000 stores within eight months.” This is a customer statement published by Palantir, so the figures should be read in that context (Palantir corporate homepage).
The same homepage lists other customer-reported figures, including General Mills’ reported savings of about $40,000 per day, Sompo’s reported $60 million improvement in profit over three years, and Fujitsu’s reported $9 million annual cost reduction within three months. These figures use different definitions, time periods and business contexts. They are not comparable measures and should not be combined into a general return-on-investment claim.
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Security, oversight and operational limits
Palantir’s materials describe access controls, lineage and auditability for sensitive work. These capabilities can help an organization govern who sees information, understand how data was transformed and review actions taken in a workflow. They do not remove the need for sound identity management, data stewardship, secure integrations and operational procedures.
- Data quality: AI and analytics cannot correct missing, stale or contradictory source information without explicit processes to detect it.
- Permissions: A model should receive only the information and action authority required for its task.
- Human accountability: High-impact decisions may require review, approval and an auditable explanation of what happened.
- Model uncertainty: Generated text or recommendations can be wrong, incomplete or inconsistent; test them against representative cases.
- Change management: Business rules, source systems and model behavior evolve, so workflows need ongoing monitoring.
No independent security review, comparative effectiveness test or industry-wide outcome statistic is established by the cited materials. Vendor-documented controls should therefore be treated as capabilities to evaluate in a specific architecture, not as a blanket security or performance guarantee.
Cost, requirements and availability
The cited documentation does not provide a standard public price list for Foundry, AIP, Apollo or Gotham. Enterprise cost will depend on the deployment, data sources, users, environments, integrations, governance requirements and enabled capabilities. A serious evaluation should request a written scope that separates platform access, implementation work, infrastructure, support, model usage and any usage-based charges.
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- Authoritative source systems and data owners
- Identity, role and access-control requirements
- Critical workflows and the actions they must support
- Data residency, security and regulatory constraints
- Technical teams responsible for integrations and applications
- How AI outputs will be evaluated, reviewed and logged
- Measures of success that can be checked independently of vendor marketing claims
How to decide whether the platform family fits
Palantir is most relevant when the problem involves several data sources, complex operational rules and a need to connect analysis to action under strict governance. It is less useful to approach the software as a simple dashboard, a generic chatbot or a plug-in that automatically fixes fragmented processes.
Questions for an evaluation
- Can the organization describe one high-value workflow in terms of inputs, decisions, approvals and measurable outcomes?
- Are the source systems accessible, sufficiently accurate and legally usable for the intended purpose?
- Which users need read, write, approval or administrative permissions?
- What must remain human-controlled, and what actions—if any—may software trigger?
- How will the team test AI behavior, monitor drift and investigate an incorrect result?
- Can the deployment meet required security, residency, availability and audit obligations?
- What implementation skills and long-term operating ownership are available?
The answers should be demonstrated in a bounded pilot with representative data and a documented baseline. Customer quotations can illustrate possible outcomes, but they cannot substitute for measurements from the organization’s own process.
Bottom line
Palantir’s core business is enterprise software for connecting data, operational logic, workflows and AI. Foundry is the data and workflow foundation, AIP brings governed AI capabilities into that environment, Apollo delivers and maintains the software, and Gotham occupies a separate mission-oriented place in the four-platform portfolio. The practical value depends on the quality of an organization’s data, Ontology, integrations, controls and operating processes—not on the platform names alone.
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