As Palantir gains attention, domestic enterprises have begun to study its technical architecture, Ontology, and AIP platform capabilities. The market's focus has also gradually shifted from individual product capabilities to a more specific question: Palantir organizes data governance, business modeling, AI applications, and software deployment into a full-stack system; do Chinese enterprises possess the conditions to adopt this system?
Palantir's full-stack architecture has evolved through long-term complex projects, addressing long-standing enterprise challenges such as fragmented data, lack of business semantics, and the absence of closed-loop actions. Meanwhile, large Chinese enterprises typically already possess data platforms and business systems, and the construction methods for AI applications are rapidly changing. Studying Palantir requires understanding why its architecture is effective, as well as determining which platform boundaries stem from specific customer environments and development stages.
This article focuses on answering three questions: Why did Palantir form a full-stack technical system? What conditions must be met for this system to be applicable in the Chinese market? And how should Chinese enterprises recombine data, ontology, and Agent capabilities in the AI era?
The purpose of this article is to find a technical path more suitable for Chinese enterprises, beyond complete replication or outright rejection.
01. Palantir Is a Complete Technical System Centered on Ontology
According to Palantir's official website, the current standard architecture consists of Foundry, AIP, and Apollo: Foundry handles data operations and business development, AIP is responsible for connecting large models and supporting Agents and automation, and Apollo manages continuous deployment and operations. These three platforms together constitute what Palantir calls the enterprise operating system.
Ontology sits at the core of this architecture, transforming enterprise data into business objects such as customers, orders, and equipment, while connecting business logic with actions. Security governance runs through object access and action execution. Palantir further divides Ontology into Language, Engine, and Toolchain: Language is used to express business, Engine supports queries and state updates, and Toolchain handles development and governance.
Palantir is a technical system composed of data infrastructure, business expression, and AI applications. Foundry provides the data and computing foundation, while Ontology organizes enterprise data into continuously running business objects. AIP enables models and Agents to use these objects and related business capabilities, and Apollo ensures the continuous deployment and stable operation of the entire system.
The emergence of AIP has expanded Palantir's capability boundaries. Palantir's early advantages were concentrated in data integration, analysis, and complex decision-making. Currently, AIP covers model integration, Agent development, and operational evaluation, invoking enterprise data and business actions through Ontology.
AIP is built upon the foundation already established by Foundry and Ontology. Foundry provides governed data, Ontology provides business objects, rules, and action capabilities, and AIP enables large models and Agents to invoke these resources. Consequently, enterprises' previous investments in data governance and business modeling can continue to serve AI applications.
Determining the applicability of Palantir's full-stack architecture requires focusing on four conditions: whether the enterprise lacks a unified data foundation, whether business problems involve complex cross-domain collaboration, whether AI needs to enter core processes and drive actions, and whether business value can support long-term construction and continuous iteration. The more sufficient these conditions are, the more obvious the value of the complete platform; when an enterprise already has a mature data platform and a narrow application scope, it is more suitable to selectively introduce Ontology, Agent, or action governance capabilities.
02. Why Palantir Formed a Full-Stack Technical System
Palantir's full-stack system bears clear traces of evolution. Its early customers primarily came from the counter-terrorism and defense fields, where related tasks often faced issues such as fragmented data, strict security requirements, and complex system environments. Customers lacked a unified data foundation that could directly support business actions, so Palantir needed to complete data ingestion and processing before building analysis and applications.
What these customers needed was not just analytical results. Intelligence analysis, resource scheduling, and industrial operations all require the system to continuously reflect business status and translate judgment results into subsequent actions. To ensure the complete chain could run, Palantir gradually supplemented data services, logical computing, and workflow capabilities. Ontology connects these capabilities, enabling objects to invoke Functions and enter business processes through Actions.
From a technical relationship perspective, Ontology is also difficult to run independently of the complete platform. Business objects require a stable data supply, Functions need a computing environment, Actions need to connect with workflows and external systems, and permissions and auditing must cover the entire operation process. Thus, Palantir has formed a complete chain from data management to business operations.
Complex deployment environments have further expanded the platform boundaries. Palantir's software needs to run in the cloud, privatized environments, and edge nodes, with continuous upgrades without interrupting business. Apollo thus became part of the standard architecture, responsible for hosting the deployment and iteration of Foundry and AIP.
On-site practices continuously drive product growth. Palantir enters customers' business sites through FDEs, transforming specific tasks into data models, Ontology resources, and application capabilities. Recurring requirements in projects continue to be fed back to the core R&D team, gradually precipitating into platform functions.
iFenxi believes that Palantir's product form bears obvious historical traces; it is the result of starting from a data platform and gradually growing through long-term projects. Its true advantage comes from systematic integration and years of cross-industry practice.
AIP has also evolved along this path. Palantir allows large models and Agents to directly enter the data and business system composed of Foundry and Ontology. Models can utilize existing objects, rules, and Actions, and enterprises can also continue to use the original permission and auditing mechanisms.
From this perspective, Palantir's full-stack architecture has clear formation conditions: customer problems are complex enough, business value can support long-term investment, and the platform can penetrate core systems. The complete architecture serves continuous business outcomes in complex environments, rather than simply pursuing the quantity of capabilities.
03. Gaps Between the Chinese Market Environment and Palantir's Formation Conditions
When Chinese enterprises study Palantir, they first need to face the differences in existing enterprise environments. After years of IT infrastructure development, large enterprises typically already possess data warehouses, lakehouses, or data middle platforms. If an ontology project rebuilds a complete data ingestion and computing system, it is easy to duplicate existing platforms and will also increase data migration and system transformation costs.
iFenxi's research has found that, after years of IT infrastructure development, many Chinese enterprises already possess data aggregation and computing capabilities. When carrying out ontology projects, enterprises focus more on how to reuse the existing data foundation, supplementing it with business objects, rules, and AI application capabilities, while controlling the cost of duplicate construction. Palantir's early customers faced more issues of fragmented data and insufficient underlying capabilities; there is an obvious gap in the starting point of construction between the two types of markets.
Differences also exist in project procurement and construction methods domestically and internationally. Software projects in Chinese enterprises are usually advanced by department and annual budget, with data systems and business systems managed by different teams. Enterprise-level Ontology requires continuous unification of objects, rules, and permissions, and actual work will cross multiple organizational boundaries. If the pursuit of enterprise-wide coverage starts from the beginning, the project is prone to entering a long-cycle governance process.
The Action closed loop is also subject to practical constraints. Some core systems have not opened stable write-back interfaces, and high-risk industries also require retaining manual approvals. According to iFenxi's research, scenarios such as transportation and public safety usually complete simulations or generate plans first, with final operations still confirmed by business personnel. This means that Actions have implementation methods of varying depths in the Chinese market. A system that can drive plan generation, process flow, or manual decision-making can also form a business closed loop; direct write-back to production systems should not be the only judgment standard.
The scope of AI applications is also more dispersed than traditional data decision-making. Knowledge services and content generation focus on accurate context, process Agents focus on tool invocation, and industrial agents also need to connect real-time status with equipment systems. Different applications have different depth requirements for data, ontology, and Actions. If all scenarios rely on a complete full-stack platform, the construction cost will be higher than actual needs.
Although facing the above significant differences in the market environment, this does not mean that Palantir's full-stack architecture lacks applicable scenarios in China. For customers with weak data foundations, complex cross-domain collaboration, and strict security requirements, the complete system still holds value.
For enterprises that already possess mature data platforms or are only building point-to-point AI applications, a more reasonable approach is to retain key mechanisms such as Ontology, permission governance, and action closed loops, and then combine them with existing systems. Chinese enterprises need to determine the platform scope based on their own construction starting points, avoiding directly transforming Palantir's product boundaries into enterprise AI construction standards.
04. Recombining Data, Ontology, and Agent Capabilities in the AI Era
Facing Chinese enterprises with many existing systems and dispersed application requirements, a compositional architecture is more actionable.
The compositional architecture emphasizes clear capability boundaries, connecting various platforms through unified interfaces, and ensuring collaboration through governance mechanisms. Enterprises can retain the key mechanisms already verified by Palantir, while selecting specific products and construction scopes according to their own foundations.
Existing data platforms continue to undertake storage, processing, and data supply. The ontology connects existing systems through APIs, event streams, or data mapping, only supplementing necessary real-time processing capabilities. The construction focus shifts from centralizing all data to forming consistent business expressions, avoiding the ontology project evolving into a data platform engineering project again.
The ontology can precipitate relatively stable business objects, core rules, and permissions, providing a shared context for different models and Agents. Objects such as customers, orders, and equipment only need to be established once and can subsequently be used jointly by Q&A assistants, process Agents, and analytical applications. Even if the model or Agent framework changes, the business expressions already formed by the enterprise can still be retained.
Figure 4: Chinese Enterprises Are More Suitable for Organizing Data, Ontology, and Agent Capabilities Through a Compositional Architecture
According to iFenxi's research, the ontology architecture in the AI era can also further reduce upfront preset costs. Traditional methods require defining a large number of Functions and Actions in advance, while new implementation methods allow AI to generate computing logic and action plans based on tasks, which are then gradually precipitated through testing, auditing, and operational feedback.
Thus, the ontology mainly retains relatively stable business objects and relationships, while rapidly changing task logic is handed over to Agents and Skills for organization. Among them, Agents are responsible for understanding intentions and planning tasks, Skills are responsible for invoking algorithms and business tools, and existing business systems continue to undertake transaction execution.
According to iFenxi's research, some domestic industrial agent platforms use the ontology primarily to organize enterprise heterogeneous data, precipitating business logic and action methods in Skills. The construction process starts from specific problems, first clarifying the methods needed to solve the problem, and then reverse-determining the data the ontology needs to connect. This forms a compositional path different from Palantir's complete upward construction.
AI can also change the construction methods of the ontology. Models can identify objects from databases and business documents, propose relationship mapping suggestions, and generate rule drafts, with business experts responsible for confirming important definitions and high-risk operations. Domestic vendors have already attempted to complete object and relationship identification through AI, followed by confirmation by business personnel. Shifting from full manual modeling by experts to AI collaboration can lower the threshold for ontology construction.
At this stage, AI automatic modeling still has clear boundaries. According to iFenxi's research, AI can already identify objects, attributes, and relationships from structured data and business materials, with the generated results confirmed by business experts. Functions and Actions involve specific business rules and execution risks, and still need to be defined individually in combination with scenarios. Therefore, AI currently mainly improves initial modeling efficiency and has not yet completely replaced business experts.
Actions need to be graded according to risk. Low-risk operations can be executed automatically, medium-risk operations add rule verification, and high-risk operations retain approval by business personnel. For scenarios where core systems cannot be accessed temporarily, action suggestions or simulation plans can be generated first. Execution results still need to return to the business context to update status and evaluate effects.
The compositional architecture also needs to face broader AI applications. Knowledge and generative applications mainly use business semantics and permissions, process applications further invoke rules and tools, and industrial applications need to connect real-time data and equipment status.
Taking equipment operation and maintenance as an example, the ontology can decompose equipment and its components, and establish relationships between components, failure modes, and maintenance actions. On this basis, the system further connects sensor data, IoT alerts, and historical work orders, enabling AI to identify potential risks in combination with the current status of the equipment.
After discovering anomalies, AI can judge possible fault causes and generate inspection steps and maintenance plans. After necessary manual confirmation, the task enters the work order system, where on-site personnel complete the processing according to the guidance. The execution process and processing results continue to return to the system to update equipment status and determine whether the problem has been resolved.
In this scenario, the ontology is responsible for organizing equipment and operation and maintenance knowledge, AI is responsible for risk identification and plan generation, and business systems are responsible for work order execution. Equipment operation and maintenance extend from single analysis to risk discovery, task execution, and result feedback, reflecting the supporting value of the ontology architecture for production-level AI applications.
Composition will also bring new governance requirements. Object definitions, interface protocols, and permission models need to remain consistent; otherwise, modularization may form new system fragmentation. Chinese enterprises need to establish a public business context while allowing data platforms, Agent frameworks, and applications to evolve independently.
The implementation principles for Chinese enterprises can be summarized as: reuse the data foundation, precipitate the business context, flexibly organize AI applications, and incorporate action execution into unified governance. Such an architecture absorbs Palantir's core idea of moving from data to action, and can also adapt to the existing IT environment of Chinese enterprises and rapidly changing AI technologies.
05. Drawing on Formation Mechanisms to Redesign Implementation Methods
Palantir has proven that enterprise data can only truly enter core business when connected with business objects and action mechanisms. Ontology plays a key role in this, while Foundry and Apollo provide the data and operational foundation for it. AIP allows models and Agents to enter this system, expanding Palantir's capability boundaries in the AI era.
Meanwhile, Palantir's full-stack form is built on specific customer environments and long-term practices. Chinese enterprises already possess a large number of data platforms, and there are also differences in construction cycles and system openness; complete replication may add new platform burdens.
A more feasible path is to reuse the existing data foundation, build the ontology as a shared business context, and then connect different applications and business systems through Agents and Skills. Enterprises can start from high-value scenarios, gradually precipitate reusable capabilities, and control action depth according to risks.
Palantir provides a full-stack path verified by complex scenarios. Chinese enterprises need to absorb its problem-solving mechanisms and reorganize data, ontology, and Agent capabilities with a compositional architecture.