Over the past year, Palantir has been pushed back to the center of the enterprise AI stage, becoming a key reference for AI's transition from "being able to talk" to "being able to act." Questions from the Chinese market have followed: Who is the Chinese Palantir? In response, iFenxi officially released the "2026 iFenxi China Market Palantir Implementation Progress and Trend Research Report" on July 8.
The report points out that most Chinese enterprises are not suitable for building a full-enterprise heavy Ontology in one step; an incremental Ontology is the realistic path. The real competition lies not in who can replicate Palantir, but in who can define the action layer of enterprise AI.
Scan the QR code at the end of the article to get the "2026 iFenxi China Market Palantir Implementation Progress and Trend Research Report"
Over the past year, Palantir has been pushed back to the center of the enterprise AI stage. In the US market, it is no longer just a software company with intelligence, defense, and government colors, but is increasingly understood as a sample of enterprise AI implementation. Founded over twenty years ago, Palantir uses Ontology and AIP to connect data, business objects, permissions, processes, tool invocations, and action audits, enabling AI not only to answer questions but also to enter real operations. There are two key concepts here.
Ontology is not a static knowledge graph, but an organization of business objects such as orders, equipment, suppliers, work orders, inventory, customers, employees, and assets, along with their relationships, permissions, and executable actions. AIP, built upon this system of business objects and permissions, allows AI to invoke tools, trigger processes, and participate in decision-making and execution in a controllable manner. Consequently, the Chinese market has also begun to ask: Who is the Chinese Palantir? This question seems very attractive, but the three more specific questions truly worth answering are:
First, what kind of capability from Palantir do Chinese enterprises actually need?
Second, why is this capability so difficult to replicate exactly in China?
Third, if it cannot be copied directly, what kind of localized path should the Chinese market take?
iFenxi believes that what the Chinese market truly lacks is not a Palantir replica, but an infrastructure that enables AI to enter business actions. Chinese enterprises indeed need the capabilities represented by Palantir the most, but the Chinese market is the least suitable for replicating Palantir exactly. The so-called Chinese Palantir will not look exactly like Palantir; it is more likely to grow out of localized data governance, a lightweight semantic layer, industry scenarios, and controllable action closed loops.
01. Action is the Core of Palantir
Over the past year, Palantir has been pushed back to the center of the enterprise AI stage. To understand this, we must first extract Palantir from several common misconceptions. Palantir is not traditional BI, nor is it a knowledge graph with a new name. It certainly has data ingestion, data governance, analytics, visualization, and AI capabilities, but these are not its most critical aspects.
Palantir truly solves a deeper problem: when an organization is highly complex, with data scattered across different systems, permissions distributed across various departments, and processes spanning IT, business, finance, supply chain, field personnel, and compliance teams, how can AI understand this organization and participate in actions within controllable boundaries?
Action is the core of Palantir's Ontology and AIP. In simpler terms, Palantir first maps data scattered in systems into computable business objects, then defines the relationships, permissions, and callable functions among these objects, and finally connects executable actions with audit records.
Ordinary data platforms mostly answer "where is the data," "how to calculate metrics," and "how to view reports," whereas Palantir takes an extra step forward: it enables the system to understand real business objects, perform analysis based on problems, trigger execution actions, and record actions for auditing and accountability. This also explains why more and more data vendors are re-discussing the semantic layer. SQL solves data accessibility, while the semantic layer in the AI era must further solve data comprehensibility, analyzability, and actionability.
As the semantic layer moves further up, it is no longer just about metric definitions and query translation; it will gradually incorporate business system APIs, Action invocations, permission management, and audit chains, becoming the support layer between decision-making and action. Therefore, the key to Ontology is not "whether it can draw an object relationship diagram." Many technologies can draw diagrams, and many systems can model. The key to Palantir is whether it can enter action after modeling. For example, if the system detects a supplier's delayed delivery, it does not just generate a risk alert, but can continue to track affected materials, orders, production lines, customers, and revenue risks, generate alternative solutions, submit the solutions for human approval, write back to ERP, WMS, or MES after approval, and finally leave a complete record of decisions and executions. This is the watershed between Palantir and ordinary AI+BI: AI+BI solves "seeing the problem," while Palantir aims to solve "solving the problem."
02. What Chinese Enterprises Need Most is This "Action Layer"
This is also why the Chinese market is interested in Palantir. Over the past two years, the easiest directions for enterprise AI implementation have been relatively clear: data querying, knowledge bases, report generation and review, etc. These scenarios are valuable and indeed improve efficiency, but most of them have not penetrated into execution and action.
The truly higher-value questions lie in the next step: after detecting inventory anomalies, can it trigger transfers? After detecting production line risks, can it coordinate procurement, production, logistics, and finance? After detecting customer churn risks, can it assign intervention tasks to the sales system?
These questions cannot be solved by ordinary data querying; they require AI not only to answer but also to understand internal objects, relationships, permissions, processes, and responsibility boundaries within the enterprise. This is precisely the pain point of Chinese enterprises: the lack of an action layer. Enterprises easily turn AI into a "talking data analyst," but find it hard to turn it into a "digital employee capable of participating in operations."
The action layer is not an additional layer outside data governance; rather, it will force the upgrade of data governance, permission management, and responsibility mechanisms. iFenxi found in interviews that in the past, data governance was often the concern of data management departments, reporting teams, or business analysis teams. Now, because enterprises want AI to directly read data, use data, and invoke systems, senior management has begun to pay more direct attention to data quality, permissions, compliance, timeliness, and business definitions. The reason is simple: if AI cannot correctly understand data, cannot fetch data within permission boundaries, and cannot explain where results come from, it cannot enter core business.
More specifically, the data foundation in the AI era must prove at least four things: whether AI can identify the meaning of data within the enterprise and know the definition differences of the same metric across different departments and scenarios; whether AI can fetch data, compute, and provide feedback fast enough; whether AI's access to and invocation of data are compliant and controllable; and whether the data quality used by AI is reliable, and whether results can be traced, reviewed, and audited.
These requirements may seem basic, but they are precisely the prerequisites for the Chinese Palantir to move from a demo to production. Many data querying, insight, and reporting products can already be implemented, but once it enters Action, the difficulty immediately increases. A technical demo is not necessarily difficult; what is truly difficult is the issue of responsibility in the production environment: if the system automatically adjusts production lines, procurement, logistics, and financial processes, continuous correctness is certainly of huge value; but if even one error causes a loss, who bears the responsibility? Therefore, Chinese enterprises do not lack a need for Palantir. On the contrary, what Chinese enterprises need most is the "AI action layer" behind Palantir.
03. Why is the Chinese Market the Hardest to Replicate Palantir?
The problem is that the more it is needed, the less it means it can be copied directly. Palantir is hard to replicate not because of Ontology technology, but because the systemic conditions behind Palantir are truly difficult to replicate.
Palantir's success is not just product success, but the result of the joint formation of product, customers, delivery, organization, and scenarios. Many of the customers it serves have sufficient budgets and organizational capabilities, and are willing to hand over complex problems to external platforms and FDE (Forward Deployed Engineer) teams to solve together. The realistic conditions in the Chinese market are very different.
Figure 1: Why the Chinese Market is the Hardest to Replicate Palantir
First, the foundation of data governance is generally immature. A Palantir-style Ontology needs to map data from different systems into unified business objects, but a large number of Chinese enterprises are still in the stages of data asset inventory, master data governance, metric definition unification, and data quality improvement. If data has not been governed well, directly building a full-enterprise heavy Ontology will easily turn into a new round of information silos. At the same time, enterprise systems are shifting from "humans using software" to "AI using data and tools," which will continue to force data governance to redesign interfaces and permissions. If the underlying platform cannot provide comprehensible, callable, and traceable data and tools for AI, no matter how strong the upper-layer applications are, they can only stay at the demo stage.
Second, unifying semantics is difficult. The same customer, order, revenue, and inventory may have inconsistent definitions in sales, finance, supply chain, production, and regional organizations. Ontology is superficially about building objects, but essentially about unifying organizational language. Technical teams can build tables, but they cannot decide the definitions for the enterprise.
Third, there is a lack of process owners. The value of Palantir lies in cross-departmental action, but many enterprises do not have clear owners for cross-departmental processes. If a risk is passed from sales to supply chain, from supply chain to procurement, and then to finance, who is ultimately responsible for the result? Without a process owner, it is difficult to close the loop for Action.
Fourth, permission and responsibility boundaries are complex. What data can AI see? What fields can it modify? What processes can it trigger? These are not issues of model capability, but issues of corporate governance. As long as AI enters enterprise actions, there must be review, confirmation, recording, and a responsibility chain; otherwise, when AI makes a mistake, one cannot simply say "this is AI's responsibility."
Fifth, IT procurement in China is more project-oriented. Palantir's model emphasizes continuous evolution, platform-based subscription, and long-term co-creation, whereas many Chinese enterprise customers are still accustomed to project initiation, contract scope, functional acceptance, and milestone delivery. This project-based system is suitable for delivering systems, but not for cultivating a continuously evolving Ontology.
Sixth, the FDE model is difficult to replicate exactly. Palantir's FDEs are not ordinary implementation consultants, but a frontline engineering delivery mechanism with high permissions, strong on-site presence, and continuous co-creation. It requires engineers to enter the customer's real business scenarios, access real data and real processes, quickly build prototypes, abstract the customer's specific problems into platform capabilities, and then precipitate these capabilities back into the product.
The bottleneck in the Chinese market is not "whether people can be stationed on-site," but whether, after stationing, they can obtain sufficiently real problems, sufficiently complete data, sufficiently clear business owners, and sufficiently continuous iteration space. Many enterprise customers can accept on-site project implementation, but may not necessarily accept external teams deeply engaging in core operations for the long term; many projects can do prototype verification, but contracts, budgets, and acceptance mechanisms may not support continuous co-creation; many vendors have engineering teams, but may not simultaneously possess industry experience, business abstraction capabilities, and platform precipitation capabilities. Therefore, Chinese vendors are not unable to create Ontology demos, but find it difficult to get customers to hand over core business actions to the system. This is the biggest fault line in replicating Palantir.
04. Chinese Local Vendors are Mostly Still Just Partial Components of Palantir
This does not mean there are no opportunities in China. iFenxi's research shows that a large number of practices approaching Palantir's capabilities at different levels have already emerged in the Chinese market; it's just that these capabilities are mostly scattered among different types of vendors and in different scenarios, and have not yet formed a complete Palantir model.
Domestic related capabilities are mainly distributed among several types of vendors: cloud and data platform vendors, who excel in data governance, data development, and AI platforms; knowledge graph and semantic intelligence vendors, who excel in entity relationships, industry knowledge, data fusion, and enterprise knowledge precipitation; BI and metric platform vendors, who excel in metric systems, trusted data analysis, and structured data services; and vertical industry vendors, who are closer to real business objects, industry knowledge, and scenario processes.
From iFenxi's recent series of interviews, it can be seen that the technical paths of local vendors are diverging. Some integrate Ontology capabilities as part of an AI application platform, forming a chain from data cleaning, information extraction, Ontology generation to workflow orchestration; some build knowledge graphs and semantic layers into industry knowledge foundations; some start from BI and metric systems, turning trusted structured data into a callable and interpretable data foundation; and others enter from vertical scenarios such as equipment and facilities, industrial chains, and risk control, first building industry objects and relationships, and then extending to the application layer. These practices show that the Chinese market does not lack local supply of Palantir-style capabilities, but currently it is more like having different modules of Palantir, rather than a complete Palantir having emerged.
The gaps are mainly in four areas:
First, there is no stable connection between data governance, Ontology construction, and business objects;
Second, unstructured knowledge has not been sufficiently precipitated into a computable semantic layer;
Third, analysis results have not stably entered the execution phase;
Fourth, permission control, auditing, rollback, and responsibility chains are still far from production-level requirements.
Therefore, China now does not lack "parts that look like Palantir"; what it truly lacks is the platform capability to string together data governance, business objects, permissions, and action closed loops. Precisely because these capabilities are still scattered among different vendors and in different scenarios, the Chinese Palantir will not start from a complete large platform, but will first appear in the form of partial modules, vertical scenarios, and incremental capability combinations.
05. Incremental Ontology is the Realistic Path for the Chinese Palantir
If Palantir cannot be completely copied, how should the Chinese market proceed?
An important judgment is: most Chinese enterprises are not suitable for building a full-enterprise heavy Ontology right from the start. A heavy Ontology is not simply divided by customer type. It does not mean that central state-owned enterprises, financial institutions, and operators are naturally suitable for a heavy Ontology, nor does it mean that private enterprises are definitely unsuitable.
The real judgment criteria are scenario complexity, data governance foundation, organizational collaboration capability, responsibility boundaries, budget cycles, and business stability. A heavy Ontology requires enterprises to first establish a relatively complete data, semantic, permission, and knowledge system, and then map core business objects, processes, and actions into it, which means long-term investment, high organizational collaboration, and continuous governance. Even large customers with relatively stable budgets may not be willing to pay for a platform engineering project with delayed value for the long term.
For most enterprises, business definitions are still changing, the data foundation is not yet unified, and process owners are not always clear. If a full-enterprise heavy Ontology is built from the beginning, it will easily turn into a new round of platform engineering with long cycles, heavy investments, and slow results. Therefore, for most Chinese enterprises, a more realistic path is not a heavy Ontology, but an incremental Ontology.
The so-called incremental Ontology starts from specific high-value scenarios and gradually precipitates a lightweight semantic layer, industry knowledge, and controllable actions. Its basic idea is: with data governance as the foundation, starting from a scenario-level lightweight semantic layer, connecting key business objects, industry knowledge, workflows, and controllable Actions; as scenario verification succeeds, gradually precipitating industry templates, reusing object models, expanding permission and audit capabilities, and forming a sustainable operational capability system under the requirements of Xinchuang, localized deployment, and security compliance. This path has several principles.
Figure 2: Principles for Implementing Incremental Ontology
First, do not start with a full-enterprise Ontology, but start with a high-value scenario. For example, supply chain risk, equipment maintenance, anti-fraud, production scheduling optimization, and urban emergency response. Only when the scenario is specific enough will the business objects and actions be clear enough.
Second, do not pursue a large and all-inclusive object graph first, but build key objects first. Orders, customers, equipment, assets, work orders, suppliers, inventory, and contracts—these objects are more important than an abstract enterprise knowledge system. The system must first understand what these objects are, who they are related to, and what actions can be performed.
Third, do not pursue fully automated execution first, but implement controllable Actions first. Low-risk actions can be automated, while high-risk actions must be manually confirmed. The true implementation of enterprise AI is not to let machines take over business overnight, but to gradually expand the scope of action under human approval, permission control, and audit tracking.
Fourth, do not just generate reports and query data, but gradually integrate approval, write-back, and auditing. If the system only outputs analysis conclusions forever, it is just an analysis tool. Only by entering processes, approvals, system write-backs, and auditing does it begin to approach an operational system.
Fifth, customers must not be allowed to rely on external experts for the long term. Localized FDEs must be tied to the capability building of the customer's internal team; otherwise, the Ontology platform will become a new consulting project, dazzling in the short term but difficult to scale in the long term.
Sixth, bring the effects forward into the delivery methodology. iFenxi found in recent interviews that the biggest difference between AI projects and traditional software projects is that customers are unwilling to just look at blueprints and PPTs, but want to see effects with real data as early as possible. What Chinese vendors should learn most from Palantir Bootcamp and FDE is not copying job titles, but running through customer needs, real data, scenario value, technical capabilities, and business closed loops in the early stages of the project. Customers will only have the confidence to continue investing after seeing the effects first. From the perspective of scenario selection, an incremental Ontology is more suitable for entering from scenarios with high complexity and high value density. Such scenarios usually have several characteristics: complex data, complex processes, complex permissions, high cost of errors, and high value of actions.
Conversely, ordinary data querying, ordinary reports, lightweight customer service, and single-point knowledge bases do not require Palantir-style heavy capabilities. Equipment and energy management is currently one of the easiest scenarios to clearly explain this path. It naturally possesses objects and action chains: equipment, components, pipe networks, work orders, sensors, failure modes, maintenance strategies, control strategies, and on-site execution, which can be organized into the same set of business semantics.
iFenxi has seen in interviews that some vendors have already started from physical semantic models, connecting equipment objects, operational data, IoT alarms, work order systems, and decision-making systems, enabling the system to perform risk assessment, energy review, control strategy recommendations, and work order triggering.
Scenarios such as supply chain, high-end manufacturing, financial risk control, government emergency response, and energy and power are also more suitable for an incremental Ontology. Their common characteristics are clear objects, complex relationships, explicit actions, and important responsibility boundaries. As long as the scenario is specific enough, business objects and Actions can be defined, and value is also easier to verify in the early stages.
06. The Real Competition is Who Can Define the Action Layer of Enterprise AI
China will not simply produce an exact proportional replica of Palantir. Palantir's formation has very special historical conditions: starting with government and defense customers, honing capabilities in high-security, high-complexity, and high-consequence scenarios, delivering deep services through FDEs, and then migrating to commercial customers.
These conditions are difficult to replicate exactly in China, but the core ideas behind Palantir deserve serious attention: enterprise AI cannot just stay at Q&A, writing, reporting, and chatting; it must ultimately enter the real business world, understand business objects, respect permission boundaries, invoke deterministic tools, trigger controllable actions, and be responsible for every execution.
iFenxi believes that the Chinese Palantir is more likely to be a hybrid form. Its underlying layer consists of domestic data governance, data security, Xinchuang adaptation, and localized deployment capabilities; the middle layer is a lightweight business object layer, industry knowledge graph, and structured semantic layer; the upper layer consists of workflows, controllable Actions, and audit chains oriented towards specific scenarios; and the delivery side requires a localized joint co-creation mechanism, rather than simply relying on long-term on-site external experts. It will not start by replicating Palantir's full-chain capabilities, but will grow out of high-value industry scenarios one by one; it will not be a full-enterprise operating system from the beginning, but will first become the action layer for certain key processes; it will not completely rely on external elite teams, but must help customers form continuous operational capabilities internally.
Therefore, what is truly worth paying attention to is not who is best at talking about the "Chinese Palantir," but who can answer more specific questions: whether the Ontology fully understands business objects and knows action permissions; whether the system can write back to business systems like ERP and penetrate the execution layer; whether high-risk actions have approval, rollback, and responsibility chains; whether customers can expand from a single-point scenario to multiple business processes; and whether the delivery method can bring effects forward, rather than proving value only at the end of the project. These questions are more important than "who is the Chinese Palantir."
The first half of enterprise AI is about letting machines answer questions; the second half is about letting AI participate in actions. Whoever can define this action layer is the one who truly approaches the Chinese Palantir.
Scan the QR code to get the "2026 iFenxi China Market Palantir Implementation Progress and Trend Research Report"