Is Palantir Airbus entering China again? Haizhi Technology (02706) integrates graph models to open the trillion-dollar door of intelligent aerospace manufacturing.

On March 17th, the high-level interaction dynamic between Commercial Aircraft Corporation of China (COMAC) and Haizhi Technology (02706) has triggered a strong market response in the domestic AI industry circle and high-end manufacturing field. According to information released by Haizhi Technology, the Party Secretary and Chairman of COMAC, He Dongfeng, personally led the core business and technology team to visit Haizhi Technology. The two sides focused on the core innovation of knowledge graph and large-scale model integration (graph-model fusion) technology, engaging in in-depth exchanges on key topics such as the application of technology in complex scenarios in the aviation industry and the coordinated localization of domestic technology. They also reached a high degree of consensus on the cooperation direction and implementation path in the field of intelligent manufacturing, injecting new possibilities for the digitalization and intelligence upgrading of the entire lifecycle of domestically-produced large aircraft. In our view, this may be a highly symbolic event in the development history of China's industrial AI - reminiscent of the industry milestone twenty years ago when Palantir partnered with Airbus. The resolution of the "Who is the Chinese version of Palantir" discussion seems to be gradually emerging. Palantir's battle with Airbus: Ontology engineering is the ultimate key to penetrating complex industrial systems. Understanding the milestone significance of COMAC's visit to Haizhi Technology requires an understanding of the legendary story of Palantir and Airbus - this was not only Palantir's consecration march from defense industry into the commercial market, but also the ultimate proof of the value of ontology engineering in the high-end manufacturing field. In 2015, Airbus faced the most severe crisis since its establishment. Its most advanced wide-body aircraft, the A350, was deeply mired in a "production hell." Each aircraft consisted of over 5 million parts, involving hundreds of suppliers globally, customs logistics across dozens of countries, and over a hundred assembly processes. The entire supply chain system was fragmented into 25 disconnected "digital fortresses." At that time, Airbus was completely overwhelmed by three insurmountable anxieties: procurement managers did not know which parts being discontinued would halt the assembly line, assembly line supervisors could not locate rivets that were supposedly "in stock" but missing, and financial directors were unclear on whether urgently airlifted materials could offset the exorbitant delayed delivery penalties. Airbus faced a potential risk of up to $1 billion in order defaults. Traditional ERP and data warehouse solutions were deployed one after another but were unable to address the core pain points - these solutions could only record what had happened but were unable to link the relationships between events, let alone anticipate the chain reactions triggered by changes in variables. Enter Palantir - rather than introducing more complex large-scale models or building larger data warehouses, they did one thing: they constructed a complete, computable "ontology" system for the entire A350 supply chain. Palantir's engineers did not initially touch data; instead, they delved deep into production lines, procurement, finance, quality inspections, and all other processes to complete the first step of ontology construction: embracing chaos and excavating a rich knowledge mine from experiential intuition. They began by using the anxieties of procurement managers, the anger of assembly line supervisors, and the sighs of financial directors - all the chaos and fragmented frontline business experiences - as a starting point for consensus building, rather than rushing to use data tools to smooth out differences. The second crucial step in ontology engineering is consensus forging, which translates the real world into a universal language understandable to AI. The focus of "ontology" is not the data itself but the "relationships between data." For Airbus, the supply chain world was broken down into two core ontologies: the first layer, the elemental ontology, defined the basic nouns of the world, structuring all five core elements of aviation manufacturing - people, machines, materials, methods, and environment. It clearly defined the entity properties of every component, every workstation, every supplier, and crucially abstracted the relationships, dependencies, and substitution logic between them. The second layer, the business ontology, defined the verbs of the world, translating the entire flow of procurement, transportation, warehousing, quality inspection, assembly, and delivery into actionable business operations. By building these two layers of ontology, Palantir transformed Airbus's supply chain from a jumble of messy Excel and ERP data into a huge, living, computable "business graph." When this ontology construction was complete, miracles happened - this was the third step: ontology projection, using consensus to dissolve explanations. When a procurement manager received a notification of a delayed delivery of titanium alloy fasteners from a French supplier, old-world responses that required hours of interdepartmental meetings and countless spreadsheets to barely produce a solution were instantly handled by AI in the Palantir system. Along the chain of relationships in the ontology, AI completed a comprehensive impact assessment, matched alternative materials, and calculated costs and risks, offering multiple executable decision options. The entire process was almost devoid of "explanations" - managers did not need to ask AI "why these options," as each option was described in the shared ontology language (cost, time, risk, delivery date), consensus was already embedded in the question and answer. This is Palantir's ultimate capability: using an ontology system to completely eliminate information friction and decision black boxes within the enterprise. Ultimately, this solution significantly increased the delivery efficiency of the A350, helping Airbus not only resolve the $1 billion default crisis but also collaborate with Palantir to create Skywise, the first open data platform in the aviation industry, fundamentally altering the digital landscape of the aviation industry. Why can only Palantir achieve this? At the AIP Con 8 conference held last year, Palantir CEO Alex Karp bluntly stated: "Using LLM well requires good ontology architecture." The core is not computational power or modeling capability but the twenty years of ontology construction expertise. Traditional databases can only store isolated data, general large models can only generate coherent text - only through ontology engineering can real-world complex business relationships be translated into a language that AI can understand, reason with, and execute. It doesn't solve the problem of "data storage" but the problem of "business understanding" - this is precisely the biggest bottleneck in the application of AI in high-end manufacturing and complex supply chain scenarios, and it is the core problem that no general large model can solve. From Airbus to COMAC, Haizhi Technology is poised to replicate and surpass the legend of Palantir The story of Palantir and Airbus seems to be perfectly replayed in China, and even may exceed it in all aspects. The development of domestically produced large aircraft by COMAC is a more extensive and complex system engineering project than Airbus's A350 program. From research and development design, supply chain management, production, to operation and maintenance through the entire lifecycle, involving millions of precise components, global collaboration systems, and zero-fault aviation safety and compliance requirements. COMAC has stated that as a benchmark in high-end manufacturing, the business characteristics of the aviation industry are inherently aligned with knowledge graph technology. Haizhi's graph-model fusion technology system in technology innovation, domestic substitution, industry scenario application, and experience are highly compatible with COMAC's technological exploration and needs. The two parties unanimously acknowledge the core application value of graph-model fusion technology in the complex scenarios of aviation intelligent manufacturing. Haizhi Technology's high degree of recognition from COMAC is rooted in its twelve years of deep cultivation in this field. The core barrier that Palantir constructed for Airbus was a complete ontology system tailored to the aviation manufacturing scenario, solving the "consensus" problem in complex industrial scenarios. Haizhi Technology has gone further by creating a more comprehensive "Ontology Graph Ontology + Context Graph Context Graph" graph-model fusion full-stack technology system. If Palantir built an accurate "digital skeleton" for aviation manufacturing, Haizhi Technology's graph-model fusion technology injects an "intelligent soul" capable of independent decision-making and evolution into this framework. Specifically: 1. In terms of the core competency of ontology construction, Haizhi Technology and Palantir have completely consistent underlying logic while achieving a comprehensive upgrade in technical efficiency. Utilizing over a decade of knowledge graph technology accumulation, Haizhi Technology can achieve unified modeling of core business elements in the entire aviation manufacturing process through a standardized "ontology." It can automatically extract entities and relationships with high precision using large models, integrating heterogeneous data scattered across research and development, procurement, production, quality inspection, and operations into a standardized, understandable, and computable knowledge base for AI. 2. Palantir's ontology system solved the core problem of "what business is and how it's related" in the aviation manufacturing scene, while Haizhi Technology's graph-model fusion technology goes further to solve the deeper questions of "why these decisions are made and how future decisions should be made." Through the complementary advantages of knowledge graphs and large models, this technology fundamentally breaks through the "illusion" problem of large models, achieving a dual boost in accuracy and interpretability of AI applications. Based on this graph-model fusion technology, its self-developed AI intelligence unit combines deep business understanding, precise reasoning, and efficient execution capabilities. It can deeply analyze unstructured documents, such as design specifications, production procedures, operations manuals, historical decision records, compliance requirements, etc., parsing the complete business decision chain from goals, rules, judgments to execution, bringing the implicit management logic of enterprises to explicit, fragmenting decision rules into a system, perfectly adapting to the extreme complexity requirements of high-end industrial sectors like aviation manufacturing. Most importantly, this technology system from Haizhi Technology achieves complete localization and independent control. This is an irreplaceable core value for COMAC, which bears the strategic mission of domestically produced large aircraft. From Palantir serving Airbus to Haizhi Technology entering high-end manufacturing, the growth trajectories of the two are remarkably similar: Palantir initially served the US defense industry and honed its core technology in scenarios with the utmost demands for data security, accuracy, and real-time requirements before replicating it in industries like commercial aviation. Haizhi Technology, on the other hand, initially focused on finance, electricity, and government core industries, honing its graph-model fusion and ontology construction capabilities over twelve years. Now, it is formally entering the high-end manufacturing aviation sector. This may mark a milestone moment where the Chinese version of Palantir truly transitions from deep industry expertise to national strategic implementation. The OpenClaw trend is now happening, where ontology engineering is the ultimate future of AI autonomy Palantir's legendary story with Airbus was just the beginning of industrial AI; the explosion of the Open Claw trend is defining the vision of industrial AI. The rise of Open Claw has made it clear to the global market: the future of industrial AI is not about creating a smarter operating system for enterprises, but rather about building an all-AI organizational structure that can plan, coordinate, and make decisions autonomously. Communication barriers, division of labor friction, authorization boundaries, and efficiency ceilings faced by traditional enterprise organizations will be completely shattered - and the more complex the business chain, the more stakeholders, the stricter the compliance requirements, and the more demanding the system complexity, the more likely high-end manufacturing companies like COMAC are to be the first to achieve this organizational evolution. However, most people might overlook a core prerequisite: to achieve AI autonomy and Multi-Agent intelligent agent deployment, the first thing necessary is to solve the "consensus problem." When hundreds or thousands of intelligent agents need to work together, there must be a unified, understandable, and unambiguous "consensus grammar." Otherwise, they will fight independently, or even create bigger system chaos - and this grammar is precisely what Haizhi Technology's years of ontology engineering aims to solve. Palantir achieved "consensus dissolution explanation" with its ontology system, enabling frictionless collaboration between humans and AI; while Haizhi Technology's graph-model fusion technology further realizes frictionless collaboration between AI agents. Reflecting on the evolution of the AI industry in this wave, a clear pattern emerges: In the first stage, the market believed in models, eagerly investing in competitions for parameter scale. In the second stage, the market believed in data, offering premiums for high-quality training data and data infrastructure. Now, the market is finally sober: no matter how powerful the model or how massive the data, it also needs practical business scenarios and an understanding of business logic through ontology systems. Companies that truly transcend the AI concept cycle don't rely on storytelling or hype but deeply embed themselves in national core industries, with the ability to construct digital ontologies for complex real-world scenarios. Over twenty years, Palantir has grown into an indispensable strategic asset in AI for the US defense industry and high-end manufacturing sectors, surpassing a valuation of over a hundred billion dollars. This success was not based on modeling capabilities but on its core ability to construct digital ontologies for complex real-world systems. Today, Haizhi Technology's entry into the high-end manufacturing sector may mark the real formation of the Chinese version of Palantir. Riding the tide of domestically-produced large aircraft and high-end manufacturing localization, entering the new era of AI autonomy sparked by OpenClaw, Haizhi Technology, with its twelve years of dedication to graph-model fusion technology, is constructing the most robust and independently controllable digital foundation for industrial intelligence in China.