证券研究报告：计算机 | 公司点评报告 发布时间：2025-09-03 股票投资评级 增持|首次覆盖 个股表现 -1% 30% 61% 92% 123% 154% 185% 216% 247% 278% 2024-09 2024-11 2025-01 2025-04 2025-06 2025-08 鼎捷数智 计算机 分析师:陈涵泊 SAC 登记编号:S1340525080001 Email:chenhanbo@cnpsec.com 分析师:王思 SAC 登记编号:S1340525080002 Email:wangsi1@cnpsec.com 鼎捷数智(300378) AI 赋能下，聚焦高质量增长 ⚫ 事件 鼎捷数智发布 2025 年半年报，基本符合预期。2025H1，公司实 现营收 10.45 亿元，同比+4.08%；实现归母净利润 0.45 亿元，同比 +6.09%；实现扣非归母净利润 0.36 亿元，同比-9.89%。 ⚫ AI 业务对外通过赋能客户经营实现放量增长，对内通过 优化管理提效达成高质量发展 对外：丰富 AI 产品矩阵获客户认可，25H1 AI 业务收入同比 +125.91%。公司依托鼎捷雅典 ...

本报讯 （记者张文湘 见习记者占健宇）8月29日晚间，鼎捷数智股份有限公司（以下简称"鼎捷数智"）发布2025年中期业 绩报告。报告显示，2025年上半年，公司实现营业收入10.45亿元，同比增长4.08%；实现归母净利润4502.67万元，同比增长 6.09%，营收与利润持续保持稳健双增态势。 在非中国大陆地区，上半年公司实现营收5.69亿元，同比增长3.65%。其中，公司在中国台湾地区紧抓AI、劳动力短缺、 信息安全等产业趋势，持续拓展AI应用方案的广度与深度，已完成数十家客户签约并引入近百家AI生态伙伴，积累了电子、医 药、流通等六大产业的AI应用场景模板。与此同时，公司把握中企出海与东南亚数智化升级机遇，升级出海一站式解决方案， 在电子、汽车零部件、装备制造等行业取得了显著增长。东南亚本地拓展方面，公司通过深化行业协会合作及产业联盟运营扩 大商机触达范围，带动收入同比增长60.87%。 研发层面，上半年公司围绕"鼎捷雅典娜数智原生底座"持续攻坚，推动平台功能与性能全面升级，完成多个企业级AI智能 体的开发，AI业务收入同比增长125.91%。近期，公司研发持续加码，发布智能数据套件、企业智能体套件、四 ...

研发层面，上半年公司围绕"鼎捷雅典娜数智原生底座"持续攻坚，推动平台功能与性能全面升级，完成 多个企业级AI智能体的开发，AI业务收入同比增长125.91%。近期，公司发布智能数据套件、企业智能 体套件、四大工业软件AI智能套件、AIoT指挥中心和工业机理AI套件等四大新产品。 8月29日晚间，鼎捷数智（300378）披露2025年半年度业绩报告。今年上半年公司实现营业收入10.45亿 元，较上年同期增长4.08%；实现归母净利润4502.67万元，同比增长6.09%，营收与利润两大核心指标 均实现持续稳健增长。公司研发设计、数字化管理、生产控制、AIoT等四大业务板块协同发展，上半 年营收均实现同比增长。 其中，在中国大陆地区，公司紧抓消费补贴政策、半导体制造国产替代等机遇，聚焦高景气细分市场。 通过AI技术升级产品性能，同时优化运营降本增效，持续开拓市场增长空间。2025年上半年，公司在 中国大陆地区实现营收4.76亿元，同比增长4.61%。在非中国大陆地区，上半年公司实现营收5.69亿 元，同比增长3.65%。 其中，智能数据套件涵盖智能数据引擎、智能指标管理、智能数据治理等模块，加速企业数据资产管理 ...

Core Insights - The article discusses the rise of Industrial AI as a significant revolution in the manufacturing sector, contrasting it with the more visible generative AI trends in content creation and software [1] - It highlights the challenge of transferring tacit knowledge from experienced workers to digital systems, emphasizing the need for a system that can effectively bridge the gap between operational technology and information technology [1][2] Group 1: Industrial AI Development - Industrial AI is seen as a solution to the challenge of integrating the tacit knowledge of experienced workers into digital systems, which is crucial for the future of Chinese manufacturing [1] - Dingjie Zhizhi has launched a series of enterprise-level AI suites aimed at connecting the "arterial" and "venous" knowledge within manufacturing [1][2] Group 2: Challenges in AI Adoption - Many manufacturing companies face a dilemma between the risks of falling behind in AI adoption and the potential pitfalls of investing in technology without a clear strategic purpose [4] - The need for a "thinking system" rather than just a technical system is emphasized, advocating for a decoupled architecture that separates knowledge from action [4] Group 3: Product Matrix and Features - Dingjie has developed a "three-layer rocket" product matrix to integrate the experience of skilled workers with large model reasoning [5] - The first layer, the Intelligent Data Suite, aims to conduct a comprehensive "data CT" for factories, addressing the issue of data silos between operational and management data [6][7] Group 4: Intelligent Collaboration - The second layer involves the creation of a self-developed MACP protocol that enables digital employees to collaborate effectively, enhancing decision-making processes across departments [8][10] - This collaboration allows for complex decision-making tasks to be executed efficiently by multiple AI agents working together [10] Group 5: AIoT Command Center - The third layer includes an AIoT command center that connects various production and facility devices, facilitating a comprehensive AI-driven operational environment [11][12] - The Industrial Mechanism AI aims to understand the underlying physical processes in manufacturing, transforming tacit knowledge into actionable insights [12][13] Group 6: Knowledge Digitalization - Dingjie’s system addresses the aging workforce in manufacturing by digitizing tacit knowledge, capturing it in a structured format that AI can understand [14] - The approach includes multi-modal data capture during demonstrations to lower the barrier for knowledge entry into the system [14] Group 7: Real-World Applications - Case studies from Jia Li Co. and Ying Fei Te illustrate the practical applications of Dingjie’s AI solutions, showcasing significant improvements in productivity and efficiency [17][19][23] - Jia Li Co. achieved a 20% increase in per capita output and a 15% reduction in energy consumption through AI-driven transformations [19] Group 8: Business Model Evolution - The article discusses a shift from traditional project-based revenue models to subscription-based models in industrial software, driven by AI capabilities [24][25] - This evolution allows for a more flexible adoption of AI technologies, reducing the initial capital investment required from companies [25] Group 9: Future of Industrial AI - The competitive landscape is shifting towards the ability to translate complex industry knowledge into AI-understandable formats, which will be crucial for success in the industrial AI space [28] - The article concludes with the notion that the future of industrial AI will depend on trust in algorithms, continuous knowledge acquisition, and the ability to foster a thriving ecosystem of third-party developers [28][29]