滴普科技2025年营收增超7成，年内有望迎盈利拐点：AI业务成增长主引擎

Core Viewpoint - Dipu Technology (1384.HK) has released its first financial report since its listing on the Hong Kong Stock Exchange, showing significant revenue and profit growth driven by its core Data+AI business segments [8][9]. Financial Performance - In 2025, Dipu Technology reported a revenue of 415 million RMB, representing a year-on-year growth of 70.8% [8]. - The gross profit reached 229 million RMB, with an impressive year-on-year increase of 81.1% [8]. - The gross margin improved by 3.2 percentage points to 55.1% compared to 2024 [6][8]. - Adjusted net loss narrowed significantly by 71.4% to approximately 27.54 million RMB [8]. Business Segments - The FastAGI enterprise-level AI solution generated 254 million RMB in revenue, marking a year-on-year growth of 181.5% and accounting for 61.3% of total revenue [10][12][13]. - The gross margin for the FastAGI segment increased by 6.1 percentage points to 55.2% due to economies of scale and improved delivery efficiency [10][14]. - The FastData enterprise-level data intelligence solution maintained steady growth with a revenue of 161 million RMB, reflecting a year-on-year increase of 5.2% and a gross margin of 54.9% [17]. Product Development - Dipu Technology continues to invest heavily in R&D, with expenses reaching 108 million RMB in 2025, focusing on computational power and the FastAGI solution [19]. - The upgraded Deepexi enterprise model can build customized business logic chains and perform deep training, enhancing its understanding of business needs [20]. - The DeepexiOS AI-level enterprise operating system integrates various platforms to support the deployment of AI employees across different industries [19][22]. Market Positioning - The company is positioned to benefit from the current policy incentives in the AI industry, with significant government support for AI applications in manufacturing and other sectors [24][25]. - Dipu Technology's focus on vertical industries aligns well with the growing demand for digital and AI transformation, addressing the challenges of deploying general AI models in complex business environments [25].