中电科普天科技股份有限公司 2025 年 5 月 13 日投资者活动记录表 编号：2025-002 | | √特定对象调研 | | □分析师会议 | | --- | --- | --- | --- | | 投资者关系活动类 | □媒体采访 | | □业绩说明会 | | 别 | □新闻发布会 | | □路演活动 | | | □现场参观 | | □电话会议 | | | □其他： | （请文字说明其他活动内容） | | | 参与单位名称 | 彭陈张资产 | 陈荣新 | | | | 浦健投资 | 邢 军 | | | | 嘉强基金 | 邓海茵 | | | 及人员姓名 | 广金基金 | 冯柏昌 | | | | 汇阳基金 | 蔡诺怡 | | | | 华福证券 | 王二鑫 游世雯 | 祁建龙 | | 时间 | 2025年5月13日 | | | | 地点 | 公司大楼会议室 | | | | 公司接待人员姓名 | 董秘、副总裁：周震； | | | | | 董事会办公室：刘梦雨、邓晓华 | | | | | 一、公司介绍 | | | | 投资者关系活动 | | | 中电科普天科技股份有限公司（简称"普天科技"）是专业从事公网 | | ...

Core Viewpoint - The implementation of "credit instead of proof" aims to enhance public management and service efficiency by replacing multiple compliance certificates with a single credit report, thereby optimizing the business environment and reducing administrative burdens [1][2]. Group 1: Implementation and Benefits - The new policy replaces the requirement for various compliance certificates with a single credit report, which can significantly reduce the time and costs associated with administrative approvals [1]. - The initiative is expected to improve administrative service efficiency and create a more favorable business environment by integrating credit empowerment with the reduction of unnecessary documentation [1]. - The establishment of a cross-province mutual recognition mechanism for credit reports will facilitate a unified national market, breaking down regional barriers and promoting the flow of resources [1]. Group 2: Data Management and Governance - The notification emphasizes the need for enhanced data collection and sharing between local and central governments, as well as horizontal integration among various regulatory bodies, indicating a more robust data governance framework [1]. - A feedback and correction mechanism for credit data will be established, allowing for the display of corrected credit information, which reflects a balanced approach to governance [2]. - The initiative faces challenges such as regional standard discrepancies and data security risks, necessitating improvements in technical safeguards and data management systems [2]. Group 3: Future Directions - There is a call to expand the applicability of "credit instead of proof" to include more social organizations and individuals, as well as to broaden its use in various market transactions like financing and credit [2]. - The goal is to foster a culture of trust and credit usage within society, maximizing the benefits of credit and enhancing market vitality [2].

Financial Data and Key Metrics Changes - Total revenues in Q1 were $93.1 million, up 25% year over year, exceeding guidance [22] - SaaS revenue for Q1 was $68.9 million, representing year-over-year growth of 34% on a constant currency basis, comprising 74% of total revenues [22][23] - Gross profit for Q1 was $69.8 million, with a gross margin of 75%, an improvement from 74.1% in Q1 of 2024 [29] - Operating income for Q1 was $13.4 million, with an operating margin of 14.4%, compared to 8.9% in the prior year [30] Business Line Data and Key Metrics Changes - Term license and support revenue grew 12% in Q1, driven by large deals in the APAC region [23] - Maintenance revenue declined year over year, while service revenues grew 4% but decreased as a percentage of total revenues [23] - Combined SaaS and term license revenues grew 31% in Q1, indicating strong subscription revenue growth [23] Market Data and Key Metrics Changes - North America SaaS revenues grew 31% year over year, while EMEA and APAC saw growth rates of 36% and 40% respectively [24][25] - Total ARR at the end of Q1 was $345.5 million, representing year-over-year growth of 26% to 28% when adjusted for FX [26] - New ARR in Q1 was $18.5 million, representing organic growth of 57% year over year, the highest as a public company [26] Company Strategy and Development Direction - The company aims to become the world's leading data management software company, targeting $1 billion in ARR by 2029 [7] - Focus on integrated platforms to address converging challenges in data security, governance, and resilience [13][20] - Continued innovation in multi-cloud capabilities and AI-powered solutions to enhance customer value [14] Management's Comments on Operating Environment and Future Outlook - Management remains confident in navigating macroeconomic uncertainties, emphasizing the mission-critical nature of their solutions [8][20] - The demand environment has remained stable, with AI and security being top priorities for enterprises [38] - The company is cautious about potential geopolitical risks impacting the second half of the year while maintaining a strong pipeline [33] Other Important Information - The company ended Q1 with $351.8 million in cash and equivalents, including $87.3 million from warrant exercises [31] - Free cash flow was negative $1 million, primarily due to one-time tax payments [31] - The company repurchased 800,000 shares for approximately $12 million in Q1 [32] Q&A Session Summary Question: Changes in demand environment - Management indicated that the demand environment has remained stable, with no significant changes in customer hesitation or sales cycles [38] Question: Competitive environment and data governance - Management noted that data governance is a key driver for new customer acquisitions, especially as enterprises prepare for AI deployment [40] Question: Momentum in the MSP segment - The company remains optimistic about the MSP segment, which accounted for 14% of total ARR and grew 60% annually from 2020 to 2024 [47] Question: License outperformance in the quarter - The outperformance in license revenue was attributed to a combination of large deals and favorable timing dynamics [75] Question: AI implementation among customers - Management confirmed that AI implementation has not slowed down, with a sense of urgency among businesses to adopt AI solutions [80]

Summary of Key Points from Conference Call Records Industry and Companies Involved - The conference call discusses the performance and trends in the AI application sector, particularly focusing on companies like Duolingo, Palantir, and Applovin, as well as the education industry and programmatic advertising in China [1][4][8]. Core Insights and Arguments - **Growth in AI Applications**: The AI application sector in the US stock market is expected to see significant growth starting in the second half of 2024, with companies like Duolingo, Palantir, and Applovin reporting revenue and profit that exceed expectations, leading to stock price increases of 5-10 times [1][4]. - **Duolingo's Performance**: Duolingo has integrated GPT-4 into its platform, launching Duolingo Max, which has led to a 44% year-over-year increase in daily active users to 46 million and a 40% increase in paid subscribers to 10.3 million [1][9]. - **Palantir's Financials**: Palantir reported Q1 revenue of $884 million, a 39% year-over-year increase, with net profit rising 130% to $214 million. The company raised its full-year revenue guidance to $3.89 to $3.92 billion, reflecting a 20% growth [3][11]. - **Applovin's Advertising Growth**: Applovin's Q1 revenue reached $1.5 billion, with a 71% growth in advertising business and a non-GAAP profit margin of 81%. The company holds a 28% market share in game advertising and is expanding into the e-commerce advertising market [1][5][6]. - **AI and Programmatic Advertising**: The combination of AI and programmatic advertising offers insights for domestic companies like Tencent and Kuaishou, potentially increasing app display time and conversion rates [1][8]. Other Important but Potentially Overlooked Content - **Challenges in Education Sector**: The education industry faces challenges in producing large-scale, low-cost, high-quality educational products. AI technology can enhance personalized education but is limited by cost and precision issues [9][10]. - **Domestic AI Adoption**: Chinese education companies are adopting low-cost AI technologies to upgrade their products, with companies like Haotian and Anlian Education utilizing models like DeepSeek [3][10]. - **Future of AI in Enterprises**: The core challenge for enterprise AI applications lies in data governance. Companies that can manage data effectively are likely to benefit in the future [12][16]. - **Potential of Domestic Companies**: Companies like Fourth Paradigm are seen as potential equivalents to Palantir in China, focusing on data training platforms and data governance [17]. This summary encapsulates the key points from the conference call records, highlighting the performance of specific companies, industry trends, and the implications for future developments in AI applications.

Summary of the Conference Call for New Point Software Company Overview - **Company**: New Point Software - **Industry**: Software and AI solutions for government and construction sectors Key Points and Arguments Financial Performance - In Q1 2025, New Point Software reported revenue of 277 million yuan, a year-on-year decline of 5.94% [3] - Net profit attributable to shareholders was 41 million yuan, an increase of 23.2% year-on-year, with losses narrowing by approximately 12 million yuan [3] - The company experienced declines across various business segments: Smart Finance down 7.47%, Smart Government down 2.83%, and Digital Construction down 7.59% [3] - Despite the declines, overall conditions are gradually improving, with orders recovering [2][3] Cost Management and Profitability - The company achieved a 10.13% year-on-year reduction in operating expenses, leading to a continuous improvement in net profit margin [2][5] - The internal target for gross profit growth from orders is set at 30% [7] - The company anticipates a positive revenue turnaround for the full year, with rapid profit growth and sustained profit margin improvement [7][8] AI Developments - New Point Software has made significant progress in AI, launching the AI model product "Trading Brain" to empower the entire process of government procurement [2][6] - AI-driven customer service upgrades have improved service accuracy and reduced costs [2][6] - The company has developed various AI applications, including intelligent evaluation assistants and procurement document preparation tools, enhancing digital transformation for state-owned enterprises [6][10] Market Trends and Customer Engagement - Government IT spending is showing slight recovery, but overall budget growth remains cautious [22] - Government clients are increasingly pragmatic, focusing on immediate results and showing reluctance towards large-scale investments in traditional systems [23] - The company is expanding its target customer base from large projects to small and medium-sized clients, increasing market reach [26][27] Data Governance and Management - New Point Software has implemented a systematic public data collection and governance strategy, collaborating with local governments for data governance projects [19] - The company is focusing on procurement data, regulatory data, and supply chain management in the manufacturing sector [19] SaaS Business Growth - The maintenance service revenue growth rate has increased significantly, reaching 20% in Q1 2025, with government service revenue accounting for 40% of total revenue [31] - The market potential for SaaS applications is estimated to be between 1 billion to 2 billion yuan, with ongoing efforts to enhance service offerings [31] Future Outlook - The company aims to leverage AI to enhance the quality of government services and governance, addressing existing bottlenecks [25] - New Point Software is committed to achieving a turnaround in revenue and profit levels despite uncertainties in project acceptance cycles [8][9] Additional Important Insights - The company is focusing on improving cash flow management and has established a dedicated accounts receivable management department to enhance collection efficiency [28] - AI's impact on the company's business model is significant, leading to the creation of numerous SaaS payment application scenarios [13] - The integration of AI technology is expected to transform the entire landscape of government services, with interactive intelligent agents potentially replacing traditional service platforms [32][33]

Group 1 - The European Commission has launched the "Artificial Intelligence Continental Action Plan" to enhance its position in the AI sector through policy relaxation, computing infrastructure, and industry applications, marking a shift towards balancing regulation and innovation [1][2] - The plan aims to reduce regulatory burdens on industries and ensure that the upcoming AI Act is simple and conducive to innovation, reflecting a subtle adjustment in EU AI policy [1][2] - A significant aspect of the plan is the construction of "AI super factories" equipped with approximately 100,000 advanced AI chips to address the computing power gap, as current AI computing resources in Europe are significantly lagging behind the US and China [2][3] Group 2 - In 2024, the EU AI Act will come into effect, categorizing AI applications by risk levels and imposing strict compliance requirements on high-risk sectors like healthcare and finance, raising concerns about potential hindrances to innovation [2][4] - The EU's private investment in AI is approximately $8 billion, compared to the US's $109.1 billion, highlighting a significant investment gap that could impact the competitiveness of European AI startups [4] - The EU faces challenges in talent retention, with only 0.41% of the workforce being AI professionals, despite a 124% increase in AI talent since 2016, indicating a mismatch between educational output and industry needs [4][5] Group 3 - The plan emphasizes the need for high-quality data acquisition channels and aims to leverage the EU's strengths in strategic sectors like manufacturing, healthcare, and transportation [3][4] - The construction of AI super factories has faced delays, with site selection still undecided after 18 months of discussions, indicating internal challenges within the EU [5][6] - Data sharing remains a slow process, with 75% of multinational companies hesitant to share industrial data due to compliance risks, which hampers the overall progress of the AI initiative [5][6]

Core Insights - The tax big data market in China is experiencing significant growth, with the market size projected to increase from 30.869 billion yuan in 2019 to 103.213 billion yuan by 2024, indicating a strong demand driven by ongoing tax administration reforms [1][9]. Tax Big Data Industry Definition and Classification - Tax big data refers to the vast and diverse data sets generated, collected, stored, and managed during tax management, collection, and inspection processes, encompassing structured, unstructured, and semi-structured data [1]. Tax Big Data Industry Value Chain Analysis - The industry value chain includes data collection from various sources, data storage and processing, and application services, with a focus on optimizing tax management and enhancing taxpayer services [3]. Development History of China's Tax Big Data Industry - The industry has evolved from initial informatization in the 1980s to intelligent applications post-2013, with significant advancements in data integration and risk management [5]. Current State of China's Tax Big Data Industry - The market is growing steadily, with a notable increase in demand for tax big data software and services due to the implementation of data-driven tax management strategies [9]. Downstream Application Areas of Tax Big Data - The industry exhibits a diversified application landscape, with tax collection accounting for 45% of the market, risk prevention at 25%, taxpayer services at 18%, and economic analysis at 10% [11]. Key Enterprises in China's Tax Big Data Industry - Major players include Digital China, which focuses on tax digitalization solutions; Aerospace Information, a leader in electronic invoicing; Inspur, which provides infrastructure and data governance; and Yonyou Network, specializing in enterprise tax management [13][14][16]. Future Development Trends of China's Tax Big Data Industry - The industry is expected to enhance data governance and compliance, expand data sharing and integration across departments, and leverage technologies like AI and blockchain to innovate business scenarios and improve efficiency [18][19][20].

人工智能技术的快速发展在为各行业注入创新活力的同时，也出现了一系列亟待解决的问题，如数据安 全与模型可信度不足、历史数据孤岛化、大模型与行业场景适配性差等问题。针对痛点问题，罗普特聚 焦三大核心方向，强化数据治理能力，持续迭代"行业数据中台+AI模型"一体化解决方案，进一步深 化"大模型+行业知识"定制化路径，以客户实际场景为训练目标，开发轻量化、高精度的行业专属模 型，着力攻克垂直领域难题。 22日晚间，罗普特(688619)发布2024年年度业绩报告。公告显示，2024年公司实现营业收入1.43亿元， 期末总资产为15.21亿元，净资产为8.92亿元。同时，公司披露2025年一季报，第一季度公司经营性现金 流实现回正，公司业务内生动力取得突破。 罗普特年报披露，公司围绕人工智能算力、算法和数据三大核心要素进行战略布局。在算力领域，公司 在加强与华为昇腾、英伟达等算力厂商合作的基础上，深化与国内算力私有化部署的头部厂商合作，着 力打造边缘端私有化部署能力，并针对公安、政务、能源等对数据安全敏感的领域，共同研发适配边缘 端的轻量化算力设备。 在算法领域，罗普特基于此前在安防监控、城市交通等场景可触及的海量图像 ...

导读：今天，中国实体出口贸易到达天花板，全球金融、航运等传统领域服务贸易格局不易 打破，数据跨境服务，是一块全新的经济增量蛋糕。 麦肯锡预测，数据流动量每增加10%，将带动GDP增长0.2%。经济合作与发展组织 （OECD）测算，数据流动对各行业利润增长的平均促进率在10%，在数字平台、金融业等 行业中可达到32%。 当前，中国大约贡献全球数据总量的10%左右，但中国企业在国际数据市场上的市场占有率 只有不到2%。相较而言，美国目前的市占率在30%左右，欧盟在10%左右。 日前，跨境数科总经理李晶向科工力量分享了他关于中国参与全球数据治理和产业规则标准 制定的思考。 科工力量：为什么说跨境数据流动对很多企业来说是刚需？ 李晶：全球迈入数字经济时代，现在数字化、产业数字化的转型程度高，转型速度快。 今天，几乎国内外所有的跨国大型企业的业务管理流程，都使用数字化系统，数据是数字化 系统中的核心要素，尤其是最近几年，随着通信、算力、算法等技术突破，大数据、人工智 能的产业应用加速落地，很多企业已经把数据资源看成生命线。 数据作为数字化、网络化和智能化的基础，已迅速融入生产、分配、流通、消费以及社会服 务管理等各个环 ...

数据资产的交易与融资规模不断扩大的同时，如何为其提供风险保障是业内正在思考的问题。如今，部 分险企正在这一领域加速探索。 公开信息显示，近期多地落地了首单数据资产相关保险产品。业内人士告诉《中国经营报》记者，基于 数据资产的价值挖掘需以数据治理为基础，但多数险企尚未建立统一的数据标准、质量管理和安全体 系，使得数据资产的价值评估难度较高，是险企首要面临的挑战。 多款产品落地 近日，河北省第一笔数字资产信息安全责任保险在秦皇岛市落地，由珠峰财产保险股份有限公司为某高 新技术企业提供数字安全责任保障，并引入风险减量服务，帮助企业主动预防和减少网络安全事件的发 生。 本报记者 蒋牧云 李晖 上海、北京报道 评估体系有待完善 不过，记者也多方了解到，险企在数据资产领域中仍面临不少挑战，尽管不少险企都关注到数字资产领 域的机会，但探索的实质进展仍然缓慢。此前有数据交易所联合多家保险公司上线相关数据类保险专 区，但近一年业务动向和承保情况尚无公开披露。 对此，天使投资人、资深人工智能专家郭涛告诉记者，当前整体的数据治理体系薄弱。数据资产的价值 挖掘需以数据治理为基础，但多数险企尚未建立统一的数据标准、质量管理和安全体系， ...