Datasea Intelligent Technology Ltd. (NASDAQ: DTSS) has moved its AI agent digital marketing services into scaled commercial deployment, signing five cooperation agreements and pre-order arrangements with customers including Beijing Judongjiujiu Technology Co., Ltd. The agreements represent a potential annualized service volume of up to approximately US$89.4 million (RMB600 million), based on the maximum anticipated monthly service fees sustained over a full year.
Under the "recharge, consumption and service" billing model, anticipated monthly fees range from roughly US$0.45 million to US$1.49 million per customer. The aggregate anticipated monthly service fees across all five pre-order arrangements fall between approximately US$2.24 million and US$7.45 million. The company cautioned that these figures do not constitute minimum purchase commitments, guaranteed revenue, recognized revenue, or backlog - actual revenue depends on customer usage, platform billing, and settlement.
Services delivered and initial validation
All five customers have begun receiving services, with an aggregate service value of approximately US$1.10 million (RMB7.36 million) as of the press release date. Beijing Judongjiujiu Technology Co., Ltd. accounted for the largest portion at roughly US$0.51 million. The company views this shift from agreements into actual service delivery as initial validation of customer demand and its execution capabilities in real-world AI agent and digital marketing scenarios.
The AI agent platform provides content generation, information publishing, intelligent ad placement, data analytics, marketing execution, and technical support across different application scenarios. Services also include full-chain digital marketing solutions, SaaS services, campaign planning, ad placement strategy formulation, creative material generation, video production, and customization of professional software and mini-programs.
Usage-based billing and the three-layer commercial system
Datasea is structuring its AI agent business around a three-layer commercial system: platform, execution, and scenario. The platform layer provides algorithm invocation, system interfaces, data review, and technical integration. The execution layer covers content generation, digital marketing, intelligent ad placement, data analytics, and SaaS services. The scenario layer targets beauty, wellness, health management, lifestyle services, retail, customized apparel, and other physical commercial environments.
The company said the "recharge, consumption and service" model helps advance the business from single-delivery projects toward recurring invocation and continuous settlement. Unlike one-time project development, these services operate continuously around content generation, marketing campaigns, user engagement, and performance optimization, giving them stronger usage-based consumption characteristics. For marketing professionals exploring how AI integrates into operational workflows, AI for Marketing training resources can provide practical grounding in these exact capabilities.
Chief Executive Officer Zhixin Liu said: "AI agents are becoming important tools for enterprise digital marketing and business operations. Compared with traditional marketing services, AI agents can integrate content generation, ad placement strategy, intelligent execution, data analytics and continuous optimization into a service loop." Liu added that he has adopted a Rule 10b5-1 trading plan to purchase up to US$1.0 million of Datasea's Class A ordinary shares using personal funds.
Why this matters for marketing professionals
Datasea's deployment signals that AI agent services are moving beyond pilot programs into recurring commercial contracts with real billing cycles. For marketing managers, the shift toward usage-based AI services - where clients pay for content generation, ad placement execution, and data analytics as consumed - changes how budgets are structured and how vendor relationships are evaluated. Instead of fixed project fees, marketing teams may increasingly manage variable costs tied directly to campaign activity and performance data. Marketing managers building competency in these models can follow an AI Learning Path for Marketing Managers to understand how AI-driven marketing services fit into operational strategy.
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