Startup Fundraising

AutoAgents.ai Raises Pre-A Funding for Digital Workforce

Future-style Intelligence (AutoAgents.ai) closes Pre-A round to advance its AI agent platforms, Lingda and Daidai, empowering knowledge workers and digital labor.

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Alvaro de la Maza

Partner at Aninver

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Key Takeaways

  • 未来式智能 raised a new round from 凡创资本, 中关村资本, 探元资本, 东证创新, 麟阁创投.
  • Sector: Artificial Intelligence (AI), Technology, Software & Gaming, Business Services.
  • Geography: China.

Analysis

未来式智能 (AutoAgents.ai) has successfully closed its Pre-Series A funding round, signaling strong investor confidence in its vision to build a factory for digital labor. The investment, which saw participation from new backers 凡创资本 (Fanchuang Capital), 中关村资本 (Zhongguancun Capital), and 探元资本 (Tanyuan Capital), alongside follow-on investments from existing supporters 东证创新 (Dongzheng Chuangxin) and 麟阁创投 (Lingge Chuangtou), will fuel the company's expansion in computing power, team growth, and the development of its innovative product ecosystem.

Founded in June 2023, AutoAgents.ai focuses on empowering knowledge workers through intelligent agent technology. The company's core team brings a wealth of experience from leading tech giants such as Alibaba's DAMO Academy, Tencent, ByteDance, and Google. CEO and founder 杨劲松 (Yang Jinsong), a veteran with prior roles at DAMO Academy, ByteDance's Feishu AI, and Amazon AWS, has a proven track record in AI product development and commercialization, having previously led initiatives like Alibaba's Alicemind.

A cornerstone of AutoAgents.ai's offering is “灵搭 (Lingda)”, an enterprise-grade intelligent agent construction platform. This solution directly addresses critical enterprise needs for deploying and utilizing large language models, emphasizing data security and privacy, granular permission management, seamless integration with complex systems, and robust delivery of agents in real-world business scenarios. Lingda has strategically targeted industries with stringent stability and compliance requirements, including the power, finance, and manufacturing sectors.

Distinguishing itself from the broader large model development race, AutoAgents.ai's strategic focus on agents, or AI-powered autonomous systems, is rooted in the understanding that while LLMs serve as foundational infrastructure, enterprises require tangible, results-driven systems. Lingda operates as a low-code platform designed for business users, aiming to democratize the creation and deployment of AI agents. It offers over 20 standard module nodes, supporting natural language workflow generation and a skills engine, thereby shifting complex integration tasks from IT departments to business-centric personnel.

The company has demonstrated significant traction, achieving substantial revenue growth. After generating millions in revenue in 2024, AutoAgents.ai saw a fourfold increase in 2025, with revenue now diversified across the power, finance, and manufacturing industries. The company is projecting ambitious growth, targeting 100 million RMB in revenue for the current year. This success is built on deep industry experience and the development of reusable templates for large enterprise clients, with a notable 100% renewal rate among its over 20 power grid clients.

Complementing Lingda, AutoAgents.ai has launched “袋袋 (Daidai)”, an AI digital expert marketplace. If Lingda is the production facility for digital labor, Daidai serves as the employment platform. It digitizes the deep expertise of human specialists, packaging it into ready-to-deploy digital employees that users can hire on-demand, paying for results. This model aligns with the philosophy that most users will consume AI agents rather than manage them. Daidai currently supports specific tasks like customs declaration and tax reporting, as well as complex roles in areas such as AI video production, e-commerce marketing, and investment due diligence, having already secured over ten million RMB in ARR potential from early validation.

The synergy between Lingda and Daidai forms AutoAgents.ai's unique "Harness Engineering" model. This approach leverages vast amounts of task data from real-world scenarios to refine agent decision-making. User data and task trajectories from Daidai feed back into Lingda, enhancing its underlying models and agent capabilities, which are then made available on Daidai. This closed-loop system has significantly improved task success rates from an initial 72% to 91%.