INFIFORCE Raises Nearly RMB 1 Billion in Series A and A+ Rounds

Summarized by NextFin AI

INFIFORCE completed Series A and A+ financing rounds totaling nearly RMB 1 billion, led by Dunhong Asset and a major state-owned capital platform.
Funding will support research into the AtomBrain embodied brain and causal world models, upgrades to the DataGrid AI infrastructure, and robot platform deployment.
The company develops Ego-native world models that help robots complete long-horizon physical tasks through continuous learning in real-world environments.
INFIFORCE has deployed systems across several Chinese cities and plans expanded commercial rollout and industrial validation following the new capital injection.

NextFin News — Embodied-intelligence company INFIFORCE has completed Series A and A+ financing rounds totaling nearly RMB 1 billion, the firm announced on Thursday.

Dunhong Asset and a leading state-owned capital platform co-led the latest stage, with participation from Zhejiang University Science & Technology Innovation Group, Yandu State-owned Control, Lishui Municipal State-owned Company and other industrial and institutional investors. Existing shareholder CCV continued to invest. Proceeds will primarily fund research on the AtomBrain embodied brain and causal world models, ongoing upgrades to the full-stack AI infrastructure DataGrid, and scaled delivery and validation of multi-form robot platforms in real industrial settings.

Founded to develop next-generation physical AI foundations, INFIFORCE focuses on Ego-native world models that enable robots to perform long-horizon tasks through continuous learning in physical environments. The company has already deployed systems across multiple Chinese cities and plans further commercial rollout following the capital injection.

Explore more exclusive insights at nextfin.ai.

Insights

What does embodied intelligence mean, and how does it differ from traditional AI software?

What are Ego-native world models, and why are they important for long-horizon robot tasks?

How do causal world models help robots learn continuously in physical environments?

What is the role of AtomBrain in INFIFORCE's technical system?

How does DataGrid support INFIFORCE's full-stack AI infrastructure and robot development?

Why are state-owned investors and industrial funds backing embodied-intelligence companies like INFIFORCE?

What does this financing suggest about the current market interest in physical AI and robotics in China?

How far has INFIFORCE progressed from research to deployment across Chinese cities?

What kinds of real industrial settings are most suitable for INFIFORCE's multi-form robot platforms?

What user or enterprise feedback will matter most when validating INFIFORCE's robots at scale?

What are the latest signs that embodied AI is becoming a major investment theme in 2024?

How could recent Chinese policy or industrial priorities influence INFIFORCE's expansion?

What are the biggest technical barriers to building robots that can handle long-horizon tasks reliably?

What challenges might INFIFORCE face when moving from pilot deployments to large-scale commercial rollout?

What risks or controversies surround heavy investment in embodied AI before clear commercial returns are proven?

How does INFIFORCE compare with other embodied-intelligence or robotics startups pursuing physical AI foundations?

Which past robotics companies offer useful comparisons for understanding INFIFORCE's growth path?

What long-term impact could Ego-native and causal world models have on the future of industrial robotics?

NextFin.Al

No Noise, only Signal.

Open App