River AI raises $1.1 billion to build personalized AI stack
The newly launched AI company is developing personal AI agents designed to continuously learn a user’s preferences, goals and working style.
River AI has secured $1.1 billion as the company seeks to make customized AI models faster, cheaper and more accessible to developers and companies.
The funding round was led by General Catalyst and AMP PBC, with participation from NVIDIA and AMD Ventures, Y Combinator and Temasek.
Founded by former DeepMind, OpenAI and xAI researcher Igor Babuschkin, the AI startup is building a personalized AI infrastructure that lets users customize and continually train models for their own needs.
The company said general-purpose models can be powerful but are rarely tailored to the specific data and workflows of individual organizations. Until now, creating a custom model typically required specialized hardware, an infrastructure team and months of development.
River’s API aims to reduce that complexity. The company said enterprises can complete complex reinforcement-learning training runs in as little as 15 to 20 minutes without maintaining an infrastructure team, while achieving two to four times the cost savings of closed-source alternatives.
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The platform supports LoRA fine-tuning and reinforcement learning for frontier open-weight models and manages the underlying infrastructure automatically. This includes fast movement of model weights, consistency between sampling and training and elastic compute allocation.
Once training is complete, models can be deployed immediately, while token-based billing means customers pay for actual training and inference usage rather than unused GPU capacity, according to the company.
Babuschkin said River’s goal is to shift ownership of AI from the companies and laboratories that train models toward the people and organizations that use them. The company believes AI should be open, affordable and personalized for individual users.
The company is also developing hardware and consumer-facing products alongside its training infrastructure. Its long-term strategy is to build a vertically integrated stack in which personal AI can operate close to users, continually learn from them and remain under their control.