River AI has successfully raised $1.1 billion in funding to scale its full-featured artificial intelligence platform. The round was led by General Catalyst and AMP PBC, with strategic support from NVIDIA and AMD Ventures. Y Combinator and Temasek also participated in the financing.
The Palo Alto-based company was founded by Igor Babushkin, who previously co-founded xAI. Before that, Babushkin worked on generative modeling and reinforcement learning at Google DeepMind, and also managed large-scale training processes at OpenAI before helping launch xAI.
The funds raised by River will be directed towards product development and expanding the company's operations. River creates infrastructure that gives developers and enterprises greater control over their AI models. The company's strategy focuses on training, deployment, hardware, and personalized AI products.
To achieve this, River uses an API specifically designed for creating custom models. The platform offers fine-tuning using LoRA and reinforcement learning for advanced open-weight models. This allows developers to adapt models without building extensive infrastructure themselves.
The company claims that enterprises can complete complex reinforcement learning sessions in 15–20 minutes. Furthermore, River states that its platform costs two to four times less than closed-source alternatives. These processes include fast weight transfer and stable sampling during training. River also manages elastic computing resources as workloads change, allowing developers to focus more on improving model performance.
Trained models can transition directly into production environments through the River platform. The company uses token-based billing for training and inference, which is intended to reduce costs associated with unused GPU power.
Babushkin stated that the company is convinced of the need to make AI more accessible and open. He also emphasized that AI should work for the benefit of users, not the laboratories that create it. Therefore, River aims to give users greater control over intelligence.
The company is implementing a broader strategy that goes beyond enterprise software. It is developing hardware designed to bring personal AI closer to users. Work is also underway on products focused on personalization and continuous learning.
The funding round also attracted major semiconductor companies. NVIDIA and AMD Ventures joined the round as strategic investors. Their participation reflects the significance of computing infrastructure for River's expansion.
General Catalyst CEO Hemant Tanenya noted the growing importance of open-weight models for AI leadership. He also highlighted River's commitment to giving users greater control. Thus, the investment supports a broader trend toward more accessible AI development.
The founders of River have extensive experience in cutting-edge AI research and development. Babushkin worked on generative modeling and reinforcement learning at Google DeepMind, and later helped lead large training programs at OpenAI before co-founding xAI. The team also includes specialists from xAI and Tesla, giving River experience in deep learning and reinforcement learning, supporting ambitions to develop AI infrastructure at multiple levels.
River positions its platform around this demand, aiming for enterprises to customize models without needing to maintain expensive infrastructure teams. The token-based pricing model is also aimed at increasing predictability of computing costs.
The implementation of enterprise software has encountered a significant hurdle. Companies are acquiring advanced artificial intelligence tools, but integrating these tools into legacy systems remains a slow, costly, and complex task. To solve this problem, the company June AI has raised $20 million in seed funding.
Deploying enterprise software is a labor-intensive and complicated process. Globally, organizations invest hundreds of billions of dollars annually in configuring, integrating, and supporting their core systems. Businesses aim to integrate models like GPT, but the presence of years of technical debt complicates the operation of such systems. There are very few seamless connections between older platforms, such as SAP, Oracle, Workday, and Salesforce, and modern cloud data infrastructure.
June AI directly addresses this implementation gap. Instead of forcing companies to hire expensive external consulting teams, the startup uses AI agents to optimize the integration process. The platform functions as an automated digital bridge, analyzing enterprise workflows and automating repetitive configuration tasks, which significantly reduces the time required for enterprise software setup.
The process at June AI begins with deep system analysis and process mining. The platform studies existing database logs to determine how business workflows are actually executed within an organization. After mapping these hidden patterns, the system identifies operational bottlenecks and automates structural changes in the software. This technology works natively in core data environments such as Databricks and Snowflake.
By mapping inter-system relationships, June AI ensures that updates in one department do not disrupt subsequent processes. The platform allows engineers working directly in the system and system integration teams to deploy enterprise AI agents with less friction and lower overall costs. The global systems integration market is expected to reach over a trillion dollars in the coming decade. As companies increase their investments in AI, automated integration will become an integral part of corporate infrastructure. Advanced foundational AI models are of little value if enterprises cannot connect them to their real-time operational data.
The founding team brought deep experience in enterprise software to solve this problem. June AI is the brainchild of CEO Ephrat Rapoport, CTO Idan Zittat, President Barak Goldstein, and Chief Architect Ohad Hen. The founders previously developed the conversational intelligence platform Bonobo AI, which Salesforce acquired in 2019. Over the past five years, they led key product and engineering teams within Salesforce, gaining firsthand experience with challenges in the enterprise sector.
They noticed that purchasing AI software is relatively simple, but implementing it into disparate user workflows is extremely difficult. June AI was created to replace manual professional services with automated software workflows, providing faster value realization for modern enterprise solution buyers. With the $20 million in new capital raised and support from leading industry figures, June AI is well-positioned to innovate in automated deployment. By eliminating technical debt and accelerating integrations, the company helps businesses move from simple AI demonstrations to fully functional automated operations.
