General Catalyst backs River AI with $1.1B in seed/Series A

River AI, founded by Igor Babuschkin—a co-founder at xAI with prior leadership roles at DeepMind and OpenAI—emerged from stealth in June with an ambition to rebuild AI from the ground up. Rather than following the prevailing trajectory of large language models designed to replace human workers, Babuschkin's stated mission is to create personally trainable AI assistants that serve as "guardian angels: quietly present, on your side, helping with what actually matters to you." To realize this vision, he argues that "the stack has to be rebuilt end to end: training, models, the product layer, and new hardware that lets personal AI live close to you."

The company's immediate product is an API that allows developers to fine-tune open-weight models using reinforcement learning and low-rank adaptation (LoRA), billed per 1 million tokens with rates varying by model choice. River's positioning frames this as a corrective to prompt engineering: "Prompting steers a model you don't own and can't improve. River lets you train open models into ones that are truly yours—and serve them like any other endpoint." In its funding announcement, the company claimed that "any enterprise can complete a complex reinforcement learning run in 15 to 20 minutes with no infrastructure team required, at two to four times the cost savings relative to closed-source alternatives."

The $1.1 billion seed/Series A round, led by General Catalyst and AMP PBC (an AI-focused investment firm founded in 2026 by former Andreessen Horowitz general partner Anjney Midha), was joined by Nvidia, AMD Ventures, Y Combinator, and Temasek. While the round size for a company less than three months old is substantial—and arguably indicative of an overheated AI investment environment—the timing coincides with a genuine shift in enterprise thinking. Companies are increasingly seeking to control their AI model destiny through a mix of open-weight and proprietary models, and River is positioning itself to solve the technical and operational bottleneck of post-training customization at scale. The broader vision extends to personal AI agents akin to OpenClaw and its derivatives, suggesting River intends to serve both enterprise and consumer use cases as the market matures.