Applied Compute

Applied Compute helps enterprises train, deploy and improve AI models around their proprietary data and workflows. Its AC2, Applied Compute Agent Cloud, available in private beta, combines open-model training, dedicated inference and learning from production traces. AI teams can bring their own training harness, inspect model rollouts and use feedback from deployed agents in subsequent training. Its research agent Ari analyzes experiments and identifies failure modes. Customers include Cognition, DoorDash and Harvey.

Founded in 2025, the company is led by cofounders Yash Patil, CEO; Rhythm Garg, CTO; and Linden Li, Chief Architect. Its research includes Relevance-Masked Self-Distillation, a method that uses teacher–student probability differences and an AI judge to select which tokens receive training updates. This concentrates learning on desired behaviors rather than unrelated wording changes. Experiments on a synthetic task explored how models could learn unfamiliar behaviors while retaining existing capabilities.

By August 2026, Applied Compute had a reported $50 million annualized revenue run rate. Its business combines consulting services with charges for compute. In April 2026, it announced $80 million in financing led by Kleiner Perkins at a $1.3 billion post-money valuation, bringing total funding to $160 million.