Osmosis

Osmosis is a reinforcement learning post-training platform for building, deploying, and continuously improving task-specific AI models. It addresses the problem of manually adjusting prompts and context for each edge case in AI agents, where failure rates compound across multi-step tasks. The platform supports reinforcement fine-tuning techniques such as GRPO and DAPO, along with multi-turn tool training, without requiring teams to manage the underlying infrastructure.

Key capabilities include training with tools synced from GitHub or created natively on the platform, MCP integration, automated hyperparameter sweeps, and real-time reinforcement learning. Models can be served on single-tenant compute or exported for self-hosting, with full on-premises and VPC deployment available. Continuous improvement is driven by automatic triggers based on time intervals or evaluation scores, with real-time data ingestion enabling model updates as frequently as every hour. The platform integrates with existing evaluation solutions and supports data processing at scale, from hundreds of examples to millions.

Osmosis is aimed at engineers and teams building AI agents for use cases such as domain-specific data extraction, tool use, and code generation. It offers hands-on deployment support spanning feature engineering, reward function creation, dataset curation, and model delivery. The platform supports training and maintaining numerous model versions. No specific pricing, plan, or trial details are stated.