Osmosis

Osmosis is a reinforcement fine tuning platform that helps companies build task specific models intended to outperform general foundation models at a fraction of the cost. It positions itself as forward deployed, meaning its team works directly with customers across the post training workflow, from feature engineering and dataset curation to reward function design and serving the finished model. The platform is aimed at engineers building AI agents who want reinforcement learning without running the infrastructure. It supports techniques such as GRPO and DAPO, multi turn tool training, and tools synced from GitHub or connected through MCP. Data can start with hundreds of examples and scale to millions, and automated hyperparameter sweeps are included. A continuous improvement loop connects to a customer's evaluation setup, monitors performance and starts retraining runs automatically, with updates as frequent as every hour. Named use cases are document data extraction, tool use for agents and code generation for domain specific languages, front end components and tests. Customers own the trained weights. Models can be served on Osmosis single tenant compute or exported for self hosting, and the full platform can be deployed inside a customer's own cloud or on premises. Pricing is not published. The site directs visitors to book a demo.