Osmosis

About Osmosis

Osmosis is a forward-deployed reinforcement learning platform for teams that want a task-specific model to beat a general foundation model on their particular job at a fraction of the inference cost. The company works hands-on across the entire post-training workflow — feature engineering, reward function design, and the training and serving process itself — rather than shipping a self-serve console and leaving customers to figure out reward shaping alone. Underneath, it exposes current reinforcement fine-tuning techniques including GRPO and DAPO plus multi-turn tool training, without requiring the customer to stand up the GPU infrastructure those methods normally demand. The platform integrates with whatever evaluation setup a customer already has and monitors production performance continuously, kicking off retraining runs automatically when quality drifts — no engineer in the loop — and it can ingest real-time data to refresh models as often as hourly. Three use cases are called out on the site: domain-specific extraction models that hold an exact output schema, agents trained against the precise tool set they will have in production so multi-step tool use stays reliable, and specialized coding models for domain-specific languages, front-end components, and context-aware test generation.

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