Osmosis

Companies that want AI models tailored to narrow tasks like document extraction, tool-using agents, or domain-specific code generation currently rely on large foundation models, which can be costly and imprecise for specialized needs. Building and maintaining reinforcement learning post-training infrastructure in-house requires significant engineering effort, from feature engineering to reward function design to continuous retraining pipelines. Without this, teams either overpay for general-purpose models or get agents and extraction systems that fail on complex, multi-step, multi-tool tasks or lack schema precision.

Osmosis is a forward deployed reinforcement learning platform that works hands-on with customers across the entire post-training workflow, including feature engineering and reward function creation, to ensure models meet customer specifications. The platform gives engineers access to reinforcement learning techniques such as GRPO and DAPO, along with capabilities like multi-turn tool training, without requiring them to manage the underlying infrastructure. It also integrates with customers' evaluation solutions to monitor performance and automatically trigger retraining runs, ingesting real-time data to update models as frequently as every hour without an engineer in the loop. Named use cases include domain-specific document extraction, training AI agents to reliably use production tools in multi-step tasks, and generating specialized code such as domain-specific languages, front-end components, and tests.