OxyrizExpert AI training data
Tasks and reference solutions built by domain specialists.
Training data that reflects the task
Oxyriz develops expert-authored tasks, reference solutions, and review criteria across coding, STEM, and professional reasoning. We start with the behavior a team wants to develop: a correct answer, a well-supported explanation, effective tool use, or completion of a multi-step workflow. That objective determines the examples, environment, and feedback needed.
Supervised examples and human feedback
Expert-written reference solutions can be reviewed and converted into prompt–solution pairs for supervised fine-tuning. Tasks that require judgment can use explicit rubrics covering accuracy, completeness, and traceability. Human review is particularly useful when a single executable test cannot capture the quality of the response. The format and review process are scoped to the project.
Verifiable tasks for reinforcement learning
Coding, STEM, and structured reasoning tasks can include executable checks or numerical correctness criteria. These checks provide the basis for verifiable reward signals. A reference solution helps establish that a task can be solved, while the checks define what counts as success. Reward design should measure the intended outcome rather than incidental formatting or shortcuts.
Agent and browser environments
Our agentic work includes multi-step software tasks in reproducible environments, where an agent inspects code, uses tools, implements changes, and tests its work. Browser-use tasks connect interface behavior to application state. Outcome checks can evaluate workflow completion, error handling, and UI correctness. These environments support training experiments and capability assessment.
Expertise and delivery
Oxyriz brings together a 30-person internal team and a network of more than 1,000 experts, including PhD and master’s degree holders. Read about our expert network. For a new project, share the domain, example task, desired output format, review criteria, and timeline so we can discuss a practical scope.
How is training different from evaluation?
Training examples help develop behavior; evaluation tasks measure capability. Held-out evaluation material should remain separate from the training set to avoid misleading results. See our model evaluation services for reference checks, custom benchmarks, and simulation examples. If you need source material first, explore data sourcing and cleaning.