Products
Training data, environments, and evaluations for real-world model & agent performance.
Build models that reason through complex tasks, use tools effectively, and recover when workflows break. Our expert-built data, reward signals, and environments support training, while targeted evaluations reveal where further improvement is needed.
Supervised Fine-Tuning (SFT)
Expert-written responses and reasoning traces teach models to solve complex professional tasks, with examples that capture both the answer and the process behind it.
Reinforcement Learning from Human Feedback (RLHF)
Expert comparisons, rankings, and feedback help models learn which responses meet professional standards and why others fall short.
Rubric-Guided Reinforcement Learning
Turn expert judgment into explicit scoring criteria and reward signals that evaluate reasoning, decision quality, and task outcomes.
Reinforcement Learning for Real-World Tool Use
Train models to choose tools, execute multi-step workflows, and recover from failures across APIs, MCP servers, and developer tools, with automated evaluation of decisions and outcomes.
Computer and Browser Use
Expert demonstrations teach models to navigate interfaces, track changing application state, and complete tasks across desktop and browser workflows.
Model Evaluation and Failure Analysis
Test reasoning, planning, tool use, and task completion. Expert review identifies where models fail and which capabilities need further training.
Expert Domain Training Data
Train models on the evidence, standards, and decisions that shape professional work in finance, law, software, and other specialized fields.
Software Engineering Training Data
Teach models to debug repositories, write tests, review changes, and reason through engineering decisions using expert workflows that connect each action to a working result.
Deep Research Training Tasks
Train models to investigate complex questions, compare sources, resolve conflicting evidence, and produce conclusions supported by traceable research.
Multimodal Reasoning Data
Expert tasks teach models to combine evidence from documents, images, audio, video, and structured data to solve problems that span multiple formats.
Custom Model Post-Training
Build a targeted training program around your model’s capability gaps, combining expert data, feedback, reward signals, and environments with evaluations that measure progress.
Ready-to-Use Training Datasets
Start training sooner with validated datasets for specific model capabilities, without waiting for a custom data program.
Work with Alpheva
Close your model’s next capability gap.
Tell us what your model needs to do better. We’ll design the training data, environment, or evaluation around that goal.
Talk with Alpheva AI