Products
Training data, environments, and evaluations for advanced AI.
Real-world work rarely follows a clean path. It involves incomplete information, competing constraints, difficult tradeoffs, tool use, and decisions where more than one approach may be valid.
Our products translate that complexity into training data, reward signals, interactive environments, and rigorous evaluations designed to develop and measure advanced model capabilities.
From learning how to approach difficult problems to proving they can execute them reliably, we build for the full path from training to performance.
Supervised fine-tuning (SFT)
High-quality examples, explanations, and process traces that give models a durable foundation for complex professional tasks.
Reinforcement Learning from Human Feedback (RLHF)
Expert comparisons and written feedback that show models the difference between a plausible response and professional-quality work.
Rubric-guided reinforcement learning
Expert-authored criteria and outcome checks that reward sound judgment, penalize shortcuts, and make nuanced quality measurable.
Tool-connected agent environments
Interactive API, MCP, and software environments that let agents plan, act, recover from errors, and verify results in realistic workflows.
Computer and browser use
Demonstrated desktop and browser trajectories that teach agents to operate interfaces, manage state, and complete multi-step work.
Failure analysis and agent evaluation
Targeted evaluations that reveal where reasoning, planning, tool use, and recovery break down before models reach production.
Professional domain data
Training material grounded in the standards, evidence, and decisions that shape work in finance, law, software, and other expert fields.
Code and engineering workflows
Production-oriented code, tests, reviews, debugging sessions, and technical decisions that capture how experienced teams build software.
Deep research tasks
Long-horizon research workflows that require evidence gathering, source comparison, structured synthesis, and defensible conclusions.
Multimodal reasoning data
Expert tasks that connect documents, images, audio, video, and structured information to the decisions they inform.
Custom model improvement programs
A tailored combination of tasks, data, rubrics, and environments built around the exact capability gap your model needs to close.
Ready-to-deploy data
Validated data packages for priority capabilities when a team needs to begin improving a model without waiting for a custom build.
Work with Alpheva
Build models that understand real work.
Tell us where your model struggles. We will help design the data, evaluation, or environment it needs.
Talk with Alpheva AI