01
Supervised Fine-Tuning (SFT)
High-quality prompts, responses, and reasoning traces designed to teach models how to solve complex, professional-grade tasks.
Expert data + agent evaluations
Building the intelligence
behind frontier AI.
Alpheva transforms expert judgment and real-world work into the training data,
environments, and evaluations that advance frontier models.
Alpheva Founders Featured In
Applied AI research
Intelligence built from the decisions experts make, not abstractions around their work.
We work with leading experts to capture the reasoning, decisions, and feedback behind complex work, creating the high-quality data and evaluations advanced AI systems need to improve.
The problem
AI is capable. But the hardest work still requires expertise.
Models can reproduce patterns while missing the decisions behind financial analysis, legal reasoning, risk review, and production software.
That knowledge lives in tools, revisions, feedback, and the choices experts make under uncertainty.
Our solution
Expert-built training data for the next generation of AI.
Alpheva brings domain experts into the development loop to create the data and environments advanced AI systems need to improve.
We capture the full process behind complex work: reasoning through incomplete information, weighing tradeoffs, making architectural and strategic decisions, using specialized tools, correcting mistakes, and evaluating outcomes against professional standards.
We turn that expertise into training data, reward signals, evaluations, and interactive environments that teach models not only how to produce better answers, but how to perform the work itself.
What we build
Training data and evaluations for advanced AI.
02
Reinforcement Learning + Rubrics
Expert-designed tasks, rubrics, and feedback that translate nuanced judgment, tradeoffs, and decision quality into reliable reward signals.
03
AgentEnvironments
Tool-connected environments across APIs, software, and services that recreate realistic workflows for agent training and evaluation.
04
AgentEvaluations
Rigorous evaluations that measure reasoning, planning, tool use, decision quality, error recovery, and successful task completion.
05
Computer-Use Trajectories
Expert-demonstrated browser, desktop, and coding workflows that capture how complex tasks are executed from start to finish.
Data partnerships
Got data? Earn up to $5M
Partner with Alpheva to turn real-world expertise, workflows, and knowledge into data that advances frontier AI.
Explore data partnershipsResearch
Research shaping applied intelligence.
Measuring model performance on economically valuable work
What GDPval reveals about evaluating frontier models on realistic professional deliverables across major industries.
A practical framework for evaluating AI agents
How teams can design agent evaluations that measure outcomes, survive non-determinism, and improve alongside the product.
What summarization taught us about learning from human feedback
How preference data, reward modeling, and reinforcement learning helped smaller models produce summaries people preferred.
Built by top minds from:
OpenAICiti
Anthropic
HarvardDeloitte
Microsoft
CFA InstituteMcKinsey
xAI
IUB
Merrill LynchOpenAICiti
Anthropic
HarvardDeloitte
MicrosoftCFA InstituteMcKinsey
xAIIUBMerrill LynchOpenAICiti
Anthropic
HarvardDeloitte
MicrosoftCFA InstituteMcKinsey
xAIIUBMerrill LynchCareers at Alpheva
Help advanced AI learn from real work.
Join a team building rigorous systems for expert knowledge, evaluation, and model behavior.
Explore open roles