AI EVALUATIONS + EXPERT DATA

Building the intelligence AI needs.

Alpheva AI finds where your models and agents fail, then turns expert judgment and real-world work into the training data, environments, and evaluations needed to improve them.

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The problem

The better AI gets, the harder it is to know where it fails.

Models are getting remarkably good at benchmarks, reasoning, and tool use. But real-world work is messier. The hardest failures often hide behind plausible outputs: a missed constraint, a flawed decision, a weak recovery, the wrong tool, or a workflow that looks complete but fails the standard that actually matters.

Generic benchmarks tell you whether a model can answer. Real-world evaluation tells you whether it can do the work.

Our solution

From AI failures to measurable improvement.

Alpheva evaluates your models and agents on the real-world work they need to perform, identifies where they fail and why, then turns expert judgment into the data and infrastructure needed to improve them.

We capture the decisions, reasoning, tradeoffs, tool use, corrections, and outcomes behind complex work, creating training data, evaluations, reward signals, and interactive environments that help AI systems perform better where it matters.

Evaluate. Diagnose. Build. Improve.

Every failure becomes a signal for what your AI needs to learn next.

Expertise

Expertise where AI still needs to learn.

From established professions to highly specialized fields, we connect AI systems with the experts behind the work.

Explore 120+ domains

Finance
Medicine
Software
Agronomy
Law
Mining
Engineering
Gemology
Biology
Retail
Archaeology
Energy
Horticulture

What we build

The data and infrastructure to close AI capability gaps.

From evaluation to training to agent performance, we build the expert-powered data, environments, and evaluation systems advanced AI needs to improve.

Supervised Fine-Tuning (SFT)

High-quality prompts, responses, and reasoning traces designed to teach models how to solve complex, professional-grade tasks.

System settings window with calibration gear and rubric adjustment sliders

Reinforcement Learning + Rubrics

Expert-designed tasks, rubrics, and feedback that translate nuanced judgment, tradeoffs, and decision quality into reliable reward signals.

Browser interface showing connected agent environment nodes

AgentEnvironments

Tool-connected environments across APIs, software, and services that recreate realistic workflows for agent training and evaluation.

Evaluation dashboard with benchmark performance bars and verified checkmark

AgentEvaluations

Rigorous evaluations that measure reasoning, planning, tool use, decision quality, error recovery, and successful task completion.

Computer monitor with interactive cursor trajectory for computer-use tasks

Computer-Use Trajectories

Expert-demonstrated browser, desktop, and coding workflows that capture how complex tasks are executed from start to finish.

Proof

What happens when expert intelligence meets real AI systems.

Teaching Kimi K2.6 to Perform Financial Work

How Alpheva turned expert judgment, agent environments, and outcome-based training into specialized model capability.

Read the case study

Aggregate expert score: 67.4% → 81.9%
Agent task completion: 52.4% → 76.9%
Critical-error rate: 8.7% → 3.1%

Custom agents

Specialized agents grounded in expert data.

We build specialized agents around the workflows that matter, ground them in expert data, evaluate them against real-world standards, and continuously improve them with expert feedback.

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Data partnerships