Insights on expert networks, market research, UX research, and AI training from the CleverX team.
108 articles
AI agents fail on whole workflows, not single answers, so they need data that captures the full task. This guide covers agent demonstrations, evals, and why you need domain experts to build them.
Frontier progress now depends on expert judgment, not more data. This guide covers why domain judgment matters, which fields need it, how to verify experts, and how to source them at scale.
Expert data is the human judgment that pushes a model past average competence into professional-grade output. This guide covers what it is, when you need it, and how to buy and vet it.
Financial expert data is the evaluations, rubrics, benchmarks, and demonstrations that verified finance professionals produce so AI models and agents meet the standards a practitioner would apply. This guide covers what that data looks like, who makes it, and how AI teams buy it.
Healthcare expert data is the evaluations, rubrics, benchmarks, and demonstrations that licensed clinicians produce so medical AI is measured against clinical correctness and patient safety. This guide covers what it looks like, who makes it, and how AI teams use it.
RLHF is only as good as the humans giving feedback. This is a practical framework for choosing a provider: the data types, the provider landscape, and the criteria that separate signal from noise.
Legal expert data is the evaluations, rubrics, benchmarks, and demonstrations that practising lawyers produce so legal AI is measured against real legal correctness and jurisdiction fit. This guide covers what it looks like, who makes it, and how AI teams use it.
Model evaluation data is the set of scored judgments that tells you whether a model is actually correct in a field, not just fluent. This guide covers rubrics, benchmarks, expert graders, and how to run an eval.
Expert red-teaming stress-tests an AI system with people qualified to know what real harm looks like. This guide covers who to hire, how to run it, and how verification makes findings defensible.
The AI training data market splits into distinct tiers with very different price tags. This is a taxonomy of provider types, the cost ranges for each, and a framework for matching provider to task.
Appen built its name on crowd scale, but AI teams now need verified expertise. Here are the strongest Appen alternatives in 2026 and where each one fits.
Handshake AI leans on a student and early-career network. Here are the strongest alternatives in 2026 when you need deeper, verified domain expertise.