Insights on expert networks, market research, UX research, and AI training from the CleverX team.
A practical guide for corporate strategy teams: the market and competitive intelligence methods that produce reliable primary insight, an ethical framework, and how to source verified industry experts, competitor customers, and real buyers.
Original data is what separates thought leadership from opinion. This framework shows enterprise teams how to run a credible, defensible study fast enough to matter, using verified experts.
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.
Niche B2B roles are hard to reach because they are rare, busy, and often bound by compliance rules. This guide compares the channels that actually deliver verified specialists and shows where each one breaks down.
A step-by-step guide to recruiting senior B2B decision-makers, from sourcing and screening to incentives, scheduling, and verifying that they are who they claim.
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.