Mercor describes frontier data, benchmarks, evaluation environments, and large-scale human datasets through a marketplace of experts.[1] Prolific emphasizes high-quality human data from verified participants.[2] Toloka positions around training data for AI agents and LLMs, including coding, agentic skills, and safety.[3]
These products are relevant when access to qualified humans is the bottleneck. Humanbased is broader: contributor supply is only one part of the campaign system. The buyer can define the workflow, mix experts with agents and teams, tune rewards, verify the supply process, preserve attribution for accepted work, and route future compensation through trusted organization terms.[4]
Humanbased also has scale from its Codatta history, with a public profile describing 1M+ data contributors, mostly KYC-verified.[5] That does not mean it is already as reliable as a specialist expert provider in every domain. Reputation and experience systems should be treated as campaign inputs to test and improve, not as a finished guarantee.
Decision
Choose a human intelligence network when the hard part is finding pre-qualified people. Choose Humanbased when the hard part is manufacturing high-quality data with workflow control, a large supply base, qualification gates, incentives, provenance, and reuse economics.
Comparison
| Question | Human intelligence network | Humanbased |
|---|---|---|
| Primary job | Find qualified people, experts, participants, or evaluators. | Run a campaign that turns human, expert, team, and agent work into accepted data assets. |
| Control surface | Talent qualification, matching, task supply, and feedback collection. | Workflow design, pre-labeling agents, reward functions, gates, validation, payment, and reuse terms. |
| Supply model | A vendor network of experts or participants. | A broad global contributor network plus experts, teams, company-led supply, agents, and buyer-owned annotators. |
| Expertise maturity | Usually stronger when the provider has already built a specialist bench for the target task. | Broad scale is available, but domain expertise must be qualified and proven through campaign performance. |
| Provenance | Depends on platform reporting and workflow design. | Supply, review, acceptance, payment, and reuse can stay attached to the accepted work. |
| Long-run economics | Pay for access to people or completed tasks. | Pay for accepted work, then preserve attribution and possible royalty paths when data is reused with trusted organizations. |
Best Fit
Human intelligence networks are useful when the labor market is the main constraint. Humanbased is useful when the company wants a repeatable data production system where experts are part of the factory, not the entire product, and where qualification, provenance, payment, and reuse stay inside the workflow.